A Review on Biometric Recognition
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paper_title: A feature-based approach to automatic injection mold generation

paper_content:

This paper presents a feature-based approach to the automatic generation of an injection mold. In the approach, all the molding features of a molded part are recognized first using a universal hint-based feature recognition algorithm, and the optimal parting direction is determined based on the feature model. Then, all the faces related with the parting line are classified into three groups according to the determined parting direction, and two kinds of parting lines are automatically determined by extracting the largest profile of each face group. Finally, the parting surface is generated based on the optimal pacing line, thereafter the mold core and mold cavity are automatically set up by splitting the mold box of the molded part with the generated parting surface. By using recognized molding features, the approach makes it possible to identify all undercuts, to determine internal parting lines and internal parting surfaces, to generate the mold core and cavity of a complex molded part with undercuts as well.
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paper_title: Evaluation of multiclass support vector machine classifiers using optimum threshold-based pruning technique

paper_content:

Support vector machine (SVM) is the state-of-the-art classifier used in real world pattern recognition applications. One of the design objectives of SVM classifiers using non-linear kernels is reducing the number of support vectors without compromising the classification accuracy. To meet this objective, decision-tree approach and pruning techniques are proposed in the literature. In this study, optimum threshold (OT)-based pruning technique is applied to different decision-tree-based SVM classifiers and their performances are compared. In order to assess the performance, SVM-based isolated digit recognition system is implemented. The performances are evaluated by conducting various experiments using speaker-dependent and multispeaker-dependent TI46 database of isolated digits. Based on this study, it is found that the application of OT technique reduces the minimum time required for recognition by a factor of 1.54 and 1.31, respectively, for speaker-dependent and multispeaker-dependent cases. The proposed approach is also applicable for other SVM-based multiclass pattern recognition systems such as target recognition, fingerprint classification, character recognition and face recognition.
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paper_title: Local Feature Analysis: A general statistical theory for object representation

paper_content:

Low-dimensional representations of sensory signals are key to solving many of the computational problems encountered in high-level vision. Principal component analysis (PCA) has been used in the past to derive practically useful compact representations for different classes of objects. One major objection to the applicability of PCA is that it invariably leads to global, non-topographic representations that are not amenable to further processing and are not biologically plausible. In this paper we present a new mathematical construction, local feature analysis (LFA), for deriving local topographic representations for any class of objects. The LFA representations are sparse-distributed and, hence, are effectively low-dimensional and retain all the advantages of the compact representations of the PCA. But, unlike the global eigenmodes, they give a description of objects in terms of statistically derived local features and their positions. We illustrate the theory by using it to extract local features for th...
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paper_title: Generalized Discriminant Analysis Using a Kernel Approach

paper_content:

We present a new method that we call generalized discriminant analysis (GDA) to deal with nonlinear discriminant analysis using kernel function operator. The underlying theory is close to the support vector machines (SVM) insofar as the GDA method provides a mapping of the input vectors into high-dimensional feature space. In the transformed space, linear properties make it easy to extend and generalize the classical linear discriminant analysis (LDA) to nonlinear discriminant analysis. The formulation is expressed as an eigenvalue problem resolution. Using a different kernel, one can cover a wide class of nonlinearities. For both simulated data and alternate kernels, we give classification results, as well as the shape of the decision function. The results are confirmed using real data to perform seed classification.
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paper_title: 67 Biometrics Verification: a Literature Survey

paper_content:

Biometric verification refers to an automatic verification of a person based on some specific biometric features derived from his/her physiological and/or behavioral characteristics. A biometric verification system has more capability to reliably distinguish between an authorized person and an imposter than the traditional systems that use a card or a password. In biometrics, a person could be recognized based on who he/she is rather than what he/she has (ID card) or what he/she knows (password). Currently, biometrics finds use in ATMs, computers, security installations, mobile phones, credit cards, health and social services. The future in biometrics seems to belong to the multimodal biometrics (a biometric system using more than one biometric feature) as a unimodal biometric system (biometric system using single biometric feature) has to contend with a number of problems. In this paper, a survey of some of the unimodal biometrics will be presented that are either currently in use across a range of environments or those still in limited use or under development, or still in the research realm.
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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: Eigenfaces for Recognition

paper_content:

We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
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paper_title: A Human Identification Technique Using Images of the Iris and Wavelet Transform

paper_content:

A new approach for recognizing the iris of the human eye is presented. Zero-crossings of the wavelet transform at various resolution levels are calculated over concentric circles on the iris, and the resulting one-dimensional (1-D) signals are compared with model features using different dissimilarity functions.
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paper_title: Image understanding for iris biometrics : A survey

paper_content:

This survey covers the historical development and current state of the art in image understanding for iris biometrics. Most research publications can be categorized as making their primary contribution to one of the four major modules in iris biometrics: image acquisition, iris segmentation, texture analysis and matching of texture representations. Other important research includes experimental evaluations, image databases, applications and systems, and medical conditions that may affect the iris. We also suggest a short list of recommended readings for someone new to the field to quickly grasp the big picture of iris biometrics.
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paper_title: Use of one-dimensional iris signatures to rank iris pattern similarities

paper_content:

A one-dimensional approach to iris recognition is presented. It is translation-, rotation-, illumination-, and scale-invariant. Traditional iris recognition systems typically use a two-dimensional iris signature that requires circular rotation for pattern matching. The new approach uses the Du measure as a matching mechanism, and generates a set of the most probable matches (ranks) instead of only the best match. Since the method generates one-dimensional signatures that are rotation-invariant, the system could work with eyes that are tilted. Moreover, the system will work with less of the iris than commercial systems, and thus could enable partial-iris recognition. In addition, this system is more tolerant of noise. Finally, this method is simple to implement, and its computational complexity is relatively low.
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paper_title: Iris recognition: an emerging biometric technology

paper_content:

In this paper, iris recognition as one of the important method of biometrics-based identification systems and iris recognition algorithm is described. As technology advances and information and intellectual properties are wanted by many unauthorized personnel. As a result many organizations have being searching ways for more secure authentication methods for the user access. In network security there is a vital emphasis on the automatic personal identification. Due to its inherent advantages biometric based verification especially iris identification is gaining a lot of attention. Iris recognition uses iris patterns for personnel identification. The system steps are capturing iris patterns; determining the location of iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting iris code based on texture analysis. The system has been implemented and tested using dataset of number of samples of iris data with different contrast quality. The developed algorithm performs satisfactorily on the images, provides 93% accuracy. Experimental results show that the proposed method has an encouraging performance.
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paper_title: Improving Iris Recognition Accuracy via Cascaded Classifiers

paper_content:

As a reliable approach to human identification, iris recognition has received increasing attention in recent years. The most distinguishing feature of an iris image comes from the fine spatial changes of the image structure. So iris pattern representation must characterize the local intensity variations in iris signals. However, the measurements from minutiae are easily affected by noise, such as occlusions by eyelids and eyelashes, iris localization error, nonlinear iris deformations, etc. This greatly limits the accuracy of iris recognition systems. In this paper, an elastic iris blob matching algorithm is proposed to overcome the limitations of local feature based classifiers (LFC). In addition, in order to recognize various iris images efficiently a novel cascading scheme is proposed to combine the LFC and an iris blob matcher. When the LFC is uncertain of its decision, poor quality iris images are usually involved in intra-class comparison. Then the iris blob matcher is resorted to determine the input iris' identity because it is capable of recognizing noisy images. Extensive experimental results demonstrate that the cascaded classifiers significantly improve the system's accuracy with negligible extra computational cost.
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paper_title: Iris Recognition System Using Fractal Dimensions of Haar Patterns

paper_content:

Classification of iris templates based on their texture patterns is one of the most effective methods in iris recognition systems. This paper proposes a novel algorithm for automatic iris classification based on fractal dimensions of Haar wavelet transforms is presented. Fractal dimensions obtained from multiple scale features are used to characterize the textures completely. Haar wavelet is applied in order to extract the multiple scale features at different resolutions from the iris image. Fractal dimensions are estimated from these patterns and a classifier is used to recognize the given image from a data base. Performance comparison was made among different classifiers.
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paper_title: Iris verification using correlation filters

paper_content:

Iris patterns are believed to be an important class of biometrics suitable for subject verification and identification applications. Earlier methods proposed for iris recognition were based on generating iris codes from features generated by applying Gabor wavelet processing to iris images. Another approach to image recognition is the use of correlation filters. Correlation filter methods differ from many image-based recognition approaches in that two-dimensional Fourier transforms of the images are used in this approach. In correlation filter methods, normal variations in an authentic iris image can be accommodated by designing a frequency-domain array (called a correlation filter) that captures the consistent part of iris images while deemphasizing the varying parts. Correlation filters also offer other benefits such as shift-invariance, graceful degradation and closed-form solutions. In this paper, we discuss the basics of correlation filters and show how they can be used for iris verification.
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paper_title: Localized Iris Image Quality Using 2-D Wavelets

paper_content:

The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enroll (FTE) rates. In this paper, a wavelet-based quality measure for iris images is proposed. The merit of the this approach lies in its ability to deliver good spatial adaptivity and determine local quality measures for different regions of an iris image. Our experiments demonstrate that the proposed quality index can reliably predict the matching performance of an iris recognition system. By incorporating local quality measures in the matching algorithm, we also observe a relative matching performance improvement of about 20% and 10% at the equal error rate (EER), respectively, on the CASIA and WVU iris databases.
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paper_title: Text-Independent Writer Identification Based on Fusion of Dynamic and Static Features

paper_content:

Handwriting recognition is a traditional and natural approach for personal authentication. Compared to signature verification, text-independent writer identification has gained more attention for its advantage of denying imposters in recent years. Dynamic features and static features of the handwriting are usually adopted for writer identification separately. For text-independent writer identification, by using a single classifier with the dynamic or the static feature, the accuracy is low, and many characters are required (more than 150 characters on average). In this paper, we developed a writer identification method to combine the matching results of two classifiers which employs the static feature (texture) and dynamic features individually. Sum-Rule, Common Weighted Sum-Rule and User-specific Sum-Rule are applied as the fusion strategy. Especially, we gave an improvement for the user-specific Sum-Rule algorithm by using an error-score. Experiments were conducted on the NLPR handwriting database involving 55 persons. The results show that the combination methods can improve the identification accuracy and reduce the number of characters required.
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paper_title: ECG to identify individuals

paper_content:

The electrocardiogram (ECG also called EKG) trace expresses cardiac features that are unique to an individual. The ECG processing followed a logical series of experiments with quantifiable metrics. Data filters were designed based upon the observed noise sources. Fiducial points were identified on the filtered data and extracted digitally for each heartbeat. From the fiducial points, stable features were computed that characterize the uniqueness of an individual. The tests show that the extracted features are independent of sensor location, invariant to the individual's state of anxiety, and unique to an individual.
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paper_title: ECG analysis: a new approach in human identification

paper_content:

A new approach in human identification is investigated. For this purpose, a standard 12-lead electrocardiogram (ECG) recorded during rest is used. Selected features extracted from the ECG are used to identify a person in a predetermined group. Multivariate analysis is used for the identification task. Experiments show that it is possible to identify a person by features extracted from one lead only. Hence, only three electrodes have to be attached on the person to be identified. This makes the method applicable without too much effort.
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paper_title: 67 Biometrics Verification: a Literature Survey

paper_content:

Biometric verification refers to an automatic verification of a person based on some specific biometric features derived from his/her physiological and/or behavioral characteristics. A biometric verification system has more capability to reliably distinguish between an authorized person and an imposter than the traditional systems that use a card or a password. In biometrics, a person could be recognized based on who he/she is rather than what he/she has (ID card) or what he/she knows (password). Currently, biometrics finds use in ATMs, computers, security installations, mobile phones, credit cards, health and social services. The future in biometrics seems to belong to the multimodal biometrics (a biometric system using more than one biometric feature) as a unimodal biometric system (biometric system using single biometric feature) has to contend with a number of problems. In this paper, a survey of some of the unimodal biometrics will be presented that are either currently in use across a range of environments or those still in limited use or under development, or still in the research realm.
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paper_title: ECG Biometric Recognition Without Fiducial Detection

paper_content:

Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric, the human heartbeat, can be used for identity recognition. Existing approaches address the problem by using electrocardiogram (ECG) data and the fiducials of the different parts of the heartrate. However, the current fiducial detection tools are inadequate for this application since the boundaries of waveforms are difficult to detect, locate and define. In this paper, an ECG biometric recognition method that does not require any waveform detections is introduced based on classification of coefficients from the discrete cosine transform (DCT) of the Autocorrelation (AC) sequence of ECG data segments. Low false negative rates, low false positive rates and a 100% subject recognition rate for healthy subjects can be achieved for parameters that are suitable for the database.
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paper_title: ECG signal based human identification method using features in temporal and wavelet domains

paper_content:

This paper presents an effective method for human identification using temporal and wavelet domain features extracted from electrocardiogram (ECG) signal. Instead of directly using the ECG data of a person as the feature, first, it is shown that a few number of reflection coefficients extracted from the autocorrelation function of the data can efficiently perform the recognition task. Next, the discrete wavelet transform (DWT) coefficients are utilized as features, which offer even a better recognition performance. A combination of these two features is found to demonstrate a high within class compactness and between class separation. A pre-processing scheme is incorporated prior to feature extraction to reduce the effect of different noises and artefacts. In the recognition phase, a linear discriminant based classifier is employed, where the two features are jointly utilized. The proposed human identification method has been tested on a standard ECG database and high recognition accuracy is achieved with a low feature dimension.
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paper_title: User-Customized Password Speaker Verification Using Multiple Reference and Background Models

paper_content:

This paper discusses and optimizes an HMM/GMM based User-Customized Password Speaker Verification (UCP-SV) system. Unlike text-dependent speaker verification, in UCP-SV systems, customers can choose their own passwords with no lexical constraints. The password has to be pronounced a few times during the enrollment step to create a customer dependent model. Although potentially more ``user-friendly'', such systems are less understood and actually exhibit several practical issues, including automatic HMM inference, speaker adaptation, and efficient likelihood normalization. In our case, HMM inference (HMM topology) is performed using hybrid HMM/MLP systems, while the parameters of the inferred model, as well as their adaptation, will use GMMs. However, the evaluation of a UCP-SV baseline system shows that the background model used for likelihood normalization is the main difficulty. Therefore, to circumvent this problem, the main contribution of the paper is to investigate the use of multiple reference models for customer acoustic modeling and multiple background models for likelihood normalization. In this framework, several scoring techniques are investigated, such as Dynamic Model Selection (DMS) and fusion techniques. Results on two different experimental protocols show that an appropriate selection criteria for customer and background models can improve significantly the UCP-SV performance, making the UCP-SV system quite competitive with a text-dependent SV system. Finally, as customers' passwords are short, a comparative experiment using the conventional GMM-UBM text-independent approach is also conducted.
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paper_title: Text-Dependent Speaker Recognition

paper_content:

Text-dependent speaker recognition characterizes a speaker recognition task, such as verification or identification, in which the set of words (or lexicon) used during the testing phase is a subset of the ones present during the enrollment phase. The restricted lexicon enables very short enrollment (or registration) and testing sessions to deliver an accurate solution but, at the same time, represents scientific and technical challenges. Because of the short enrollment and testing sessions, text-dependent speaker recognition technology is particularly well suited for deployment in large-scale commercial applications. These are the bases for presenting an overview of the state of the art in text-dependent speaker recognition as well as emerging research avenues. In this chapter, we will demonstrate the intrinsic dependence that the lexical content of the password phrase has on the accuracy. Several research results will be presented and analyzed to show key techniques used in text-dependent speaker recognition systems from different sites. Among these, we mention multichannel speaker model synthesis and continuous adaptation of speaker models with threshold tracking. Since text-dependent speaker recognition is the most widely used voice biometric in commercial deployments, several
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paper_title: 67 Biometrics Verification: a Literature Survey

paper_content:

Biometric verification refers to an automatic verification of a person based on some specific biometric features derived from his/her physiological and/or behavioral characteristics. A biometric verification system has more capability to reliably distinguish between an authorized person and an imposter than the traditional systems that use a card or a password. In biometrics, a person could be recognized based on who he/she is rather than what he/she has (ID card) or what he/she knows (password). Currently, biometrics finds use in ATMs, computers, security installations, mobile phones, credit cards, health and social services. The future in biometrics seems to belong to the multimodal biometrics (a biometric system using more than one biometric feature) as a unimodal biometric system (biometric system using single biometric feature) has to contend with a number of problems. In this paper, a survey of some of the unimodal biometrics will be presented that are either currently in use across a range of environments or those still in limited use or under development, or still in the research realm.
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paper_title: A new approach to utterance verification based on neighborhood information in model space

paper_content:

We propose to use neighborhood information in model space to perform utterance verification (UV). At first, we present a nested-neighborhood structure for each underlying model in model space and assume the underlying model's competing models sit in one of these neighborhoods, which is used to model alternative hypothesis in UV. Bayes factors (BF) is first introduced to UV and used as a major tool to calculate confidence measures based on the above idea. Experimental results in the Bell Labs communicator system show that the new method has dramatically improved verification performance when verifying correct words against mis-recognized words in the recognizer's output, relatively more than 20% reduction in equal error rate (EER) when comparing with the standard approach based on likelihood ratio testing and anti-models.
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paper_title: A Tutorial on Principal Component Analysis

paper_content:

Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly understood. The goal of this paper is to dispel the magic behind this black box. This manuscript focuses on building a solid intuition for how and why principal component analysis works. This manuscript crystallizes this knowledge by deriving from simple intuitions, the mathematics behind PCA. This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of PCA as well as the when, the how and the why of applying this technique.
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paper_title: Palmprint identification using feature-level fusion

paper_content:

In this paper, we propose a feature-level fusion approach for improving the efficiency of palmprint identification. Multiple elliptical Gabor filters with different orientations are employed to extract the phase information on a palmprint image, which is then merged according to a fusion rule to produce a single feature called the Fusion Code. The similarity of two Fusion Codes is measured by their normalized hamming distance. A dynamic threshold is used for the final decisions. A database containing 9599 palmprint images from 488 different palms is used to validate the performance of the proposed method. Comparing our previous non-fusion approach and the proposed method, improvement in verification and identification are ensured.
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paper_title: Palmprint Matching Using LBP

paper_content:

Automatic person verification has become a crucial problem in networked society. Providing authorized access to a person is a challenging task. To resolve all these issues, biometrics has emerged as an important and effective solution. Although many biometrics have been proposed, public acceptability and privacy concerns remain a block to implementation in public services. Another concern is computational overhead in real-time scenario. This paper discusses a new method and its related analysis that based on palm print verification using local binary pattern (LBP) to capture the palm print texture. Experimental results demonstrate that this technique is simple, highly accurate and takes less time to process the palm print image.
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paper_title: 67 Biometrics Verification: a Literature Survey

paper_content:

Biometric verification refers to an automatic verification of a person based on some specific biometric features derived from his/her physiological and/or behavioral characteristics. A biometric verification system has more capability to reliably distinguish between an authorized person and an imposter than the traditional systems that use a card or a password. In biometrics, a person could be recognized based on who he/she is rather than what he/she has (ID card) or what he/she knows (password). Currently, biometrics finds use in ATMs, computers, security installations, mobile phones, credit cards, health and social services. The future in biometrics seems to belong to the multimodal biometrics (a biometric system using more than one biometric feature) as a unimodal biometric system (biometric system using single biometric feature) has to contend with a number of problems. In this paper, a survey of some of the unimodal biometrics will be presented that are either currently in use across a range of environments or those still in limited use or under development, or still in the research realm.
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paper_title: Competitive coding scheme for palmprint verification

paper_content:

There is increasing interest in the development of reliable, rapid and non-intrusive security control systems. Among the many approaches, biometrics such as palmprints provide highly effective automatic mechanisms for use in personal identification. This paper presents a new method for extracting features from palmprints using the competitive coding scheme and angular matching. The competitive coding scheme uses multiple 2-D Gabor filters to extract orientation information from palm lines. This information is then stored in a feature vector called the competitive code. The angular matching with an effective implementation is then defined for comparing the proposed codes, which can make over 9,000 comparisons within 1s. In our testing database of 7,752 palmprint samples from 386 palms, we can achieve a high genuine acceptance rate of 98.4% and a low false acceptance rate of 3/spl times/10/sup -6/%. The execution time for the whole process of verification, including preprocessing, feature extraction and final matching, is 1s.
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paper_title: Palm vein recognition with Local Binary Patterns and Local Derivative Patterns

paper_content:

Palm vein feature extraction from near infrared images is a challenging problem in hand pattern recognition. In this paper, a promising new approach based on local texture patterns is proposed. First, operators and histograms of multi-scale Local Binary Patterns (LBPs) are investigated in order to identify new efficient descriptors for palm vein patterns. Novel higher-order local pattern descriptors based on Local Derivative Pattern (LDP) histograms are then investigated for palm vein description. Both feature extraction methods are compared and evaluated in the framework of verification and identification tasks. Extensive experiments on CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database) identify the LBP and LDP descriptors which are better adapted to palm vein texture. Tests on the CASIA datasets also show that the best adapted LDP descriptors consistently outperform their LBP counterparts in both palm vein verification and identification.
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Title: A Review on Biometric Recognition

Section 1: Introduction
Description 1: Introduce the concept of biometric identification, its importance over traditional methods, and how a biometric system operates.

Section 2: Face Recognition
Description 2: Discuss various methods and algorithms used for facial recognition, their advancements over time, and their respective advantages and disadvantages.

Section 3: Iris Recognition
Description 3: Explain the process of iris recognition and review various techniques and historical developments in this field.

Section 4: ECG Recognition System
Description 4: Describe the use of electrocardiogram (ECG) signals in biometric recognition, including feature extraction methods and classification techniques.

Section 5: Voice Recognition
Description 5: Cover the principles of voice recognition, various system types (isolated word, connected word, continuous speech), and different feature extraction and classification techniques.

Section 6: Palm Recognition
Description 6: Explain palm recognition techniques, highlight different approaches for palm verification, and discuss the challenges and advancements in palm print and palm vein recognition.

Section 7: Conclusion
Description 7: Summarize the importance of various biometric recognition systems and discuss their applications in enhancing security and identification processes.