A Survey of 2D Face Recognition Techniques
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paper_title: Classification improvement of local feature vectors over the KNN algorithm

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The KNN classification algorithm is particularly suited to be used when classifying images described by local features. In this paper, we propose a novel image classification approach, based on local descriptors and the KNN algorithm. The proposed scheme is based on a hierarchical categorization tree that uses both supervised and unsupervised classification techniques. The unsupervised one is based on a hierarchical lattice vector quantization algorithm, while the supervised one is based on both feature vectors labelling and supervised feature selection method. The proposed tree improves the effectiveness of local feature vector classification and outperforms the exact KNN algorithm in terms of categorization accuracy.
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paper_title: Collusion, MPEG4 compression and frame dropping resistant video watermarking

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We present in this article a new video watermarking which resists collusion, MPEG4 compression and frame dropping attacks. This scheme is based on video mosaicing. For that, we are going to start by describing the mosaicing technique in order to illustrate the contribution of this technique in video watermarking. In fact, mosaicing allows to select an interesting area where the mark should be embedded. The idea is to insert the same mark into the same pixels which represent the same physical point. This is exactly the information which can be provided by a mosaic image at least for the located points in the scene background. Next, we present extensive experimental simulations which prove the watermark imperceptibility and robustness against several video attacks.
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paper_title: A New System for Event Detection from Video Surveillance Sequences

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In this paper, we present an overview of a hybrid approach for event detection from video surveillance sequences that has been developed within the REGIMVid project. This system can be used to index and search the video sequence by the visual content. The platform provides moving object segmentation and tracking, High-level feature extraction and video event detection.We describe the architecture of the system as well as providing an overview of the descriptors supported to date. We then demonstrate the usefulness of the toolbox in the context of feature extraction, events learning and detection in large collection of video surveillance dataset.
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paper_title: Video Watermarking Based on Neural Networks

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In this paper, we propose a novel digital video watermarking scheme based on multi resolution motion estimation and artificial neural network. A multi resolution motion estimation algorithm is adopted to preferentially allocate the watermark to coefficients containing motion. In addition, embedding and extraction of the watermark are based on the relationship between a wavelet coefficient and its neighbor's. A neural network is given to memorize the relationships between coefficients in a 3times3 block of the image. Experimental results show that embedding watermark where picture content is moving is less perceptible. Further, it shows that the proposed scheme is robust against common video processing attacks
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paper_title: Graph-based approach for human action recognition using spatio-temporal features

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Due to the exponential growth of the video data stored and uploaded in the Internet websites especially YouTube, an effective analysis of video actions has become very necessary. In this paper, we tackle the challenging problem of human action recognition in realistic video sequences. The proposed system combines the efficiency of the Bag-of-visual-Words strategy and the power of graphs for structural representation of features. It is built upon the commonly used Space-Time Interest Points (STIP) local features followed by a graph-based video representation which models the spatio-temporal relations among these features. The experiments are realized on two challenging datasets: Hollywood2 and UCF YouTube Action. The experimental results show the effectiveness of the proposed method.
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paper_title: Shearlet Network-based Sparse Coding Augmented by Facial Texture Features for Face Recognition

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One open challenge in face recognition (FR) is the single training sample per subject. This paper addresses this problem through a novel approach that combine Shearlet Networks (SN) and PCA called (SNPCA). Shearlet Network takes advantage of the sparse representation (SR) properties of shearlets in biometric applications, Especially, for face coding and recognition. The main contributions of this paper are (1) the combination of the multi-scale representation which capture geometric information to derive a very efficient representation of facial templates, and the use of a PCA-based approach and (2) the design of a fusion step by a refined model of belief function based on the Dempster-Shafer rule in the context of confusion matrices. This last step is helpful to improve the processing of facial texture features. We compared our algorithm (SNPCA) against SN, a wavelet network (WN) implementation and other standard algorithms. Our tests, run on several face databases including FRGC, Extended Yale B database and others, shows that this approach yields a very competitive performance compared to wavelet networks (WN), standard shearlet and PCA-based methods.
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paper_title: A dynamic video watermarking algorithm in fast motion areas in the wavelet domain

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In this paper, we propose a video watermarking algorithm which embeds different parts of a single watermark into different shots of a video under the wavelet domain. Based on a Motion Activity Analysis, different regions of the original video are separated into perceptually distinct categories according to motion information and region complexity. Thus, the localizations of the watermark are adjusted adaptively in accordance with the human visual system and signal characteristics, which makes them perceptually invisible and less vulnerable to automated removal. In addition, contrary to traditional methods where the watermark remains at a fixed position on the screen, the watermark moves along with moving objects and thus motion artefacts can be avoid. The multi-frame based extraction strategy ensures that the watermark can be correctly recovered from a very short segment of video. Individual frames extracted from the video also contain watermark information. Experimental results show that the inserted watermark is not only less perceptible but also robust against common video processing attacks.
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paper_title: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

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We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.
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paper_title: Generalizing discriminant analysis using the generalized singular value decomposition

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Discriminant analysis has been used for decades to extract features that preserve class separability. It is commonly defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular limits its application to data sets with certain relative dimensions. We examine a number of optimization criteria, and extend their applicability by using the generalized singular value decomposition to circumvent the nonsingularity requirement. The result is a generalization of discriminant analysis that can be applied even when the sample size is smaller than the dimension of the sample data. We use classification results from the reduced representation to compare the effectiveness of this approach with some alternatives, and conclude with a discussion of their relative merits.
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paper_title: Boosting linear discriminant analysis for face recognition

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In this paper, we propose a new algorithm to boost performance of traditional linear discriminant analysis (LDA)-based face recognition (FR) methods in complex FR tasks, where highly nonlinear face pattern distributions are often encountered. The algorithm embodies the principle of "divide and conquer", by which a complex problem, is decomposed into a set of simpler ones, each of which can be conquered by a relatively easy solution. The Ad-aBoost technique is utilized within this framework to: 1) generalize a set of simple FR sub-problems and their corresponding LDA solutions; 2) combine results from the multiple, relatively weak, LDA solutions to form a very strong solution. Experimentation performed on the FERET database indicates that the proposed methodology is able to greatly enhance performance of the traditional LDA-based method with an averaged improvement of correct recognition rate (CRR) up to 9% reported.
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paper_title: View-based and modular eigenspaces for face recognition

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We describe experiments with eigenfaces for recognition and interactive search in a large-scale face database. Accurate visual recognition is demonstrated using a database of O(10/sup 3/) faces. The problem of recognition under general viewing orientation is also examined. A view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose. In addition, a modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer. This modular representation yields higher recognition rates as well as a more robust framework for face recognition. An automatic feature extraction technique using feature eigentemplates is also demonstrated. >
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paper_title: Discriminant local feature analysis of facial images

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In the traditional DKL algorithm, PCA is performed before LDA to achieve stable numerical computation and good generalization in small-sample-size problems. However, PCA is based on the global information, ignoring the significant local characteristics. This paper will propose a novel algorithm called discriminant local feature analysis based on a broader understanding of LFA features. In the algorithm, LFA instead of PCA is applied before LDA. On the one hand, LFA captures local characteristics with little loss of global information. On the other hand, it presents an effective low-dimensional representation of signals, and thus reduces the dimensionality for LDA. By combining LFA and LDA, the DLFA algorithm outperforms DKL, which is showed by experiments on open-set face verification.
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paper_title: Dual-space linear discriminant analysis for face recognition

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Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they are often unstable and have to discard some discriminative information. In this paper, a dual-space LDA approach for face recognition is proposed to take full advantage of the discriminative information in the face space. Based on a probabilistic visual model, the eigenvalue spectrum in the null space of within-class scatter matrix is estimated, and discriminant analysis is simultaneously applied in the principal and null subspaces of the within-class scatter matrix. The two sets of discriminative features are then combined for recognition. It outperforms existing LDA approaches.
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paper_title: Regularized discriminant analysis

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Abstract Linear and quadratic discriminant analysis are considered in the small-sample, high-dimensional setting. Alternatives to the usual maximum likelihood (plug-in) estimates for the covariance matrices are proposed. These alternatives are characterized by two parameters, the values of which are customized to individual situations by jointly minimizing a sample-based estimate of future misclassification risk. Computationally fast implementations are presented, and the efficacy of the approach is examined through simulation studies and application to data. These studies indicate that in many circumstances dramatic gains in classification accuracy can be achieved.
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paper_title: Face recognition using eigenfaces

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An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space ('face space') that best encodes the variation among known face images. The face space is defined by the 'eigenfaces', which are the eigenvectors of the set of faces; they do not necessarily correspond to isolated features such as eyes, ears, and noses. The framework provides the ability to learn to recognize new faces in an unsupervised manner. >
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paper_title: Null space approach of Fisher discriminant analysis for face recognition

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The null space of the within-class scatter matrix is found to express most discriminative information for the small sample size problem (SSSP). The null space-based LDA takes full advantage of the null space while the other methods remove the null space. It proves to be optimal in performance. From the theoretical analysis, we present the NLDA algorithm and the most suitable situation for NLDA. Our method is simpler than all other null space approaches, it saves the computational cost and maintains the performance simultaneously. Furthermore, kernel technique is incorporated into discriminant analysis in the null space. Firstly, all samples are mapped to the kernel space through a better kernel function, called Cosine kernel, which is proposed to increase the discriminating capability of the original polynomial kernel function. Secondly, a truncated NLDA is employed. The novel approach only requires one eigenvalue analysis and is also applicable to the large sample size problem. Experiments are carried out on different face data sets to demonstrate the effectiveness of the proposed methods.
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paper_title: Learning the parts of objects by non-negative matrix factorization

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Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.
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paper_title: Block LDA for Face Recognition

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Linear Discriminant Analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in the traditional LDA some weaknesses. In this paper, we propose a new LDA-based method called Block LDA (BLDA) that can outperform the traditional Linear Dicriminant Analysis (LDA) methods. As opposed to conventional LDA, BLDA is based on 2D matrices rather than 1D vectors. That is, we firstly divides the original image into blocks. Then, we transform the image into a vector of blocks. By using row vector to represent each block, we can get the new matrix which is the representation of the image. Finally LDA can be applied directly on these matrices. In contrast to the between-class and within-class covariance matrices of LDA, the size of the these covariance matrices using BLDA is much smaller. As a result, BLDA has three important advantages over LDA. First, it is easier to evaluate the between-class and within-class covariance matrices accurately. Second, less time is required to determine the corresponding eigenvectors. And finally, block size could be changed to get the best results. Experiment results show our method achieves better performance in comparison with the other methods.
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paper_title: High Performance Human Face Recognition using Gabor based Pseudo Hidden Markov Model

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This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e. a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.
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paper_title: Penalized Discriminant Analysis

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Fisher's linear discriminant analysis (LDA) is a popular data-analytic tool for studying the relationship between a set of predictors and a categorical response. In this paper we describe a penalized version of LDA. It is designed for situations in which there are many highly correlated predictors, such as those obtained by discretizing a function, or the grey-scale values of the pixels in a series of images. In cases such as these it is natural, efficient and sometimes essential to impose a spatial smoothness constraint on the coefficients, both for improved prediction performance and interpretability. We cast the classification problem into a regression framework via optimal scoring. Using this, our proposal facilitates the use of any penalized regression technique in the classification setting. The technique is illustrated with examples in speech recognition and handwritten character recognition.
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paper_title: Discriminative common vectors for face recognition

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In face recognition tasks, the dimension of the sample space is typically larger than the number of the samples in the training set. As a consequence, the within-class scatter matrix is singular and the linear discriminant analysis (LDA) method cannot be applied directly. This problem is known as the "small sample size" problem. In this paper, we propose a new face recognition method called the discriminative common vector method based on a variation of Fisher's linear discriminant analysis for the small sample size case. Two different algorithms are given to extract the discriminative common vectors representing each person in the training set of the face database. One algorithm uses the within-class scatter matrix of the samples in the training set while the other uses the subspace methods and the Gram-Schmidt orthogonalization procedure to obtain the discriminative common vectors. Then, the discriminative common vectors are used for classification of new faces. The proposed method yields an optimal solution for maximizing the modified Fisher's linear discriminant criterion given in the paper. Our test results show that the discriminative common vector method is superior to other methods in terms of recognition accuracy, efficiency, and numerical stability.
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paper_title: Normalized radial basis function networks and bilinear discriminant analysis for face recognition

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In this paper, we present a novel approach for face recognition, using a new subspace method called bilinear discriminant analysis (BDA) and normalized radial basis function networks (NRBFNs). In a first step, BDA extracts the features that enhance separation between classes by using a generalized bilinear projection-based Fisher criterion, computed from image matrices directly. In a second step, the features are fed into a NRBFN that learns class conditional probabilities. This results in an efficient and computationally simple open-world identification process. Experimental results assess the performance and robustness of the proposed algorithm compared to other subspace methods combined with NRBFNs, in the presence of variations in head poses, facial expressions, and partial occlusions.
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paper_title: Gabor features and LDA based face recognition with ANN classifier

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Although many approaches for face recognition have been proposed in the past, none of them can overcome the main problem of lighting, pose and orientation. For a real time face recognition system, these constraints are to be a major challenge which has to be addressed. In this proposed work, a methodology is adopted for improving the robustness of a face recognition system based on two well-known statistical modeling methods to represent a face image: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). These methods extract the discriminant features from the face. Preprocessing of human face image is done using Gabor wavelets which eliminates the variations due to pose, lighting and features to some extent. PCA and LDA extract low dimensional and discriminating feature vectors and these feature vectors were used for classification. The classification stage uses Backpropagation neural network (BPN) as classifier. This proposed system has been successfully tested on ORL face data base with 400 frontal images corresponding to 40 different subjects of variable illumination and facial expressions. The results are compared with standard eigen face method using distance measure as classifier. The system gives a better recognition rate compared to other standard techniques.
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paper_title: Face recognition using enhanced fisher linear discriminant model with facial combined feature

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Achieving higher classification rate under various conditions is a challenging problem in face recognition community. This paper presents a combined feature Fisher classifier (CF2C) approach for face recognition, which is robust to moderate changes of illumination, pose and facial expression. The success of this method lies in that it uses both facial global and local information for robust face representation while at the same time employs an enhanced Fisher linear discriminant model (EFM) for good generalization. Experiments on ORL and Yale face databases show that the proposed approach is superior to traditional methods, such as eigenfaces and fisherfaces.
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paper_title: Two-dimensional PCA: a new approach to appearance-based face representation and recognition

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In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
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paper_title: Thermal face recognition in an operational scenario

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We present results on the latest advances in thermal infrared face recognition, and its use in combination with visible imagery. Previous research by the authors has shown high performance under very controlled conditions, or questionable performance under a wider range of conditions. This paper shows results on the use of thermal infrared and visible imagery for face recognition in operational scenarios. In particular, we show performance statistics for outdoor face recognition and recognition across multiple sessions. Our results support the conclusion that face recognition performance with thermal infrared imagery is stable over multiple sessions, and that fusion of modalities increases performance. As measured by the number of images and number of subjects, this is the largest ever reported study on thermal face recognition.
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paper_title: The Nature of Statistical Learning Theory

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Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
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paper_title: Dynamic facial expression recognition using Laplacian Eigenmaps-based manifold learning

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In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are: (i) a view- and texture independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker; (ii) the complexity of the non-linear facial expression space is modelled through a manifold, whose structure is learned using Laplacian Eigenmaps. The projected facial expressions are afterwards recognized based on Nearest Neighbor classifier; (iii) with the proposed approach, we developed an application for an AIBO robot, in which it mirrors the perceived facial expression.
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paper_title: Kernel independent component analysis

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We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical properties as measures of statistical dependence. On the other hand, building on recent developments in kernel methods, we show that these criteria can be computed efficiently. Minimizing these criteria leads to flexible and robust algorithms for ICA. We illustrate with simulations involving a wide variety of source distributions, showing that our algorithms outperform many of the presently known algorithms.
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paper_title: Distance Preserving Dimension Reduction for Manifold Learning

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A set of paving elements for the production of paving and a method of using the same are disclosed. In a preferred form, the set comprises four cylindrical paving elements of equal height and of different diameter having a diametric proportion to each other of 1:1.4:1.8:2.2. Two or more the of paving elements from each of plurality of sets may be laid to form a paving strip with the paving elements laid next to each other in mutual contact.
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paper_title: Face recognition by support vector machines

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Support vector machines (SVM) have been recently proposed as a new technique for pattern recognition. SVM with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVM on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. We also present the recognition experiment on a larger face database of 1079 images of 137 individuals. We compare the SVM-based recognition with the standard eigenface approach using the nearest center classification (NCC) criterion.
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paper_title: Face recognition based on discriminative manifold learning

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In this paper, a discriminative manifold learning method for face recognition is proposed which achieved the discriminative embedding the high dimensional face data into a low dimensional hidden manifold. Unlike the recently proposed LLE, Isomap and Eigenmap algorithms, which are based on reconstruction purpose, our method uses the RCA algorithm to achieve nonlinear embedding and data discrimination at the same time. Also, the LLE and Isomap algorithms are crucially depends on the appropriateness of the neighborhood construction rule, in this paper, a CK-nearest neighborhood rule is proposed to achieve better neighborhood construction. Experimental results indicate the promising performance of the proposed method.
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paper_title: Combining local and global information for nonlinear dimensionality reduction

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Nonlinear dimensionality reduction is a challenging problem encountered in a variety of high dimensional data analysis, including machine learning, pattern recognition, scientific visualization, and neural computation. Based on the different geometric intuitions of manifolds, maximum variance unfolding (MVU) and Laplacian eigenmaps are designed for detecting the different aspects of dataset. In this paper, combining the ideas of MVU and Laplacian eigenmaps, we propose a new nonlinear dimensionality reduction method called distinguishing variance embedding (DVE). DVE unfolds the dataset by maximizing the global variance subject to the proximity relation preservation constraint originated in Laplacian eigenmaps. We illustrate the algorithm on easily visualized examples of curves and surfaces, as well as on the actual images of rotating objects, faces, and handwritten digits.
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paper_title: Unsupervised Learning of Image Manifolds by Semidefinite Programming

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Can we detect low dimensional structure in high dimensional data sets of images? In this paper, we propose an algorithm for unsupervised learning of image manifolds by semidefinite programming. Given a data set of images, our algorithm computes a low dimensional representation of each image with the property that distances between nearby images are preserved. More generally, it can be used to analyze high dimensional data that lies on or near a low dimensional manifold. We illustrate the algorithm on easily visualized examples of curves and surfaces, as well as on actual images of faces, handwritten digits, and solid objects.
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paper_title: Face recognition using non-linear image reconstruction

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We present a face recognition technique based on a special type of convolutional neural network that is trained to extract characteristic features from face images and reconstruct the corresponding reference face images which are chosen beforehand for each individual to recognize. The reconstruction is realized by a so-called "bottle-neck" neural network that learns to project face images into a low-dimensional vector space and to reconstruct the respective reference images from the projected vectors. In contrast to methods based on the Principal Component Analysis (PCA), the Linear Discriminant Analysis (LDA) etc., the projection is non-linear and depends on the choice of the reference images. Moreover, local and global processing are closely interconnected and the respective parameters are conjointly learnt. Having trained the neural network, new face images can then be classified by comparing the respective projected vectors. We experimentally show that the choice of the reference images influences the final recognition performance and that this method outperforms linear projection methods in terms of precision and robustness.
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paper_title: Nearest manifold approach for face recognition

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Faces under varying illumination, pose and non-rigid deformation are empirically thought of as a highly nonlinear manifold in the observation space. How to discover intrinsic low-dimensional manifold is important to characterize meaningful face distributions and classify them using a simpler, such as linear or Gaussian based, classifier. In this paper, we present a manifold learning algorithm (MLA) for learning a mapping from highly-dimensional manifold into the intrinsic low-dimensional linear manifold. We also propose the nearest manifold (NM) criterion for the classification and present an algorithm for computing the distance from the sample to be classified to the nearest face manifolds in light of local linearity of manifold. Based on these works, face recognition is achieved with the combination of MLA and NM. Experiments on several face databases show that the advantages of our proposed combinational approach.
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paper_title: Generalized Discriminant Analysis: A Matrix Exponential Approach

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Linear discriminant analysis (LDA) is well known as a powerful tool for discriminant analysis. In the case of a small training data set, however, it cannot directly be applied to high-dimensional data. This case is the so-called small-sample-size or undersampled problem. In this paper, we propose an exponential discriminant analysis (EDA) technique to overcome the undersampled problem. The advantages of EDA are that, compared with principal component analysis (PCA) + LDA, the EDA method can extract the most discriminant information that was contained in the null space of a within-class scatter matrix, and compared with another LDA extension, i.e., null-space LDA (NLDA), the discriminant information that was contained in the non-null space of the within-class scatter matrix is not discarded. Furthermore, EDA is equivalent to transforming original data into a new space by distance diffusion mapping, and then, LDA is applied in such a new space. As a result of diffusion mapping, the margin between different classes is enlarged, which is helpful in improving classification accuracy. Comparisons of experimental results on different data sets are given with respect to existing LDA extensions, including PCA + LDA, LDA via generalized singular value decomposition, regularized LDA, NLDA, and LDA via QR decomposition, which demonstrate the effectiveness of the proposed EDA method.
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paper_title: Validation of low-dimensional models using diffusion maps and harmonic averaging

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This paper is concerned with efficient methods of dynamic model validation under the conditions when the underlying dynamics is high-dimensional, the data is high- dimensional, or when significant noise is present. In such cases there is a need to separate the geometric properties of the data from the noise. The approach we propose is based on a mapping of data into a low-dimensional subspace which requires no a priori knowledge of their qualitative behavior or dimension. Model comparison is then based on the intrinsic geometry of data sets and their temporal characteristics. We construct a diffusion map on the graph of the experimental data set as established in the work of Coifman, Lafon, et al. We then use the Nystrom extension to map the model data set into the frame of the experimental data, thereby allowing the two to be easily compared. We apply this to both face image data and to experiments and candidate models of a combustion experiment exhibiting limit-cycling oscillations.
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paper_title: Discriminant Analysis on Embedded Manifold

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Previous manifold learning algorithms mainly focus on uncovering the low dimensional geometry structure from a set of samples that lie on or nearly on a manifold in an unsupervised manner. However, the representations from unsupervised learning are not always optimal in discriminating capability. In this paper, a novel algorithm is introduced to conduct discriminant analysis in term of the embedded manifold structure. We propose a novel clustering algorithm, called Intra-Cluster Balanced K-Means (ICBKM), which ensures that there are balanced samples for the classes in a cluster; and the local discriminative features for all clusters are simultaneously calculated by following the global Fisher criterion. Compared to the traditional linear/kernel discriminant analysis algorithms, ours has the following characteristics: 1) it is approximately a locally linear yet globally nonlinear discriminant analyzer; 2) it can be considered a special Kernel-DA with geometry-adaptive-kernel, in contrast to traditional KDA whose kernel is independent to the samples; and 3) its computation and memory cost are reduced a great deal compared to traditional KDA, especially for the cases with large number of samples. It does not need to store the original samples for computing the low dimensional representation for new data. The evaluation on toy problem shows that it is effective in deriving discriminative representations for the problem with nonlinear classification hyperplane. When applied to the face recognition problem, it is shown that, compared with LDA and traditional KDA on YALE and PIE databases, the proposed algorithm significantly outperforms LDA and
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paper_title: Learning a locality preserving subspace for visual recognition

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We have demonstrated that the face recognition performance can be improved significantly in low dimensional linear subspaces. Conventionally, principal component analysis (PCA) and linear discriminant analysis (LDA) are considered effective in deriving such a face subspace. However, both of them effectively see only the Euclidean structure of face space. We propose a new approach to mapping face images into a subspace obtained by locality preserving projections (LPP) for face analysis. We call this Laplacianface approach. Different from PCA and LDA, LPP finds an embedding that preserves local information, and obtains a face space that best detects the essential manifold structure. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. We compare the proposed Laplacianface approach with eigenface and fisherface methods on three test datasets. Experimental results show that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
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paper_title: Kernel PCA for novelty detection

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Kernel principal component analysis (kernel PCA) is a non-linear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinite-dimensional feature space. In this space, kernel PCA extracts the principal components of the data distribution. The squared distance to the corresponding principal subspace is the measure for novelty. This new method demonstrated a competitive performance on two-dimensional synthetic distributions and on two real-world data sets: handwritten digits and breast-cancer cytology.
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paper_title: Two-dimensional PCA: a new approach to appearance-based face representation and recognition

paper_content:

In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
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paper_title: Face Recognition : A Convolutional Neural Network Approach

paper_content:

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.
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paper_title: Neural network-based face detection

paper_content:

We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.
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paper_title: A feature based approach to face recognition

paper_content:

A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented. The feature extraction model is biologically motivated, and the locations of the features often correspond to salient facial features such as the eyes, nose, etc. Topological graphs are used to represent relations between features, and a simple deterministic graph-matching scheme that exploits the basic structure is used to recognize familiar faces from a database. Each of the stages in the system can be fully implemented in parallel to achieve real-time recognition. Experimental results for a 128*128 image with very little noise are evaluated. >
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paper_title: Face Authentication with Gabor Information On Deformable Graphs

paper_content:

Elastic graph matching has been proposed as a practical implementation of dynamic link matching, which is a neural network with dynamically evolving links between a reference model and an input image. Each node of the graph contains features that characterize the neighborhood of its location in the image. The elastic graph matching usually consists of two consecutive steps, namely a matching with a rigid grid, followed by a deformation of the grid, which is actually the elastic part. The deformation step is introduced in order to allow for some deformation, rotation, and scaling of the object to be matched. This method is applied here to the authentication of human faces where candidates claim an identity that is to be checked. The matching error as originally suggested is not powerful enough to provide satisfying results in this case. We introduce an automatic weighting of the nodes according to their significance. We also explore the significance of the elastic deformation for an application of face-based person authentication. We compare performance results obtained with and without the second matching step. Results show that the deformation step slightly increases the performance, but has lower influence than the weighting of the nodes. The best results are obtained with the combination of both aspects. The results provided by the proposed method compare favorably with two methods that require a prior geometric face normalization, namely the synergetic and eigenface approaches.
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paper_title: Robust visual similarity retrieval in single model face databases

paper_content:

In this paper, we introduce a novel visual similarity measuring technique to retrieve face images in photo album databases for law enforcement. Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching schemes suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be considered. This paper presents a robust face retrieval approach using structural and spatial point correspondence in which the directional corner points (DCPs) are generated for efficient face coding and retrieval. A complete investigation on the proposed method is conducted, which covers face retrieval under controlled/ideal condition, scale variations, environmental changes and subject actions. The system performance is compared with the performance of the eigenface method. It is an attractive finding that the proposed DCP retrieval technique has performed superior to the eigenface method in most of the comparison experiments. This research demonstrates that the proposed DCP approach provides a new way, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.
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paper_title: Face recognition: features versus templates

paper_content:

Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching, are presented. The results obtained for the testing sets show about 90% correct recognition using geometrical features and perfect recognition using template matching. >
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paper_title: Distortion Invariant Object Recognition in the Dynamic Link Architecture

paper_content:

An object recognition system based on the dynamic link architecture, an extension to classical artificial neural networks (ANNs), is presented. The dynamic link architecture exploits correlations in the fine-scale temporal structure of cellular signals to group neurons dynamically into higher-order entities. These entities represent a rich structure and can code for high-level objects. To demonstrate the capabilities of the dynamic link architecture, a program was implemented that can recognize human faces and other objects from video images. Memorized objects are represented by sparse graphs, whose vertices are labeled by a multiresolution description in terms of a local power spectrum, and whose edges are labeled by geometrical distance vectors. Object recognition can be formulated as elastic graph matching, which is performed here by stochastic optimization of a matching cost function. The implementation on a transputer network achieved recognition of human faces and office objects from gray-level camera images. The performance of the program is evaluated by a statistical analysis of recognition results from a portrait gallery comprising images of 87 persons. >
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paper_title: Face detection using improved LBP under Bayesian framework

paper_content:

In this paper, we present a novel face detection approach using improved local binary patterns (ILBP) as facial representation. ILBP feature is an improvement of LBP feature that considers both local shape and texture information instead of raw grayscale information and it is robust to illumination variation. We model the face and non-face class using multivariable Gaussian model and classify them under Bayesian framework. Extensive experiments show that the proposed method has an encouraging performance.
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paper_title: Distinctive Image Features from Scale-Invariant Keypoints

paper_content:

The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.
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paper_title: WLD: A Robust Local Image Descriptor

paper_content:

Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. Specifically, WLD consists of two components: differential excitation and orientation. The differential excitation component is a function of the ratio between two terms: One is the relative intensity differences of a current pixel against its neighbors, the other is the intensity of the current pixel. The orientation component is the gradient orientation of the current pixel. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT). In addition, experimental results on human face detection also show a promising performance comparable to the best known results on the MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.
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paper_title: Descriptor Based Methods in the Wild

paper_content:

Recent methods for learning similarity between images have presented impressive results in the problem of pair matching (same/notsame classification) of face images. In this paper we explore how well this performance carries over to the related task of multi-option face identification, specifically on the Labeled Faces in the Wild (LFW) image set. In addition, we seek to compare the performance of similarity learning methods to descriptor based methods. We present the following results: (1) Descriptor-Based approaches that efficiently encode the appearance of each face image as a vector outperform the leading similarity based method in the task of multi-option face identification. (2) Straightforward use of Euclidean distance on the descriptor vectors performs somewhat worse than the similarity learning methods on the task of pair matching. (3) Adding a learning stage, the performance of descriptor based methods matches and exceeds that of similarity methods on the pair matching task. (4) A novel patch based descriptor we propose is able to improve the performance of the successful Local Binary Pattern (LBP) descriptor in both multi-option identification and same/not-same classification.
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paper_title: Enhanced local texture feature sets for face recognition under difficult lighting conditions

paper_content:

Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. We address this by combining the strengths of robust illumination normalization, local texture based face representations and distance transform based matching metrics. Specifically, we make three main contributions: (i) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; (ii) we introduce Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions; and (iii) we show that replacing local histogramming with a local distance transform based similarity metric further improves the performance of LBP/LTP based face recognition. The resulting method gives state-of-the-art performance on three popular datasets chosen to test recognition under difficult illumination conditions: Face Recognition Grand Challenge version 1 experiment 4, Extended Yale-B, and CMU PIE.
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paper_title: Blur insensitive texture classification using local phase quantization

paper_content:

In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred.
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paper_title: Face recognition with local binary patterns

paper_content:

In this work, we present a novel approach to face recognition which considers both shape and texture information to represent face images. The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face image. The recognition is performed using a nearest neighbour classifier in the computed feature space with Chi square as a dissimilarity measure. Extensive experiments clearly show the superiority of the proposed scheme over all considered methods (PCA, Bayesian Intra/extrapersonal Classifier and Elastic Bunch Graph Matching) on FERET tests which include testing the robustness of the method against different facial expressions, lighting and aging of the subjects. In addition to its efficiency, the simplicity of the proposed method allows for very fast feature extraction.
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paper_title: Face recognition: features versus templates

paper_content:

Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching, are presented. The results obtained for the testing sets show about 90% correct recognition using geometrical features and perfect recognition using template matching. >
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paper_title: Face detection and recognition using hidden Markov models

paper_content:

The work presented in this paper describes a hidden Markov model (HMM)-based framework for face recognition and face detection. The observation vectors used to characterize the states of the HMM are obtained using the coefficients of the Karhunen-Loeve transform (KLT). The face recognition method presented reduces significantly the computational complexity of previous HMM-based face recognition systems, while slightly improving the recognition rate. Consistent with the HMM model of the face, this paper introduces a novel HMM-based face detection approach using the same feature extraction techniques used for face recognition.
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paper_title: Viewpoint Unconstrained Face Recognition Based on Affine Local Descriptors and Probabilistic Similarity

paper_content:

Face recognition under controlled settings, such as limited viewpoint and illumination change, can achieve good performance nowadays. However, real world application for face recognition is still challenging. In this paper, we propose using the combination of Affine Scale Invariant Feature Transform (SIFT) and Probabilistic Similarity for face recognition under a large viewpoint change. Affine SIFT is an extension of SIFT algorithm to detect affine invariant local descriptors. Affine SIFT generates a series of different viewpoints using affine transformation. In this way, it allows for a viewpoint difference between the gallery face and probe face. However, the human face is not planar as it contains significant 3D depth. Affine SIFT does not work well for significant change in pose. To complement this, we combined it with probabilistic similarity, which gets the log likelihood between the probe and gallery face based on sum of squared difference (SSD) distribution in an offline learning process. Our experiment results show that our framework achieves impressive better recognition accuracy than other algorithms compared on the FERET database.
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paper_title: An Efficient Hybrid Face Recognition Algorithm Using PCA and GABOR Wavelets

paper_content:

With the rapid development of computers and the increasing, mass use of high-tech mobile devices, vision-based face recognition has advanced significantly. However, it is hard to conclude that the performance of computers surpasses that of humans, as humans have generally exhibited better performance in challenging situations involving occlusion or variations. Motivated by the recognition method of humans who utilize both holistic and local features, we present a computationally efficient hybrid face recognition method that employs dual-stage holistic and local feature-based recognition algorithms. In the first coarse recognition stage, the proposed algorithm utilizes Principal Component Analysis (PCA) to identify a test image. The recognition ends at this stage if the confidence level of the result turns out to be reliable. Otherwise, the algorithm uses this result for filtering out top candidate images with a high degree of similarity, and passes them to the next fine recognition stage where Gabor filte...
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paper_title: Face Recognition Based on Wavelet Transform and Regional Directional Weighted Local Binary Pattern

paper_content:

With the development of information technology, face recognition technology has been continuously developed. This technology has attracted the attention of many researchers, including institutions and production enterprises. Face recognition technology has become a relatively independent application technology area in various social services. This paper presents a face recognition algorithm based on wavelet transform and regional directional weighted local binary pattern. First of all, this algorithm puts forward a new basis for a face recognition, namely the level of detailed components of face images containing valid facial texture details, and the recognition rate is better than that of the vertical component information and diagonal component information. This is called Horizontal Component Prior Principle(HCPP). According to HCPP, the original image is decomposed with wavelet transformation. The algorithm extracts the scale and level of detailed components. To improve the original LBP operator, it presents the regional directional weighted local binary pattern (RDW-LBP). Using the RDW-LBP, it can calculate the histogram of scale components and detailed components decomposed by wavelet. The histogram feature vector of face image can be got with the different weighted sub-regions. The feature vector can be matched with Chi-Square distance. This approach further enhances the ability to extract face direction information effectively
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paper_title: Face Recognition via Local Directional Pattern

paper_content:

In this paper, we propose an illumination-robust face recognition system via local directional pattern images. Usually, local pattern descriptors including local binary pattern and local directional pattern have been used in the field of the face recognition and facial expression recognition, since local pattern descriptors have important properties to be robust against the illumination changes and computational simplicity. Thus, this paper represents the face recognition approach that employs the local directional pattern descriptor and twodimensional principal analysis algorithms to achieve enhanced recognition accuracy. In particular, we propose a novel methodology that utilizes the transformed image obtained from local directional pattern descriptor as the direct input image of two-dimensional principal analysis algorithms, unlike that most of previous works employed the local pattern descriptors to acquire the histogram features. The performance evaluation of proposed system was performed using well-known approaches such as principal component analysis and Gabor-wavelets based on local binary pattern, and publicly available databases including the Yale B database and the CMU-PIE database were employed. Through experimental results, the proposed system showed the best recognition accuracy compared to different approaches, and we confirmed the effectiveness of the proposed method under varying lighting conditions.
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paper_title: An Efficient Face Recognition with ANN using Hybrid Feature Extraction Methods

paper_content:

a day's face recognition is the most interesting and active research area in the field of phycology, neuroscience, and computer vision. In this paper a fast efficient algorithm is developed with the better recognition rate of face in different conditions that is illumination, head pose, expressions etc. In which we have extract the global and local features using PCA (Principle Component Analysis) and LBP (Local Binary Pattern) respectively. we have experiment the proposed algorithm with the standard Yale database which contains 15 individuals with each contains 11 images(15*11). So the fusion of Global and Local features are fed to the MLP (Multilayer Perceptron). The BPMLP (Backpropagation Multilayer Perceptron) is used for the classification. The proposed method achieves 93% accuracy compare to the existing approach.
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paper_title: Discriminative Gaussian process latent variable model for classification

paper_content:

Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may be possible if the data lie on a low-dimensional manifold. Gaussian Process Latent Variable Models can discover low dimensional manifolds given only a small number of examples, but learn a latent space without regard for class labels. Existing methods for discriminative manifold learning (e.g., LDA, GDA) do constrain the class distribution in the latent space, but are generally deterministic and may not generalize well with limited training data. We introduce a method for Gaussian Process Classification using latent variable models trained with discriminative priors over the latent space, which can learn a discriminative latent space from a small training set.
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paper_title: Face Recognition Using HMM-LBP

paper_content:

Despite the existence of many biometric systems such as hand geometry, iris scan, retinal scanning and fingerprints, the face recognition will remain a powerful tool due to many advantages such as his low cost, the absence of physical contact between user and biometric system, and his user acceptance. Thus, a large number of face recognition approaches has been lately done. In this paper, we present a new 2D face recognition approach called HMM-LBP permitting the classification of a 2D face image by using the LBP tool (Local Binary Pattern) for feature extraction. It is composed of four steps. First, we decompose our face image into blocs. Then, we extract image features using LBP. Next, we calculate probabilities. Finally, we select the maximum probability. The obtained results were presented to prove the efficiency and performance of the novel technique.
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paper_title: Comparison of wavelet, Gabor and curvelet transform for face recognition

paper_content:

There has been much research about using Gabor wavelet for face recognition. Other multiscale geometrical tools, such as curvelet and contourlet, have also been used for face recognition, thus it is interesting to know which method performs best, especially under illumination and expression changes. In this paper, we make a systematic comparison of wavelet, Gabor and curvelet for recognition, and find the best subband irrelevant to expression and illumination changes. We combine the multiscale analysis with subspace decomposition as our algorithm. Experiments show that for expression changes, the properties of the coarse layer of curvelet and wavelet are very good. Whilst for illumination changes, the low frequency parts of the two methods are similarly influenced, but the detail coefficients of curvelet and the high frequency of wavelet work fine with PCA, with the former outperforming the latter. When these two factors change simultaneously, the detail layer of curvelet is better relative to the others.
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paper_title: A Novel Hybrid Method for Face Recognition Based on 2d Wavelet and Singular Value Decomposition

paper_content:

An efficient face recognition system using eigen values of wavelet transform as feature vectors and radial basis function (RBF) neural network as classifier is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet transformation.The wavelet coefficients obtained from the wavelet transformation are averaged for finding centers of features. In train process, four output of wavelet transform is analyzed and all eigenvalues of these images is obtained. At next step, the maximum 10 eigenvalues of wavelet sub images is stored as feature. Based on four sub images of wavelet transform and 10 eigenvalues of each sub image, the length of feature vector is 40. After obtaining features, in the train process for each person a center that has minimum Euclidean distance from all features is selected using RBF function. In fact the features are recognized by a RBF network. For a new input face image, firstly the feature vector is computed and then the distance (error) of this new vector with all centers of all persons is checked. The minimum distance is selected as target face. The proposed method on Essex face database and resultsshowed that the proposed method provide better recognition rates with low computational complexity.
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paper_title: High Performance Human Face Recognition using Gabor based Pseudo Hidden Markov Model

paper_content:

This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e. a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.
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paper_title: Face Recognition System Using PCA and DCT in HMM

paper_content:

The speed of procedures became necessities during recent years; therefore using computers turn out to be the most important factors to increase the speed of implementation especially in security aspect such as recognition of people.There are a lot of waysto recognize the people face recognition is one of them. In this work the details of the face have been taken as blocks and Discrete Cosine Transform (DCT) is used, applied on face image’s blocks. Then without doing inverse DCT Principal Component Analysis (PCA) is applied directly for dimensionality reduction this makes the system very fast. Olivetti Research Laboratory (ORL) database of faces had been used to obtain the results.Each face is considered as a numerical sequence (blocks) that can be easily modelled by HMM. On 400 face images of the (ORL) face database the system has been examined. The experiments showed a recognition rate of 95.211%, using half of the images for training.
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paper_title: Hybrid Feature Extraction Method for Partial Face Recognition

paper_content:

Face Recognition is one of the challenging tasks faced till date in the field of computer vision and pattern recognition. A robust face recognition system is essential for proper and accurate face recognition. Facial feature extraction is an important stage of face recognition process as it includes the important information required for face recognition. A good feature extraction algorithm helps in extraction of relevant information helpful in accurate face recognition. This paper discusses the SIFT (Scale Invariant Feature Transform) algorithm which is one of the successful algorithms for local feature extraction and 2DPCA (Two Dimensional Principal Component Analysis) which is an improved version of PCA (Principal Component Analysis), PCA being a holistic in nature .Here we propose the use of SIFT and 2DPCA for facial feature extraction method.
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paper_title: Distortion Invariant Object Recognition in the Dynamic Link Architecture

paper_content:

An object recognition system based on the dynamic link architecture, an extension to classical artificial neural networks (ANNs), is presented. The dynamic link architecture exploits correlations in the fine-scale temporal structure of cellular signals to group neurons dynamically into higher-order entities. These entities represent a rich structure and can code for high-level objects. To demonstrate the capabilities of the dynamic link architecture, a program was implemented that can recognize human faces and other objects from video images. Memorized objects are represented by sparse graphs, whose vertices are labeled by a multiresolution description in terms of a local power spectrum, and whose edges are labeled by geometrical distance vectors. Object recognition can be formulated as elastic graph matching, which is performed here by stochastic optimization of a matching cost function. The implementation on a transputer network achieved recognition of human faces and office objects from gray-level camera images. The performance of the program is evaluated by a statistical analysis of recognition results from a portrait gallery comprising images of 87 persons. >
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paper_title: Research of 2DPCA principal component uncertainty in face recognition

paper_content:

This paper studied 2DPCA ( two-dimensional principal component analysis ) principal component feature vectors for face recognition, it found that different principal component had the different role, and then it studied 2DPCA principal component uncertainty, it is good to distinguish 2DPCA principal component role. Using uncertainty knowledge to calculate the weights of 2DPCA principal components, and the last weights was found by optimization of weights, the principal components were processed by using weighted in the process of recognition. ORL face images were used to test the algorithm, the results indicate that the algorithm has good recognition performance.
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paper_title: Face recognition using independent component analysis and support vector machines

paper_content:

Support vector machines (SVM) and independent component analysis (ICA) are two powerful and relatively recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. ICA is a feature extraction technique which can be considered a generalization of principal component analysis (PCA). ICA has been mainly used on the problem of blind signal separation. In this paper we combine these two techniques for the face recognition problem. Experiments were made on two different face databases, achieving very high recognition rates. As the results using the combination PCA/SVM were not very far from those obtained with ICA/SVM, our experiments suggest that SVMs are relatively insensitive to the representation space. Thus as the training time for ICA is much larger than that of PCA, this result indicates that the best practical combination is PCA with SVM.
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paper_title: Performance improvement for face recognition using PCA and two-dimensional PCA

paper_content:

Now a days research is going on to design a high performance automatic face recognition system which is really a challenging task for researchers. As faces are complex visual stimuli that differ dramatically, hence developing an efficient computational approach for accurate face recognition is very difficult. In this paper a high performance face recognition algorithm is developed and tested using conventional Principal Component Analysis (PCA) and two dimensional Principal Component Analysis (2DPCA). These statistical transforms are exploited for feature extraction and data reduction. We have proposed here to assign different weight to the only very few nonzero eigenvalues related eigenvectors which are considered as non-trivial principal components for classification. Lastly face recognition task is performed by k-nearest distance measurement. Experimental results on ORL and YALE face databases show that the proposed method improves the performance of face recognition with respect to existing techniques. The results show that better recognition performance can be achieved with less computational cost than that of other existing methods.
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paper_title: Face Recognition by Independent Component Analysis

paper_content:

A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to these high-order statistics. Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons. ICA was performed on face images in the FERET database under two different architectures, one which treated the images as random variables and the pixels as outcomes, and a second which treated the pixels as random variables and the images as outcomes. The first architecture found spatially local basis images for the faces. The second architecture produced a factorial face code. Both ICA representations were superior to representations based on PCA for recognizing faces across days and changes in expression. A classifier that combined the two ICA representations gave the best performance.
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paper_title: Parameterisation of a stochastic model for human face identification

paper_content:

Recent work on face identification using continuous density Hidden Markov Models (HMMs) has shown that stochastic modelling can be used successfully to encode feature information. When frontal images of faces are sampled using top-bottom scanning, there is a natural order in which the features appear and this can be conveniently modelled using a top-bottom HMM. However, a top-bottom HMM is characterised by different parameters, the choice of which has so far been based on subjective intuition. This paper presents a set of experimental results in which various HMM parameterisations are analysed. >
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paper_title: Face recognition using a DCT-HMM approach

paper_content:

A transform domain approach coupled with Hidden Markov Model (HMM) for face recognition is presented. JPEG kind of strategy is employed to transform input subimage for training HMMs. DCT transformed vectors efface images are used to train ergodic HMM and later for recognition. ORL face database of 40 subjects with 10 images per subject is used to evaluate the performance of the proposed method. 5 images per subject are used for training and the rest 5 for recognition. This method has an accuracy of 99.5%. The results, to the best of knowledge of the authors, give the best recognition percentage as compared to any other method reported so far on ORL face database.
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paper_title: Complete Architecture of a Robust System of Face Recognition

paper_content:

human face recognition is a hard topic because of the multitude of parameters involved (e.g. variation in pose, illumination, facial expression, partial occlusions), it is very important to be interested and to invest in it viewed her many fields of application (identity authentication, physical and logical access control, video surveillance, human-machine interface...). The work presented in this paper is in this context. Its objective is the implementation of a complete architecture of a robust face recognition system. In a first time, a new approach has been developed for the detection of faces in a 2D color image. Secondly, is focused on the feature extraction using an original approach which includes the Gabor descriptor and a pose estimator. Finally, to validate this research, the developed system is tested on standard databases: Caltech_Web, AT&T and Color FERET. Keywordsrecognition, face detection, Gabor wavelets, pose estimator, Supports Vectors Machines.
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paper_title: A NEW FAST AND EFFICIENT HMM-BASED FACE RECOGNITION SYSTEM USING A 7-STATE HMM ALONG WITH SVD COEFFICIENTS

paper_content:

In this paper, a new Hidden Markov Model (HMM)-based face recognition system is proposed. As a novel point despite of five-state HMM used in pervious researches, we used 7-state HMM to cover more details. Indeed we add two new face regions, eyebrows and chin, to the model. As another novel point, we used a small number of quantized Singular Values Decomposition (SVD) coefficients as features describing blocks of face images. This makes the system very fast. The system has been evaluated on the Olivetti Research Laboratory (ORL) face database. In order to additional reduction in computational complexity and memory consumption the images are resized to 64×64 jpeg format. Before anything, an order-statistic filter is used as a preprocessing operation. Then a top-down sequence of overlapping sub-image blocks is considered. Using quantized SVD coefficients of these blocks, each face is considered as a numerical sequence that can be easily modeled by HMM. The system has been examined on 400 face images of the Olivetti Research Laboratory (ORL) face database. The experiments showed a recognition rate of 99%, using half of the images for training. The system has been evaluated on 64×64 jpeg resized YALE database too. This database contains 165 face images with 231×195 pgm format. Using five training image, we obtained 97.78% recognition rate where for six training images the recognition rate was 100%, a record in the literature. The proposed method is compared with the best researches in the literature. The results show that the proposed method is the fastest one, having approximately 100% recognition rate.
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paper_title: A taxonomy of 2D and 3D face recognition methods

paper_content:

This paper presents an evaluation of the state-of-the-art face recognition using 2D, 3D and multimodal imagery methods. The twofold motivations are: (i) to provide the insight studies of automatic face recognition of 2D and 3D face imagery methods and (ii) to critically review the exiting 2D and 3D face recognition methods. Research trends to date are summarized, and challenges confronting the development of more accurate face recognition systems are identified. These challenges include the face recognition under unconstrained environment, such as variation in illumination, pose, facial expression, occlusion, and time delay.
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paper_title: A face recognition system based on local feature analysis

paper_content:

A completely automatic face recognition system is presented. The method works on color and gray level images: after having localized the face and the facial features, it determines 16 facial fiducial points, and characterizes them applying a bank of filters which extract the peculiar texture around them (jets). Recognition is realized measuring the similarity between the different jets. The system is inspired by the elastic bunch graph method, but the fiducial point localization does not require any manual setting.
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paper_title: Face recognition using eigenfaces

paper_content:

An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space ('face space') that best encodes the variation among known face images. The face space is defined by the 'eigenfaces', which are the eigenvectors of the set of faces; they do not necessarily correspond to isolated features such as eyes, ears, and noses. The framework provides the ability to learn to recognize new faces in an unsupervised manner. >
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paper_title: Face recognition using line edge map

paper_content:

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.
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paper_title: An improved 2DPCA algorithm for face recognition

paper_content:

On the basis of two-dimensional principal component analysis, an improved two-dimensional principal component analysis (I2DPCA) is presented for face recognition. Firstly, the criterion functions of global and between-class scatters of projection features are defined. Secondly, the two defined criterion functions are fused by way of multiplication or addition. Therefore, the criterion function of I2DPCA is produced, and the optimal projection axis vector of I2DPCA algorithm is the vector which maximizes its criterion function. Experimental results show that, the correct recognition rate of I2DPCA is higher than that of 2DPCA. In I2DPCA algorithm, the correct recognition rate fused by way of addition is higher than that by way of multiplication.
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paper_title: Face Recognition Using HMM-LBP

paper_content:

Despite the existence of many biometric systems such as hand geometry, iris scan, retinal scanning and fingerprints, the face recognition will remain a powerful tool due to many advantages such as his low cost, the absence of physical contact between user and biometric system, and his user acceptance. Thus, a large number of face recognition approaches has been lately done. In this paper, we present a new 2D face recognition approach called HMM-LBP permitting the classification of a 2D face image by using the LBP tool (Local Binary Pattern) for feature extraction. It is composed of four steps. First, we decompose our face image into blocs. Then, we extract image features using LBP. Next, we calculate probabilities. Finally, we select the maximum probability. The obtained results were presented to prove the efficiency and performance of the novel technique.
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paper_title: Independent Component Analysis of Gabor Features for Face Recognition

paper_content:

We present an independent Gabor features (IGFs) method and its application to face recognition. The novelty of the IGF method comes from 1) the derivation of independent Gabor features in the feature extraction stage and 2) the development of an IGF features-based probabilistic reasoning model (PRM) classification method in the pattern recognition stage. In particular, the IGF method first derives a Gabor feature vector from a set of downsampled Gabor wavelet representations of face images, then reduces the dimensionality of the vector by means of principal component analysis, and finally defines the independent Gabor features based on the independent component analysis (ICA). The independence property of these Gabor features facilitates the application of the PRM method for classification. The rationale behind integrating the Gabor wavelets and the ICA is twofold. On the one hand, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can, thus, produce salient local features that are most suitable for face recognition. On the other hand, ICA would further reduce redundancy and represent independent features explicitly. These independent features are most useful for subsequent pattern discrimination and associative recall. Experiments on face recognition using the FacE REcognition Technology (FERET) and the ORL datasets, where the images vary in illumination, expression, pose, and scale, show the feasibility of the IGF method. In particular, the IGF method achieves 98.5% correct face recognition accuracy when using 180 features for the FERET dataset, and 100% accuracy for the ORL dataset using 88 features.
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paper_title: Face recognition based on Gabor with 2DPCA and PCA

paper_content:

In this paper, a new method combined Two-Dimensional Principal Component Analysis (2DPCA) with Principal Component Analysis (PCA) is proposed to extract Gabor features for face recognition. Gabor wavelet has been widely used in the face recognition task because it's good imitation of human visual. However, the huge redundancy of Gabor features limits its application. When processing an image use a group Gabor nuclear with five scale and eight directions, the date obtained is enormous. The traditional Two-Dimensional Principal Component Analysis (2DPCA) can limit relativity between Columns, but the number of features is still large, which affects the speed of classification. To resolve this problem, the author uses a method based on Gabor wavelet matrix applied to 2DPCA features matrix and Principal Component Analysis (PCA) for the feature extraction in this paper. The experiment results showed that the performance is superior to single 2DPCA or Gabor with 2DPCA.
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paper_title: Using hidden Markov models and wavelets for face recognition

paper_content:

In this paper, a new system for face recognition is proposed, based on hidden Markov models (HMM) and wavelet coding. A sequence of overlapping sub-images is extracted from each face image, computing the wavelet coefficients for each of them. The whole sequence is then modelled by using hidden Markov models. The proposed method is compared with a DCT coefficient-based approach (Kohir et al. (1998)), showing comparable results. By using an accurate model selection procedure, we show that results proposed in Kohir can be improved even more. The obtained results outperform all results presented in the literature on the Olivetti Research Laboratory (ORL) face database, reaching a 100% recognition rate. This performance proves the suitability of HMM to deal with the new JPEG2000 image compression standard.
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paper_title: A Review of Maximum Confidence Hidden Markov Models in Face Recognition

paper_content:

The work presented in this paper focuses on the use of Hidden markov models for face recognition. New discriminative training creation to assure model compactness and discriminability.hidden markov model(HMM) is statistical model in which the system being modeled is assumed to be markov processes with unoabserd state. Hmm can be considered as a simplest dynamics Bayesian network. In Hidden Marko model, the state is not directly visible but output dependent on state is visible. Accordingly w develop the maximum confidence hidden markov modeling (MC-HMM) for face recognition. In MC-HMM we merge transformation matrix to extract discriminative facial features. MC-HMM achieves higher recognition with lower feature dimensions.
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paper_title: Face Recognition System Using PCA and DCT in HMM

paper_content:

The speed of procedures became necessities during recent years; therefore using computers turn out to be the most important factors to increase the speed of implementation especially in security aspect such as recognition of people.There are a lot of waysto recognize the people face recognition is one of them. In this work the details of the face have been taken as blocks and Discrete Cosine Transform (DCT) is used, applied on face image’s blocks. Then without doing inverse DCT Principal Component Analysis (PCA) is applied directly for dimensionality reduction this makes the system very fast. Olivetti Research Laboratory (ORL) database of faces had been used to obtain the results.Each face is considered as a numerical sequence (blocks) that can be easily modelled by HMM. On 400 face images of the (ORL) face database the system has been examined. The experiments showed a recognition rate of 95.211%, using half of the images for training.
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paper_title: Face Recognition by Elastic Bunch Graph Matching

paper_content:

We present a system for recognizing human faces from single images out of a large database with one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial points on the face (eyes, mouth etc.) are described by sets of wavelet components (jets). Image graph extraction is based on a novel approach, the bunch graph, which is constructed from a small set of sample image graphs. Recognition is based on a straight-forward comparison of image graphs. We report recognition experiments on the FERET database and the Bochum database, including recognition across pose.
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paper_title: Face Recognition Based on Wavelet Transform and Improved 2DPCA

paper_content:

Two-dimensional principal component analysis (2DPCA) is a kind of image feature extraction method. The algorithm based on image matrix as the analysis object, before the image feature extraction, it can deal with image data directly and does not need dimension reduction. It process image data without step of vectorization. Although 2DPCA algorithm reduces the computational complexity, it takes up more storage space. In this paper, based on wavelet transform and improved 2DPCA, an approach of face recognition was proposed, Using the improved 2DPCA algorithm can effectively recognize faces. This improved method can eliminate the correlation of image rows and columns at the same time. And it could overcome the drawback mentioned above. The method is the higher efficient recognition rate than 2DPCA algorithm.
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paper_title: Two-dimensional PCA: a new approach to appearance-based face representation and recognition

paper_content:

In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
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paper_title: Face Recognition Based on SVM and 2DPCA

paper_content:

The paper will present a novel approach for solving face recognition problem. Our method combines 2D Principal Component Analysis (2DPCA), one of the prominent methods for extracting feature vectors, and Support Vector Machine (SVM), the most powerful discriminative method for classification. Experiments based on proposed method have been conducted on two public data sets FERET and ATT the results show that the proposed method could improve the classification rates.
---
paper_title: Face recognition using eigenfaces

paper_content:

An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space ('face space') that best encodes the variation among known face images. The face space is defined by the 'eigenfaces', which are the eigenvectors of the set of faces; they do not necessarily correspond to isolated features such as eyes, ears, and noses. The framework provides the ability to learn to recognize new faces in an unsupervised manner. >
---
paper_title: Face recognition using line edge map

paper_content:

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.
---
paper_title: An improved 2DPCA algorithm for face recognition

paper_content:

On the basis of two-dimensional principal component analysis, an improved two-dimensional principal component analysis (I2DPCA) is presented for face recognition. Firstly, the criterion functions of global and between-class scatters of projection features are defined. Secondly, the two defined criterion functions are fused by way of multiplication or addition. Therefore, the criterion function of I2DPCA is produced, and the optimal projection axis vector of I2DPCA algorithm is the vector which maximizes its criterion function. Experimental results show that, the correct recognition rate of I2DPCA is higher than that of 2DPCA. In I2DPCA algorithm, the correct recognition rate fused by way of addition is higher than that by way of multiplication.
---
paper_title: Face Recognition Using HMM-LBP

paper_content:

Despite the existence of many biometric systems such as hand geometry, iris scan, retinal scanning and fingerprints, the face recognition will remain a powerful tool due to many advantages such as his low cost, the absence of physical contact between user and biometric system, and his user acceptance. Thus, a large number of face recognition approaches has been lately done. In this paper, we present a new 2D face recognition approach called HMM-LBP permitting the classification of a 2D face image by using the LBP tool (Local Binary Pattern) for feature extraction. It is composed of four steps. First, we decompose our face image into blocs. Then, we extract image features using LBP. Next, we calculate probabilities. Finally, we select the maximum probability. The obtained results were presented to prove the efficiency and performance of the novel technique.
---
paper_title: Class-Specific Subspace-Based Two-Dimensional Principal Component Analysis for Face Recognition

paper_content:

In this paper, we proposed a class-specific subspacebased Two-Dimensional Principal Component Analysis (2DPCA) for face recognition. In 2DPCA, 2D face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, 2DPCA can achieve higher performance than PCA both in face recognition and face representation task. However, both PCA and 2DPCA are unsupervised techniques, no information of class labels are considered. Therefore, the directions that maximize the scatter of the data might not be as adequate to discriminate between classes. In recognition task, a projection is required to emphasize the discrimination between classes. The Face-Specific Subspace (FSS) was proposed in concept of class-specific subspace. Each subspaces learned from the training images which correspond to only one class, thus the number of these subspaces is equal to the number of classes. Since the information of class labels are considered in FSS, so the discriminant power can be improved. We apply 2DPCA to class-specific concept in our framework which consists of two methods: the first one, we apply FSS to 2DPCA method and the second one, we use the Bilateral-projection-based 2DPCA (B2DPCA) instead of 2DPCA. The B2DPCA does not only allows further reducing of the dimension of feature matrix of 2DPCA-based but also improving the classi fi cation accuracy. Experimental results on Yale face database showed an improvement of our proposed techniques over the conventional 2DPCA.
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paper_title: High Performance Human Face Recognition using Gabor based Pseudo Hidden Markov Model

paper_content:

This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e. a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.
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paper_title: Face recognition/detection by probabilistic decision-based neural network

paper_content:

This paper proposes a face recognition system, based on probabilistic decision-based neural networks (PDBNN). With technological advance on microelectronic and vision system, high performance automatic techniques on biometric recognition are now becoming economically feasible. Among all the biometric identification methods, face recognition has attracted much attention in recent years because it has potential to be most nonintrusive and user-friendly. The PDBNN face recognition system consists of three modules: First, a face detector finds the location of a human face in an image. Then an eye localizer determines the positions of both eyes in order to generate meaningful feature vectors. The facial region proposed contains eyebrows, eyes, and nose, but excluding mouth (eye-glasses will be allowed). Lastly, the third module is a face recognizer. The PDBNN can be effectively applied to all the three modules. It adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. The paper demonstrates a successful application of PDBNN to face recognition applications on two public (FERET and ORL) and one in-house (SCR) databases. Regarding the performance, experimental results on three different databases such as recognition accuracies as well as false rejection and false acceptance rates are elaborated. As to the processing speed, the whole recognition process (including PDBNN processing for eye localization, feature extraction, and classification) consumes approximately one second on Sparc10, without using hardware accelerator or co-processor.
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paper_title: Two-dimensional PCA: a new approach to appearance-based face representation and recognition

paper_content:

In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
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paper_title: A taxonomy of 2D and 3D face recognition methods

paper_content:

This paper presents an evaluation of the state-of-the-art face recognition using 2D, 3D and multimodal imagery methods. The twofold motivations are: (i) to provide the insight studies of automatic face recognition of 2D and 3D face imagery methods and (ii) to critically review the exiting 2D and 3D face recognition methods. Research trends to date are summarized, and challenges confronting the development of more accurate face recognition systems are identified. These challenges include the face recognition under unconstrained environment, such as variation in illumination, pose, facial expression, occlusion, and time delay.
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paper_title: Face recognition using independent component analysis and support vector machines

paper_content:

Support vector machines (SVM) and independent component analysis (ICA) are two powerful and relatively recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. ICA is a feature extraction technique which can be considered a generalization of principal component analysis (PCA). ICA has been mainly used on the problem of blind signal separation. In this paper we combine these two techniques for the face recognition problem. Experiments were made on two different face databases, achieving very high recognition rates. As the results using the combination PCA/SVM were not very far from those obtained with ICA/SVM, our experiments suggest that SVMs are relatively insensitive to the representation space. Thus as the training time for ICA is much larger than that of PCA, this result indicates that the best practical combination is PCA with SVM.
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paper_title: Performance improvement for face recognition using PCA and two-dimensional PCA

paper_content:

Now a days research is going on to design a high performance automatic face recognition system which is really a challenging task for researchers. As faces are complex visual stimuli that differ dramatically, hence developing an efficient computational approach for accurate face recognition is very difficult. In this paper a high performance face recognition algorithm is developed and tested using conventional Principal Component Analysis (PCA) and two dimensional Principal Component Analysis (2DPCA). These statistical transforms are exploited for feature extraction and data reduction. We have proposed here to assign different weight to the only very few nonzero eigenvalues related eigenvectors which are considered as non-trivial principal components for classification. Lastly face recognition task is performed by k-nearest distance measurement. Experimental results on ORL and YALE face databases show that the proposed method improves the performance of face recognition with respect to existing techniques. The results show that better recognition performance can be achieved with less computational cost than that of other existing methods.
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paper_title: Face Recognition by Independent Component Analysis

paper_content:

A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to these high-order statistics. Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons. ICA was performed on face images in the FERET database under two different architectures, one which treated the images as random variables and the pixels as outcomes, and a second which treated the pixels as random variables and the images as outcomes. The first architecture found spatially local basis images for the faces. The second architecture produced a factorial face code. Both ICA representations were superior to representations based on PCA for recognizing faces across days and changes in expression. A classifier that combined the two ICA representations gave the best performance.
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paper_title: A PARAMETER-BASED COMBINED CLASSIFIER FOR INVARIANT FACE RECOGNITION

paper_content:

A system for invariant face recognition is presented. A combined classifier uses the generalization capabilities of learning vector quantization (LVQ) neural networks to build a representative model of a face from a variety of training patterns with different poses, details, and facial expressions. The combined generalization error of the classifier is found to be lower than that of each individual classifier. The system is tested on an in-house built database and is capable of recognizing a face in about 1 second. The system performance compares favorably with the state-of-the-art systems. While the recognition rates of the individual classifiers ranged from 94% to 96%, a correct recognition rate of 100% is achieved by the combined classifier at 0% rejection.
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paper_title: High Performance Human Face Recognition using Gabor based Pseudo Hidden Markov Model

paper_content:

This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e. a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.
---
paper_title: Two-dimensional PCA: a new approach to appearance-based face representation and recognition

paper_content:

In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
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paper_title: A human face recognition method by improved Modular 2DPCA

paper_content:

The Modular-2DPCA is an improvement and promotion of 2DPCA. Modular-2DPCA method creates the covariance matrix by blocked sub image, which make better robustness. There are many parts in a human face, with each part could own different weight in face recognition, block made the research of parts became possible. This paper directs the characteristic of block, calculating the mean value and covariance matrix for each sub image block, with which can extract the features of each part of human face more accurate. Theoretically, this method can efficacious reduce the effect of changed facial. Otherwise, this paper contains a preliminary research of sub image weights setting. Weights of parts can further raise contribution of some special parts of human face. Appropriate weights can improve the result of recognition. Experiments show that this method can efficacious improves the insignificancy of Modular 2DPCA in features extracting and raise the correct result of recognition.
---
paper_title: High Performance Human Face Recognition using Gabor based Pseudo Hidden Markov Model

paper_content:

This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e. a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.
---
paper_title: 3D face texture modeling from uncalibrated frontal and profile images

paper_content:

3D face modeling from 2D face images is of significant importance for face analysis, animation and recognition. Previous research on this topic mainly focused on 3D face modeling from a single 2D face image; however, a single face image can only provide a limited description of a 3D face. In many applications, for example, law enforcement, multi-view face images are usually captured for a subject during enrollment, which makes it desirable to build a 3D face texture model, given a pair of frontal and profile face images. We first determine the correspondence between un-calibrated frontal and profile face images through facial landmark alignment. An initial 3D face shape is then reconstructed from the frontal face image, followed by shape refinement utilizing the depth information provided by the profile image. Finally, face texture is extracted by mapping the frontal face image on the recovered 3D face shape. The proposed method is utilized for 2D face recognition in two scenarios: (i) normalization of probe image, and (ii) enhancing the representation capability of gallery set. Experimental results comparing the proposed method with a state-of-the-art commercial face matcher and densely sampled LBP on a subset of the FERET database show the effectiveness of the proposed 3D face texture model.
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paper_title: Face Recognition Based on SVM and 2DPCA

paper_content:

The paper will present a novel approach for solving face recognition problem. Our method combines 2D Principal Component Analysis (2DPCA), one of the prominent methods for extracting feature vectors, and Support Vector Machine (SVM), the most powerful discriminative method for classification. Experiments based on proposed method have been conducted on two public data sets FERET and ATT the results show that the proposed method could improve the classification rates.
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paper_title: Fast and efficient 3D face recognition using wavelet networks

paper_content:

3D shape of face has recently emerged as a major research in face biometrics. However, while it is reputed to be relatively invariant to lighting conditions and pose, one still needs to cope with facial expression variations for a reliable face recognition solution and running time of the matching algorithms for fast identification software. We present in this paper our solutions to overcome these limitations. We propose a new method of 3D facial recognition based on wavelet networks. Firstly, depth image is preprocessed in order to crop the useful area of the face image. Secondly, a compact and representative biometric signature is produced by means of wavelet networks. Finally, the matching of two faces is made by computing Euclidean distance between their two corresponding signatures. To show the efficiency and accuracy of our approach, a subset taken from FRGC v2 dataset is used to made evaluations.
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paper_title: Face recognition using multimodal biometric features

paper_content:

This paper presents a new multimodal biometric approach using face and periocular biometric. The available face recognition algorithm performance in presence of multiple variations such as illumination, pose, expression, occlusion and plastic surgery is not satisfactory. Also, periocular biometrics face problems in presence of spectacles, head angle, hair and expression. A method which can extract multiple feature information from a single source and can give a satisfactory performance even with less number of training images is desirable. Thus combining face and periocular data obtained from the same image may increase the performance of the recognition system. A detailed performance analysis of face recognition and periocular biometric using Gabor and LBP features is carried out. This is then compared with the proposed multimodal biometric feature extraction technique. The experimental results obtained using Muct and plastic surgery face database shows that the proposed multimodal biometric performs better than other face recognition and individual biometric methods.
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paper_title: Multi Library Wavelet Neural Networks for 3D face recognition using 3D facial shape representation

paper_content:

This paper presents a new approach for 3D face modeling and recognition. Motivated by finding a representation that embodies a high power of discrimination between face classes, a new type of 3D shape descriptors is suggested. We have developed a fully automatic system which uses an alignment algorithm to register 3D facial scans. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact wavelet metadata. Our system consists in two phases. The first phase is called enrolment composed of 3 steps: data processing, alignment and metadata generating. The metadata generating step is powered by the use of Multi Library Wavelet Neural Networks (MLWNN). The second phase is called Authentication it starts with the calculation of depth distances between a probe and gallery 3D face. A K-Nearest Neighbors (K-NN) technique is used for 3D face classification. The results of this contribution are more interesting, in comparison with some others works, in term of recognition rate using the GavabDB 3D facial database.
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paper_title: Identifying Individuals Using Multimodal Face Recognition Techniques

paper_content:

Abstract A biometric system which primarily based on the cues of unimodal biometric for individual identiﬁcation is not always meet the desired results. The concept of multimodal biometrics for human Identiﬁcation is an emerging trend. In this paper, we present state-of-the-art novel multimodal biometric system, for face recognition, which combines the similarity scores of the unimodal modalities such as appearance based and texture based techniques of face recognition, to cater the decisive results at the level of matching score. Formally, it includes the fusion of unimodal techniques to devise the multimodal models in four possible combinations such as (a) Eigenfaces and local binary pattern (LBP), (b) Fisherfaces and LBP, (c) organics’ and augmented local binary pattern (A-LBP), and (d) Fisherfaces and A-LBP. The performance of the multimodal face recognition systems is tested on the publicly available face databases such as the AT & T-ORL and the Labeled Faces in the Wild (LFW) using a new Bray Curtis dissimilarity metric. The experimental results show a signiﬁcant improvement in the performance of recognition accuracies of multimodal face recognition techniques.
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paper_title: Illumination Invariant Face Recognition Using Near-Infrared Images

paper_content:

Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR image-based face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups
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paper_title: A robust method for near infrared face recognition based on extended local binary pattern

paper_content:

Face recognition is one of the most successful applications in biometric authentication. However, methods reported in the literature still suffer from some problems which prevent the further development in face recognition. This paper presents a novel robust method for face recognition under near infrared (NIR) lighting condition based on Extended Local Binary Pattern (ELBP), which solves the problems produced by variations of illumination rightly, since the NIR images are insensitive to variations of ambient lighting, and ELBP can extract adequate texture features form the NIR images. By combining the local feature vectors, a global feature vector is formed and as the global feature vectors extracted by ELBP operator often have very high dimensions, a classifier has been trained using the AdaBoost algorithm to select the most representative features for better performance and dimensionality reduction. Compared with the huge number of features produced by ELBP operator, only a small part of the features are selected in this paper, which saves much computation and time cost. The comparison with the results of classic algorithms proves the effectiveness of the proposed method.
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paper_title: 3D Face Analysis: Advances and Perspectives

paper_content:

In the past decade, research on 3D face analysis has been extensively developed, and this study briefly reviews the progress achieved in data acquisition, algorithms, and experimental methodologies, for the issues of face recognition, facial expression recognition, gender and ethnicity classification, age estimation, etc., especially focusing on that after the availability of FRGC v2.0. It further points out several challenges to deal with for more efficient and reliable systems in the real world.
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paper_title: 3-D Face Recognition Using eLBP-Based Facial Description and Local Feature Hybrid Matching

paper_content:

This paper presents an effective method for 3-D face recognition using a novel geometric facial representation along with a local feature hybrid matching scheme. The proposed facial surface description is based on a set of facial depth maps extracted by multiscale extended Local Binary Patterns (eLBP) and enables an efficient and accurate description of local shape changes; it thus enhances the distinctiveness of smooth and similar facial range images generated by preprocessing steps. The following matching strategy is SIFT-based and performs in a hybrid way that combines local and holistic analysis, robustly associating the keypoints between two facial representations of the same subject. As a result, the proposed approach proves robust to facial expression variations, partial occlusions, and moderate pose changes, and the last property makes our system registration-free for nearly frontal face models. The proposed method was experimented on three public datasets, i.e. FRGC v2.0, Gavab, and Bosphorus. It displays a rank-one recognition rate of 97.6% and a verification rate of 98.4% at a 0.001 FAR on the FRGC v2.0 database without any face alignment. Additional experiments on the Bosphorus dataset further highlight the advantages of the proposed method with regard to expression changes and external partial occlusions. The last experiment carried out on the Gavab database demonstrates that the entire system can also deal with faces under large pose variations and even partially occluded ones, when only aided by a coarse alignment process.
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paper_title: Deep learning and face recognition: the state of the art

paper_content:

Deep Neural Networks (DNNs) have established themselves as a dominant technique in machine learning. DNNs have been top performers on a wide variety of tasks including image classification, speech recognition, and face recognition. 1-3 Convolutional neural networks (CNNs) have been used in nearly all of the top performing methods on the Labeled Faces in the Wild (LFW) dataset. 3-6 In this talk and accompanying paper, I attempt to provide a review and summary of the deep learning techniques used in the state-of-the-art. In addition, I highlight the need for both larger and more challenging public datasets to benchmark these systems. Despite the ability of DNNs and autoencoders to perform unsupervised feature learning, modern facial recognition pipelines still require domain specific engineering in the form of re-alignment. For example, in Facebook's recent DeepFace paper, a 3D "frontalization" step lies at the beginning of the pipeline. This step creates a 3D face model for the incoming image and then uses a series of affine transformations of the fiducial points to "frontalize" the image. This step enables the DeepFace system to use a neural network architecture with locally connected layers without weight sharing as opposed to standard convolutional layers. 6 Deep learning techniques combined with large datasets have allowed research groups to surpass human level performance on the LFW dataset. 3, 5 The high accuracy (99.63% for FaceNet at the time of publishing) and utilization of outside data (hundreds of millions of images in the case of Google's FaceNet) suggest that current face verification benchmarks such as LFW may not be challenging enough, nor provide enough data, for current techniques. 3, 5 There exist a variety of organizations with mobile photo sharing applications that would be capable of releasing a very large scale and highly diverse dataset of facial images captured on mobile devices. Such an "ImageNet for Face Recognition" would likely receive a warm welcome from researchers and practitioners alike.
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paper_title: Wavelet Network and Geometric Features Fusion Using Belief Functions for 3D Face Recognition

paper_content:

One of the challenges in pattern recognition technologies, especially face recognition, is the ability to handle scenarios where subjects are non-cooperative in terms of position (face pose) or deformation (face expression). In this paper, we propose an innovative approach for 3D face recognition that combines heterogeneous features by using evidence theory based on belief functions. The first feature is generated via wavelet network algorithm, which approximates every face, by an optimal linear combination. The second feature models each facial surface by a collection of facial curves based on geodesic distance. The fusion procedure adopt a refined model of belief function based on the Deampster-Shafer rule in the context of confusion matrix. Experimental evaluation performed on subset of the FRGC v2 database, shows that the recognition rate increases with fusion of redundant and/or independent data. Further, the technique demonstrates robustness under different facial expressions.
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paper_title: Multimodal Deep Learning

paper_content:

Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. Our models are validated on the CUAVE and AVLetters datasets on audio-visual speech classification, demonstrating best published visual speech classification on AVLetters and effective shared representation learning.
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paper_title: Robust Facial Expression Recognition Using Near Infrared Cameras

paper_content:

In human-human communication we use verbal, vocal and non-verbal signals to communicate with others. Facial expressions are a form of nonverbal communication, recognizing them helps to improve the human-machine interaction. This paper proposes a system for poseand illumination-invariant recognition of facial expressions using near-infrared camera images and precise 3D shape registration. Precise 3D shape information of the human face can be computed by means of constrained local models (CLM), which fits a dense model to an unseen image in an iterative manner. We used a multi-class SVM to classify the acquired 3D shape into different emotion categories. Results surpassed human performance and show pose-invariant performance. Varying lighting conditions can influence the fitting process and reduce the recognition precision. We built a near-infrared and visible light camera array to test the method with different illuminations. Results shows that the near-infrared camera configuration is suitable for robust and reliable facial expression recognition with changing lighting conditions.
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paper_title: 3D Face Recognition under Expressions, Occlusions, and Pose Variations

paper_content:

We propose a novel geometric framework for analyzing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analyzing shapes of full facial surfaces. This representation, along with the elastic Riemannian metric, seems natural for measuring facial deformations and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and so on. This framework is shown to be promising from both-empirical and theoretical-perspectives. In terms of the empirical evaluation, our results match or improve upon the state-of-the-art methods on three prominent databases: FRGCv2, GavabDB, and Bosphorus, each posing a different type of challenge. From a theoretical perspective, this framework allows for formal statistical inferences, such as the estimation of missing facial parts using PCA on tangent spaces and computing average shapes.
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paper_title: A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition

paper_content:

This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of more accurate three-dimensional face recognition are identified. These challenges include the need for better sensors, improved recognition algorithms, and more rigorous experimental methodology.
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paper_title: Seeing People in the Dark: Face Recognition in Infrared Images

paper_content:

An IR image of the human face presents its unique heat-signature and can be used for recognition. The characteristics of IR images maintain advantages over visible light images, and can be used to improve algorithms of human face recognition in several aspects. IR images are obviously invariant under extreme lighting conditions (including complete darkness). The main findings of this research are that IR face images are less effected by changes of pose or facial expression and enable a simple method for detection of facial features. In this paper we explore several aspects of face recognition in IR images. First, we compare the effect of varying environment conditions over IR and visible light images through a case study. Finally, we propose a method for automatic face recognition in IR images, through which we use a preprocessing algorithm for detecting facial elements, and show the applicability of commonly used face recognition methods in the visible light domain.
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Title: A Survey of 2D Face Recognition Techniques
Section 1: Introduction
Description 1: Introduce the concept of biometrics with a focus on face recognition, discussing its advantages, challenges, and applications.

Section 2: 2D Face Recognition Survey
Description 2: Provide a classification of face recognition techniques into global, local, and hybrid approaches, and explain the principle behind each category.

Section 3: Global Approaches
Description 3: Discuss global (holistic) approaches that use the entire face as input data, explaining both linear and non-linear techniques.

Section 4: Linear Techniques
Description 4: Detail specific linear techniques such as Eigenface, 2D PCA, Independent Component Analysis (ICA), and others, highlighting their methodologies and key characteristics.

Section 5: Non-Linear Techniques
Description 5: Explain non-linear techniques that utilize kernel functions, including Kernel PCA, Support Vector Machines (SVM), and other methods that address the limitations of linear approaches.

Section 6: Local Approaches
Description 6: Describe local approaches that focus on specific facial features or regions, including interest-point-based and local appearance-based face recognition methods.

Section 7: Interest-Point-Based Face Recognition Methods
Description 7: Explore methods that start with detecting specific geometric features, followed by the classification of these features.

Section 8: Local Appearance-Based Face Recognition Methods
Description 8: Discuss methods that divide the face into regions and directly extract local features from these areas.

Section 9: Hybrid Approaches and Methods Based on Statistical Models
Description 9: Present hybrid approaches that combine global and local characteristics, as well as methods based on statistical models like Hidden Markov Models (HMM).

Section 10: 2D Face Databases
Description 10: Review various face databases used in research, their characteristics, and relevance to different testing scenarios.

Section 11: Results
Description 11: Summarize the performance results of different face recognition approaches tested on various databases.

Section 12: The Emergence of New Promising Research Directions
Description 12: Highlight emerging research trends such as 3D face recognition, multimodal approaches, deep learning techniques, and infrared imagery.

Section 13: Conclusions
Description 13: Conclude by summarizing the findings of the survey, emphasizing the progress made in face recognition techniques and future research directions.