Class Energy Image Analysis for Video Sensor-Based Gait Recognition: A Review
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paper_title: Multiple views gait recognition using View Transformation Model based on optimized Gait Energy Image

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Gait is one of well recognized biometrics that has been widely used for human identification. However, the current gait recognition might have difficulties due to viewing angle being changed. This is because the viewing angle under which the gait signature database was generated may not be the same as the viewing angle when the probe data are obtained. This paper proposes a new multi-view gait recognition approach which tackles the problems mentioned above. Being different from other approaches of same category, this new method creates a so called View Transformation Model (VTM) based on spatial-domain Gait Energy Image (GEI) by adopting Singular Value Decomposition (SVD) technique. To further improve the performance of the proposed VTM, Linear Discriminant Analysis (LDA) is used to optimize the obtained GEI feature vectors. When implementing SVD there are a few practical problems such as large matrix size and over-fitting. In this paper, reduced SVD is introduced to alleviate the effects caused by these problems. Using the generated VTM, the viewing angles of gallery gait data and probe gait data can be transformed into the same direction. Thus, gait signatures can be measured without difficulties. The extensive experiments show that the proposed algorithm can significantly improve the multiple view gait recognition performance when being compared to the similar methods in literature.
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paper_title: Variable silhouette energy image representations for recognizing human actions

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Recognizing human actions is an important topic in the computer vision community. One of the challenges of recognizing human actions is describing for the variability that arises when arbitrary view camera captures human performing actions. In this paper, we propose a spatio-temporal silhouette representation, called silhouette energy image (SEI), and multiple variability action models, to characterize motion and shape properties for automatic recognition of human actions in daily life. To address the variability in the recognition of human actions, several parameters, such as anthropometry of the person, speed of the action, phase (starting and ending state of an action), camera observations (distance from camera, slanting motion, and rotation of human body), and view variations are proposed. We construct the variability (or adaptable) models based on SEI and the proposed parameters. Global motion descriptors express the spatio-temporal properties of combined energy templates (SEI and variability action models). Our construction of the optimal model for each action and view is based on the support vectors of global motion descriptions of action models. We recognize different daily human actions of different styles successfully in the indoor and outdoor environment. Our experimental results show that the proposed method of human action recognition is robust, flexible and efficient.
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paper_title: Multifeature-Based High-Resolution Palmprint Recognition

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Palmprint is a promising biometric feature for use in access control and forensic applications. Previous research on palmprint recognition mainly concentrates on low-resolution (about 100 ppi) palmprints. But for high-security applications (e.g., forensic usage), high-resolution palmprints (500 ppi or higher) are required from which more useful information can be extracted. In this paper, we propose a novel recognition algorithm for high-resolution palmprint. The main contributions of the proposed algorithm include the following: 1) use of multiple features, namely, minutiae, density, orientation, and principal lines, for palmprint recognition to significantly improve the matching performance of the conventional algorithm. 2) Design of a quality-based and adaptive orientation field estimation algorithm which performs better than the existing algorithm in case of regions with a large number of creases. 3) Use of a novel fusion scheme for an identification application which performs better than conventional fusion methods, e.g., weighted sum rule, SVMs, or Neyman-Pearson rule. Besides, we analyze the discriminative power of different feature combinations and find that density is very useful for palmprint recognition. Experimental results on the database containing 14,576 full palmprints show that the proposed algorithm has achieved a good performance. In the case of verification, the recognition system's False Rejection Rate (FRR) is 16 percent, which is 17 percent lower than the best existing algorithm at a False Acceptance Rate (FAR) of 10-5, while in the identification experiment, the rank-1 live-scan partial palmprint recognition rate is improved from 82.0 to 91.7 percent.
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paper_title: Object Detection with Discriminatively Trained Part-Based Models

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We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.
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paper_title: Biologically inspired feature manifold for gait recognition

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Using biometric resources to recognize a person has been a recent concentration on computer vision. Previously, biometric research has forced on utilizing iris, finger print, palm print, and shoe print to authenticate and authorized a human. However, these conventional biometric resources suffer from some obviously limitation, such as: strictly distance requirement, too many user cooperation requirement and so on. Compared with the difficulties of utilization through conventional biometric resources, human gait can be easily acquired and utilized in many fields. A human's walk image can reflect the walker's physical characteristics and psychological state, and therefore, the gait feature can be used to recognize a person. In order to achieve better performance of gait recognition we represent the gait image using C1 units, which correspond to the complex cells in human visual cortex, and use a maximum mechanism to keep only the maximum response of each local area of S1 units. To enhance the gait recognition rate, we take the label information into account and utilize the discriminative locality alignment (DLA) method to classify, which is a top level discriminate manifold learning based subspace learning algorithm. Experiment on University of South Florida (USF) dataset shows: (1) the proposed C1Gait+DLA algorithms can achieve better performance than the state-of-art algorithms and (2) DLA can duly preserve both the local geometry and the discriminative information for recognition.
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paper_title: Bayesian modeling of dynamic scenes for object detection

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Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this paper, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foregrounds modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes.
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paper_title: General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

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Traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis (LDA) because of the undersample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two-dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA, compared with existing preprocessing methods such as the principal components analysis (PCA) and 2DLDA, include the following: 1) the USP is reduced in subsequent classification by, for example, LDA, 2) the discriminative information in the training tensors is preserved, and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, whereas that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor-function-based image decompositions for image understanding and object recognition, we develop three different Gabor-function-based image representations: 1) GaborD is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS, and GaborSD representations are applied to the problem of recognizing people from their averaged gait images. A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS, or GaborSD image representation, then using GDTA to extract features and, finally, using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database. Experimental comparisons are made with nine state-of-the-art classification methods in gait recognition.
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paper_title: Simplest representation yet for gait recognition: averaged silhouette

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We present a robust representation for gait recognition that is compact, easy to construct, and affords efficient matching. Instead of a time series based representation comprising of a sequence of raw silhouette frames or of features extracted therein, as has been the practice, we simply align and average the silhouettes over one gait cycle. We then base recognition on the Euclidean distance between these averaged silhouette representations. We show, using the recently formulated gait challenge problem (www.gaitchallenge.org), that the improvement in execution time is 30 times while possessing recognition power that is comparable to the gait baseline algorithm, which is becoming the comparison standard in gait recognition. Experiments with portions of the average silhouette representation show that recognition power is not entirely derived from upper body shape, rather the dynamics of the legs also contribute equally to recognition. However, this study does raise intriguing doubts about the need for accurate shape and dynamics representations for gait recognition.
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paper_title: Gait recognition based on shape and motion analysis of silhouette contours

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This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject's silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject's shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject's back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject's leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods.
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paper_title: Infrared gait recognition based on wavelet transform and support vector machine

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To detect human body and remove noises from complex background, illumination variations and objects, the infrared thermal imaging was applied to collect gait video and an infrared thermal gait database was established in this paper. Multi-variables gait feature was extracted according to a novel method combining integral model and simplified model. Also the wavelet transform, invariant moments and skeleton theory were used to extract gait features. The support vector machine was employed to classify gaits. This proposed method was applied to the infrared gait database and achieved 78%-91% for the probability of correct recognition. The recognition rates were insensitive for the items of holding ball and loading package. However, there was significant influence for the item of wearing heavy coat. The infrared thermal imaging was potential for better description of human body moving within image sequences.
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paper_title: Evaluation framework on translation-invariant representation for cumulative foot pressure image

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Ground reaction force can be used to distinguish different human gait like limb movement. Cumulative foot pressure image is a 2-D data that recorded the spatial and temporal change of ground reaction force during one gait cycle. However, when putting it into practice as a new biometric for gait recognition, it suffers from the problem of large translation variation within class caused by wearing different shoes and walking in different speed. In this paper, an evaluation framework is proposed to address the problem. The framework consists of a database containing cumulative foot pressure images and a well designed benchmark. The data are collected from 118 subjects. A locality-constrained sparse coding scheme is developed to be compared with the benchmark PCA approach. Experimental results show the potential of evaluation framework for evaluating the translation-invariant power of image representation algorithms.
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paper_title: The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication

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This paper presents the largest inertial sensor-based gait database in the world, which is made open to the research community, and its application to a statistically reliable performance evaluation for gait-based personal authentication. We construct several datasets for both accelerometer and gyroscope of three inertial measurement units and a smartphone around the waist of a subject, which include at most 744 subjects (389 males and 355 females) with ages ranging from 2 to 78 years. The database has several advantages: a large number of subjects with a balanced gender ratio, variations of sensor types, sensor locations, and ground slope conditions. Therefore, we can reliably analyze the dependence of gait authentication performance on a number of factors such as gender, age group, sensor type, ground condition, and sensor location. The results with the latest existing authentication methods provide several insights for these factors. HighlightsWe present the world largest inertial sensor-based database to the community.Based on the database, females have a better recognition performance than males.People have the best recognition performance at their twenties.An accelerometer has a better recognition performance than a gyroscope.
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paper_title: Time-sliced averaged motion history image for gait recognition

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In this paper, we propose a time-sliced averaged motion history image (TAMHI) alongside the histograms of oriented gradients (HOG) to generate gait signatures in a gait recognition problem. Building on the motion history image (MHI), TAMHI divides the gait cycle into several regular time windows to generate the same number of TAMHI composite images. HOG descriptors are then calculated on these composite images to obtain the gait signature. The time-slicing procedure to produce multi-composite images preserve more detailed transient information of gait cycles. Additionally, time-normalization also introduces gait length invariancy into the representation, hence, offering a better recognition rate to slight changes in walking speed.
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paper_title: Recognition of human actions using motion history information extracted from the compressed video

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Human motion analysis is a recent topic of interest among the computer vision and video processing community. Research in this area is motivated by its wide range of applications such as surveillance and monitoring systems. In this paper we describe a system for recognition of various human actions from compressed video based on motion history information. We introduce the notion of quantifying the motion involved, through what we call Motion Flow History (MFH). The encoded motion information readily available in the compressed MPEG stream is used to construct the coarse Motion History Image (MHI) and the corresponding MFH. The features extracted from the static MHI and MFH compactly characterize the spatio-temporal and motion vector information of the action. Since the features are extracted from the partially decoded sparse motion data, the computational load is minimized to a great extent. The extracted features are used to train the KNN, Neural network, SVM and the Bayes classifiers for recognizing a set of seven human actions. The performance of each feature set with respect to various classifiers are analyzed. q 2003 Elsevier B.V. All rights reserved.
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paper_title: Silhouette Analysis-Based Gait Recognition for Human Identification

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Human identification at a distance has recently gained growing interest from computer vision researchers. Gait recognition aims essentially to address this problem by identifying people based on the way they walk. In this paper, a simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed. For each image sequence, a background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure. Then, eigenspace transformation based on principal component analysis (PCA) is applied to time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification techniques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait. Extensive experimental results on outdoor image sequences demonstrate that the proposed algorithm has an encouraging recognition performance with relatively low computational cost.
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paper_title: Locality preserving discriminant projections for face and palmprint recognition

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A new subspace learning algorithm called locality preserving discriminant projections (LPDP) is proposed by adding the criterion of maximum margin criterion (MMC) into the objective function of locality preserving projections (LPP). LPDP retains the locality preserving characteristic of LPP and utilizes the global discriminative structures obtained from MMC, which can maximize the between-class distance and minimize the within-class distance. Thus, our proposed LPDP combining manifold criterion and Fisher criterion has more discriminanting power, and is more suitable for recognition tasks than LPP, which considers only the local information for classification tasks. Moreover, two kinds of tensorized (multilinear) forms of LPDP are also derived in this paper. One is iterative while the other is non-iterative. The proposed LPDP method is applied to face and palmprint biometrics and is examined using the Yale and ORL face image databases, as well as the PolyU palmprint database. Experimental results demonstrate the effectiveness of the proposed LPDP method.
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paper_title: Integrated face and gait recognition from multiple views

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We develop a view-normalization approach to multi-view face and gait recognition. An image-based visual hull (IBVH) is computed from a set of monocular views and used to render virtual views for tracking and recognition. We determine canonical viewpoints by examining the 3D structure, appearance (texture), and motion of the moving person. For optimal face recognition, we place virtual cameras to capture frontal face appearance; for gait recognition we place virtual cameras to capture a side-view of the person. Multiple cameras can be rendered simultaneously, and camera position is dynamically updated as the person moves through the workspace. Image sequences from each canonical view are passed to an unmodified face or gait recognition algorithm. We show that our approach provides greater recognition accuracy than is obtained using the unnormalized input sequences, and that integrated face and gait recognition provides improved performance over either modality alone. Canonical view estimation, rendering, and recognition have been efficiently implemented and can run at near real-time speeds.
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paper_title: Intracellular DNA recognition

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This Review describes how microbial or self DNA that enters the cytoplasm can be detected by various mechanisms and triggers a range of cellular responses. These include the induction of antiviral innate immune responses and inflammasome-dependent caspase-1 activation and pyroptotic cell death.
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paper_title: Toward Real-Time Automated Detection of Turns during Gait Using Wearable Inertial Measurement Units

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Previous studies have presented algorithms for detection of turns during gait using wearable sensors, but those algorithms were not built for real-time use. This paper therefore investigates the optimal approach for real-time detection of planned turns during gait using wearable inertial measurement units. Several different sensor positions (head, back and legs) and three different detection criteria (orientation, angular velocity and both) are compared with regard to their ability to correctly detect turn onset. Furthermore, the different sensor positions are compared with regard to their ability to predict the turn direction and amplitude. The evaluation was performed on ten healthy subjects who performed left/right turns at three amplitudes (22, 45 and 90 degrees). Results showed that turn onset can be most accurately detected with sensors on the back and using a combination of orientation and angular velocity. The same setup also gives the best prediction of turn direction and amplitude. Preliminary measurements with a single amputee were also performed and highlighted important differences such as slower turning that need to be taken into account.
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paper_title: Scalable multi-class object detection

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Scalability of object detectors with respect to the number of classes is a very important issue for applications where many object classes need to be detected. While combining single-class detectors yields a linear complexity for testing, multi-class detectors that localize all objects at once come often at the cost of a reduced detection accuracy. In this work, we present a scalable multi-class detection algorithm which scales sublinearly with the number of classes without compromising accuracy. To this end, a shared discriminative codebook of feature appearances is jointly trained for all classes and detection is also performed for all classes jointly. Based on the learned sharing distributions of features among classes, we build a taxonomy of object classes. The taxonomy is then exploited to further reduce the cost of multi-class object detection. Our method has linear training and sublinear detection complexity in the number of classes. We have evaluated our method on the challenging PASCAL VOC'06 and PASCAL VOC'07 datasets and show that scaling the system does not lead to a loss in accuracy.
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paper_title: Kernel coupled distance metric learning for gait recognition and face recognition

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Abstract The performances of biometrics may be adversely impact by different walking states, walking directions, resolutions of gait sequence images, pose variation and low resolution of face images. To address these problems, we presented a kernel coupled distance metric learning (KCDML) method after considering matching among different data collections. By using a kernel trick and a specialized locality preserving criterion, we formulated the problem of kernel coupled distance metric learning as an optimization problem whose aims are to search for the pair-wise samples staying as close as possible and to preserve the local structure intrinsic data geometry. Instead of an iterative solution, one single generalized eigen-decomposition can be leveraged to compute the two transformation matrices for two classifications of data sets. The effectiveness of the proposed method is empirically demonstrated on gait and face recognition tasks' results which outperform four linear subspace solutions' (i.e. CDML, PCA, LPP, LDA) and four nonlinear subspace solutions' (i.e. Huang's method, PCA-RBF, KPCA, KLPP).
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paper_title: Robust Face Recognition via Sparse Representation

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We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.
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paper_title: The Recognition of Human Movement Using Temporal Templates

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A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms.
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paper_title: A Frequency-based Approach for Features Fusion in Fingerprint and Iris Multimodal Biometric Identification Systems

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The basic aim of a biometric identification system is to discriminate automatically between subjects in a reliable and dependable way, according to a specific-target application. Multimodal biometric identification systems aim to fuse two or more physical or behavioral traits to provide optimal False Acceptance Rate (FAR) and False Rejection Rate (FRR), thus improving system accuracy and dependability. In this paper, an innovative multimodal biometric identification system based on iris and fingerprint traits is proposed. The paper is a state-of-the-art advancement of multibiometrics, offering an innovative perspective on features fusion. In greater detail, a frequency-based approach results in a homogeneous biometric vector, integrating iris and fingerprint data. Successively, a hamming-distance-based matching algorithm deals with the unified homogenous biometric vector. The proposed multimodal system achieves interesting results with several commonly used databases. For example, we have obtained an interesting working point with FAR = 0% and FRR = 5.71% using the entire fingerprint verification competition (FVC) 2002 DB2B database and a randomly extracted same-size subset of the BATH database. At the same time, considering the BATH database and the FVC2002 DB2A database, we have obtained a further interesting working point with FAR = 0% and FRR = 7.28% ÷ 9.7%.
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paper_title: Incremental Learning for Video-Based Gait Recognition With LBP Flow

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Gait analysis provides a feasible approach for identification in intelligent video surveillance. However, the effectiveness of the dominant silhouette-based approaches is overly dependent upon background subtraction. In this paper, we propose a novel incremental framework based on optical flow, including dynamics learning, pattern retrieval, and recognition. It can greatly improve the usability of gait traits in video surveillance applications. Local binary pattern (LBP) is employed to describe the texture information of optical flow. This representation is called LBP flow, which performs well as a static representation of gait movement. Dynamics within and among gait stances becomes the key consideration for multiframe detection and tracking, which is quite different from existing approaches. To simulate the natural way of knowledge acquisition, an individual hidden Markov model (HMM) representing the gait dynamics of a single subject incrementally evolves from a population model that reflects the average motion process of human gait. It is beneficial for both tracking and recognition and makes the training process of the HMM more robust to noise. Extensive experiments on widely adopted databases have been carried out to show that our proposed approach achieves excellent performance.
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paper_title: A Review of Vision-Based Gait Recognition Methods for Human Identification

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Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field.
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paper_title: Model-Based Human Gait Recognition Via Deterministic Learning

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In this paper, we present a new model-based approach for human gait recognition in the sagittal plane via deterministic learning (DL) theory. Side silhouette lower limb joint angles characterize the gait system dynamics and are selected as the gait feature. Locally accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks through DL. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L 1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Therefore, the test gait pattern can be rapidly recognized according to the smallest error principle. In contrast to other existing approaches, the main focus of this paper is on obtaining and reusing the knowledge of the gait system dynamics. Finally, experiments are carried out on the CASIA-A and CASIA-B gait databases to benchmark the effectiveness of the proposed approach.
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paper_title: Gait Recognition Using Wavelet Packet Silhouette Representation and Transductive Support Vector Machines

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Gait is an idiosyncratic biometric that can be used for human identification at a distance and as a result gained growing interest in intelligent visual surveillance. In this pa- per, an efficient gait recognition method based on describing subject outer body contour deformations using wavelet packets is proposed. With the use of Matching Pursuit algorithm, k bases of wavelet packet tree that have maximum similarity to the signal are selected and corresponding coefficients are used as features. Finally, Transductive Support vector Machine (TSVM) classification is utilized on computed eigengait space for semi-supervised identification. The proposed method of selecting features which uses a complete orthogonal or near orthogonal basis from a wavelet packet library of bases and investigating the correlational structure of gait features for each individual using TSVM, result in encouraging identification performance.
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paper_title: Feature subset selection applied to model-free gait recognition

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In this paper, we tackle the problem of gait recognition based on the model-free approach. Numerous methods exist; they all lead to high dimensional feature spaces. To address the problem of high dimensional feature space, we propose the use of the Random Forest algorithm to rank features' importance. In order to efficiently search throughout subspaces, we apply a backward feature elimination search strategy. Our first experiments are carried out on unknown covariate conditions. Our first results suggest that the selected features contribute to increase the CCR of different existing classification methods. Secondary experiments are performed on unknown covariate conditions and viewpoints. Inspired by the location of our first experiments' features, we proposed a simple mask. Experimental results demonstrate that the proposed mask gives satisfactory results for all angles of the probe and consequently is not view specific. We also show that our mask performs well when an uncooperative experimental setup is considered as compared to the state-of-the art methods. As a consequence, we propose a panoramic gait recognition framework on unknown covariate conditions. Our results suggest that panoramic gait recognition can be performed under unknown covariate conditions. Our approach can greatly reduce the complexity of the classification problem while achieving fair correct classification rates when gait is captured with unknown conditions.
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paper_title: Gait Feature Subset Selection by Mutual Information

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Feature subset selection is an important preprocessing step for pattern recognition, to discard irrelevant and redundant information, as well as to identify the most important attributes. In this paper, we investigate a computationally efficient solution to select the most important features for gait recognition. The specific technique applied is based on mutual information (MI), which evaluates the statistical dependence between two random variables and has an established relation with the Bayes classification error. Extending our earlier research, we show that a sequential selection method based on MI can provide an effective solution for high-dimensional human gait data. To assess the performance of the approach, experiments are carried out based on a 73-dimensional model-based gait features set and on a 64 by 64 pixels model-free gait symmetry map on the Southampton HiD Gait database. The experimental results confirm the effectiveness of the method, removing about 50% of the model-based features and 95% of the symmetry map's pixels without significant loss in recognition capability, which outperforms correlation and analysis-of-variance-based methods.
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paper_title: Matching shape sequences in video with applications in human movement analysis

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We present an approach for comparing two sequences of deforming shapes using both parametric models and nonparametric methods. In our approach, Kendall's definition of shape is used for feature extraction. Since the shape feature rests on a non-Euclidean manifold, we propose parametric models like the autoregressive model and autoregressive moving average model on the tangent space and demonstrate the ability of these models to capture the nature of shape deformations using experiments on gait-based human recognition. The nonparametric model is based on dynamic time-warping. We suggest a modification of the dynamic time-warping algorithm to include the nature of the non-Euclidean space in which the shape deformations take place. We also show the efficacy of this algorithm by its application to gait-based human recognition. We exploit the shape deformations of a person's silhouette as a discriminating feature and provide recognition results using the nonparametric model. Our analysis leads to some interesting observations on the role of shape and kinematics in automated gait-based person authentication.
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paper_title: Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition

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This paper describes the winning algorithm we submitted to the recent NICE.I iris recognition contest. Efficient and robust segmentation of noisy iris images is one of the bottlenecks for non-cooperative iris recognition. To address this problem, a novel iris segmentation algorithm is proposed in this paper. After reflection removal, a clustering based coarse iris localization scheme is first performed to extract a rough position of the iris, as well as to identify non-iris regions such as eyelashes and eyebrows. A novel integrodifferential constellation is then constructed for the localization of pupillary and limbic boundaries, which not only accelerates the traditional integrodifferential operator but also enhances its global convergence. After that, a curvature model and a prediction model are learned to deal with eyelids and eyelashes, respectively. Extensive experiments on the challenging UBIRIS iris image databases demonstrate that encouraging accuracy is achieved by the proposed algorithm which is ranked the best performing algorithm in the recent open contest on iris recognition (the Noisy Iris Challenge Evaluation, NICE.I).
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paper_title: A survey on vision-based human action recognition

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Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human-computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research.
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paper_title: Review on Pedestrian Gait Feature Expression and Recognition

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Various methods for gait recognition are summarized by unique metods from anthropometry,spatial temporal,kinematics,kinetics,and video stream data forms.And video stream data are analyzed in detail.The expressions,meanings and characteristics including static,dynamic,and time-varying information among different energy image species methods are compared.Moreover,the fusion of gait features,gait and other biometric and extended gait recognition are reviewed.The assessment methods for gait recognition research are also presented.In addition,the future research directions of gait recognition are addressed.
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paper_title: Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition

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The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose gait recognition as a protection mechanism. Unlike previous work on gait recognition, which was based on the use of video sources, floor sensors or dedicated high-grade accelerometers, this paper reports the performance when the data is collected with a commercially available mobile device containing low-grade accelerometers. To be more specific, the used mobile device is the Google G1 phone containing the AK8976A embedded accelerometer sensor. The mobile device was placed at the hip on each volunteer to collect gait data. Preproccesing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 51 volunteers and resulted in an equal error rate (EER) of 20%.
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paper_title: Human Gait Recognition Using Patch Distribution Feature and Locality-Constrained Group Sparse Representation

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In this paper, we propose a new patch distribution feature (PDF) (i.e., referred to as Gabor-PDF) for human gait recognition. We represent each gait energy image (GEI) as a set of local augmented Gabor features, which concatenate the Gabor features extracted from different scales and different orientations together with the X-Y coordinates. We learn a global Gaussian mixture model (GMM) (i.e., referred to as the universal background model) with the local augmented Gabor features from all the gallery GEIs; then, each gallery or probe GEI is further expressed as the normalized parameters of an image-specific GMM adapted from the global GMM. Observing that one video is naturally represented as a group of GEIs, we also propose a new classification method called locality-constrained group sparse representation (LGSR) to classify each probe video by minimizing the weighted l1, 2 mixed-norm-regularized reconstruction error with respect to the gallery videos. In contrast to the standard group sparse representation method that is a special case of LGSR, the group sparsity and local smooth sparsity constraints are both enforced in LGSR. Our comprehensive experiments on the benchmark USF HumanID database demonstrate the effectiveness of the newly proposed feature Gabor-PDF and the new classification method LGSR for human gait recognition. Moreover, LGSR using the new feature Gabor-PDF achieves the best average Rank-1 and Rank-5 recognition rates on this database among all gait recognition algorithms proposed to date.
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paper_title: Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition

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In this paper, we introduce the Minutia Cylinder-Code (MCC): a novel representation based on 3D data structures (called cylinders), built from minutiae distances and angles. The cylinders can be created starting from a subset of the mandatory features (minutiae position and direction) defined by standards like ISO/IEC 19794-2 (2005). Thanks to the cylinder invariance, fixed-length, and bit-oriented coding, some simple but very effective metrics can be defined to compute local similarities and to consolidate them into a global score. Extensive experiments over FVC2006 databases prove the superiority of MCC with respect to three well-known techniques and demonstrate the feasibility of obtaining a very effective (and interoperable) fingerprint recognition implementation for light architectures.
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paper_title: Gait Recognition With Shifted Energy Image and Structural Feature Extraction

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In this paper, we present a novel and efficient gait recognition system. The proposed system uses two novel gait representations, i.e., the shifted energy image and the gait structural profile, which have increased robustness to some classes of structural variations. Furthermore, we introduce a novel method for the simulation of walking conditions and the generation of artificial subjects that are used for the application of linear discriminant analysis. In the decision stage, the two representations are fused. Thorough experimental evaluation, conducted using one traditional and two new databases, demonstrates the advantages of the proposed system in comparison with current state-of-the-art systems.
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paper_title: A survey of advances in biometric gait recognition

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Biometric gait analysis is to acquire biometric information such as identity, gender, ethnicity and age from people walking patterns. In the walking process, the human body shows regular periodic motion, especially upper and lower limbs, which reflects the individual's unique movement pattern. Compared to other biometrics, gait can be obtained from distance and is difficult to hide and camouflage. During the past ten years, gait has been a hot topic in computer vision with great progress achieved. In this paper, we give a general review and a simple survey of recent gait progresses.
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paper_title: Letters: Dual-ellipse fitting approach for robust gait periodicity detection

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A new gait period detection algorithm, dual-ellipse fitting (DEF) approach, is proposed. DEF is that two regions of the whole silhouette divided by the centroid are fitted into two ellipses, respectively. We construct the gait fluctuation as a periodic function which depends on the eccentricities of two halves of the silhouette over time. Experimental results show that the proposed method is robust to scale, translation, direction of walking and carrying a bag.
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paper_title: Robotic platform for human gait analysis.

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A hydraulically actuated platform with 4-degrees of freedom (4-DOF) was designed to be able to apply velocity- or acceleration-controlled floor surface perturbations to freely walking human subjects. The apparatus was required to provide velocity-controlled translational perturbations over the floor surface, rotational perturbations about the ankle joint, and acceleration-controlled vertical translational perturbations. The apparatus was physically constructed, and tested by both measurements of dynamics and repeatability. Crossover of movement from one DOF to another was shown to be less than 1 mm or 0.5 degrees for all desired perturbations. Repeated perturbations were nearly identical with a standard deviation of less than 0.2 mm over translational axes. The application of the platform to human gait research was demonstrated with a protocol of midstance phase perturbations (n=8). For this, the platform controller was programmed to randomly select one out of three conditions: (1) no movement (control); (2) upward perturbation of 0.8 g, 50 mm, 300 ms after heel contact; (3) downward perturbation of 0.8 g, 50 mm, 300 ms after heel contact. In total, 90 trials (3 conditions x 30 repetitions) were recorded for each subject. By singling out the SOL EMG and normalizing and averaging over the subject population, it was shown that the upward and downward perturbations elicited at least two distinctive stereotypical reflex responses in the ankle extensors, opposite in sign. All subjects reported comfort with the apparatus and nobody fell.
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paper_title: Multiple views gait recognition using View Transformation Model based on optimized Gait Energy Image

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Gait is one of well recognized biometrics that has been widely used for human identification. However, the current gait recognition might have difficulties due to viewing angle being changed. This is because the viewing angle under which the gait signature database was generated may not be the same as the viewing angle when the probe data are obtained. This paper proposes a new multi-view gait recognition approach which tackles the problems mentioned above. Being different from other approaches of same category, this new method creates a so called View Transformation Model (VTM) based on spatial-domain Gait Energy Image (GEI) by adopting Singular Value Decomposition (SVD) technique. To further improve the performance of the proposed VTM, Linear Discriminant Analysis (LDA) is used to optimize the obtained GEI feature vectors. When implementing SVD there are a few practical problems such as large matrix size and over-fitting. In this paper, reduced SVD is introduced to alleviate the effects caused by these problems. Using the generated VTM, the viewing angles of gallery gait data and probe gait data can be transformed into the same direction. Thus, gait signatures can be measured without difficulties. The extensive experiments show that the proposed algorithm can significantly improve the multiple view gait recognition performance when being compared to the similar methods in literature.
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paper_title: Variable silhouette energy image representations for recognizing human actions

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Recognizing human actions is an important topic in the computer vision community. One of the challenges of recognizing human actions is describing for the variability that arises when arbitrary view camera captures human performing actions. In this paper, we propose a spatio-temporal silhouette representation, called silhouette energy image (SEI), and multiple variability action models, to characterize motion and shape properties for automatic recognition of human actions in daily life. To address the variability in the recognition of human actions, several parameters, such as anthropometry of the person, speed of the action, phase (starting and ending state of an action), camera observations (distance from camera, slanting motion, and rotation of human body), and view variations are proposed. We construct the variability (or adaptable) models based on SEI and the proposed parameters. Global motion descriptors express the spatio-temporal properties of combined energy templates (SEI and variability action models). Our construction of the optimal model for each action and view is based on the support vectors of global motion descriptions of action models. We recognize different daily human actions of different styles successfully in the indoor and outdoor environment. Our experimental results show that the proposed method of human action recognition is robust, flexible and efficient.
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paper_title: A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition

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Gait recognition has gained increasing interest from researchers, but there is still no standard evaluation method to compare the performance of different gait recognition algorithms. In this paper, a framework is proposed in an attempt to tackle this problem. The framework consists of a large gait database, a large set of well designed experiments and some evaluation metrics. There are 124 subjects in the database, and the gait data was captured from 11 views. Three variations, namely view angle, clothing and carrying condition changes, are separately considered in the database. The database is one of the largest database among the existing databases. Three sets of experiments, including a total of 363 experiments, are designed in the framework. Some metrics are proposed to evaluate gait recognition algorithms.
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paper_title: Gait recognition without subject cooperation

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The strength of gait, compared to other biometrics, is that it does not require cooperative subjects. In previous work gait recognition approaches were evaluated using a gallery set consisting of gait sequences of people under similar covariate conditions (e.g. clothing, surface, carrying, and view conditions). This evaluation procedure, however, implies that the gait data are collected in a cooperative manner so that the covariate conditions are known a priori. In this work, gait recognition approaches are evaluated without the assumption on cooperative subjects, i.e. both the gallery and the probe sets consist of a mixture of gait sequences under different and unknown covariate conditions. The results indicate that the performance of the existing approaches would drop drastically under this more realistic experimental setup. We argue that selecting the most relevant gait features that are invariant to changes in gait covariate conditions is the key to develop a gait recognition system that works without subject cooperation. To this end, Gait Entropy Image (GEnI) is proposed to perform automatic feature selection on each pair of gallery and probe gait sequences. Moreover, an Adaptive Component and Discriminant Analysis (ACDA) is formulated which seamlessly integrates our feature selection method with subspace analysis for robust recognition, and importantly is computationally much more efficient compared to the conventional Component and Discriminant Analysis. Experiments are carried out on two comprehensive benchmarking databases: the CASIA database and the Southampton Human ID at a distance gait database (SOTON database). Our results demonstrate that the proposed approach significantly outperforms the existing techniques particularly when gait is captured with variable and unknown covariate conditions.
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paper_title: Human activity recognition using multidimensional indexing

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In this paper, we develop a novel method for view-based recognition of human action/activity from videos. By observing just a few frames, we can identify the activity that takes place in a video sequence. The basic idea of our method is that activities can be positively identified from a sparsely sampled sequence of a few body poses acquired from videos. In our approach, an activity is represented by a set of pose and velocity vectors for the major body parts (hands, legs, and torso) and stored in a set of multidimensional hash tables. We develop a theoretical foundation that shows that robust recognition of a sequence of body pose vectors can be achieved by a method of indexing and sequencing and it requires only a few pose vectors (i.e., sampled body poses in video frames). We find that the probability of false alarm drops exponentially with the increased number of sampled body poses. So, matching only a few body poses guarantees high probability for correct recognition. Our approach is parallel, i.e., all possible model activities are examined at one indexing operation. In addition, our method is robust to partial occlusion since each body part is indexed separately. We use a sequence-based voting approach to recognize the activity invariant to the activity speed.
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paper_title: Simplest representation yet for gait recognition: averaged silhouette

paper_content:

We present a robust representation for gait recognition that is compact, easy to construct, and affords efficient matching. Instead of a time series based representation comprising of a sequence of raw silhouette frames or of features extracted therein, as has been the practice, we simply align and average the silhouettes over one gait cycle. We then base recognition on the Euclidean distance between these averaged silhouette representations. We show, using the recently formulated gait challenge problem (www.gaitchallenge.org), that the improvement in execution time is 30 times while possessing recognition power that is comparable to the gait baseline algorithm, which is becoming the comparison standard in gait recognition. Experiments with portions of the average silhouette representation show that recognition power is not entirely derived from upper body shape, rather the dynamics of the legs also contribute equally to recognition. However, this study does raise intriguing doubts about the need for accurate shape and dynamics representations for gait recognition.
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paper_title: Human gait recognition: Approaches, datasets and challenges

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As a new technology of biometrics, gait recognition has attracted a great deal of interest in computer vision community due to its advantage of unobtrusive recognition at a distance. In this paper, a review of this topic is presented in terms of approaches, databases and challenges. First, a comprehensive survey of recent developments on gait recognition approaches is reported, where relevant techniques are categorized into three classes for discussions. Then, more than 10 publically available datasets are analysed and compared in detail. In addition, challenges that constrain practical application of gait recognition systems are discussed. Finally, future trends are given to guide further research in this field. (6 pages)
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paper_title: Gait-based recognition of humans using continuous HMMs

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Gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. In this paper, we propose a view-based approach to recognize humans through gait. The width of the outer contour of the binarized silhouette of a walking person is chosen as the image feature. A set of stances or key frames that occur during the walk cycle of an individual is chosen. Euclidean distances of a given image from this stance set are computed and a lower-dimensional observation vector is generated. A continuous hidden Markov model (HMM) is trained using several such lower-dimensional vector sequences extracted from the video. This methodology serves to compactly capture structural and transitional features that are unique to an individual. The statistical nature of the HMM renders overall robustness to gait representation and recognition. The human identification performance of the proposed scheme is found to be quite good when tested in natural walking conditions.
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paper_title: Gait recognition based on shape and motion analysis of silhouette contours

paper_content:

This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject's silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject's shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject's back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject's leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods.
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paper_title: Time-sliced averaged motion history image for gait recognition

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In this paper, we propose a time-sliced averaged motion history image (TAMHI) alongside the histograms of oriented gradients (HOG) to generate gait signatures in a gait recognition problem. Building on the motion history image (MHI), TAMHI divides the gait cycle into several regular time windows to generate the same number of TAMHI composite images. HOG descriptors are then calculated on these composite images to obtain the gait signature. The time-slicing procedure to produce multi-composite images preserve more detailed transient information of gait cycles. Additionally, time-normalization also introduces gait length invariancy into the representation, hence, offering a better recognition rate to slight changes in walking speed.
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paper_title: The humanID gait challenge problem: data sets, performance, and analysis

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Identification of people by analysis of gait patterns extracted from video has recently become a popular research problem. However, the conditions under which the problem is "solvable" are not understood or characterized. To provide a means for measuring progress and characterizing the properties of gait recognition, we introduce the humanlD gait challenge problem. The challenge problem consists of a baseline algorithm, a set of 12 experiments, and a large data set. The baseline algorithm estimates silhouettes by background subtraction and performs recognition by temporal correlation of silhouettes. The 12 experiments are of increasing difficulty, as measured by the baseline algorithm, and examine the effects of five covariates on performance. The covariates are: change in viewing angle, change in shoe type, change in walking surface, carrying or not carrying a briefcase, and elapsed time between sequences being compared. Identification rates for the 12 experiments range from 78 percent on the easiest experiment to 3 percent on the hardest. All five covariates had statistically significant effects on performance, with walking surface and time difference having the greatest impact. The data set consists of 1,870 sequences from 122 subjects spanning five covariates (1.2 gigabytes of data). This infrastructure supports further development of gait recognition algorithms and additional experiments to understand the strengths and weaknesses of new algorithms. The experimental results are presented, the more detailed is the possible meta-analysis and greater is the understanding. It is this potential from the adoption of this challenge problem that represents a radical departure from traditional computer vision research methodology.
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paper_title: Recognition of human actions using motion history information extracted from the compressed video

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Human motion analysis is a recent topic of interest among the computer vision and video processing community. Research in this area is motivated by its wide range of applications such as surveillance and monitoring systems. In this paper we describe a system for recognition of various human actions from compressed video based on motion history information. We introduce the notion of quantifying the motion involved, through what we call Motion Flow History (MFH). The encoded motion information readily available in the compressed MPEG stream is used to construct the coarse Motion History Image (MHI) and the corresponding MFH. The features extracted from the static MHI and MFH compactly characterize the spatio-temporal and motion vector information of the action. Since the features are extracted from the partially decoded sparse motion data, the computational load is minimized to a great extent. The extracted features are used to train the KNN, Neural network, SVM and the Bayes classifiers for recognizing a set of seven human actions. The performance of each feature set with respect to various classifiers are analyzed. q 2003 Elsevier B.V. All rights reserved.
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paper_title: Visual Motion Perception

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In this article the author uses projective relations as the theoretical foundation of his investigations of visual space and motion. Several laboratory experiments involving perceptual vector analysis and its geometric basis are described. In most of the experiments the visual stimuli consisted of computer-controlled patterns displayed on a televisionlike screen and projected into the eyes of subjects by means of a collimating device that removed parallax as well as the possibility of seeing the screen. A common characteristics of the experiments was that the observer was evidently not free to choose between a Euclidean interpretation of the changing geometry of the figure in the display and a projective interpretation. For example, the observer could not persuade himself that what he was seeing was simply a square growing larger and smaller in the same visual plane; his visual system insisted on telling him that he was seeing a square of constant size approaching and receding. Hence he perceived rigid motion in depth, rotation in a specific slant, bending in depth and so on, paired with the highest possible degree of object constancy. Further experiments were conducted to determine if the principles of perceptual analysis hold true for the more complex paterns of motions encountered in everyday life. These experiments led to the conclusion that during locomotion the components of the human visual environment are interpreted as rigid structures in relative motion.
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paper_title: Analyzing and recognizing walking figures in XYT

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We describe a novel algorithm for gait analysis. A person walking frontoparallel to the image plane generates a characteristic "braided" pattern in a spatiotemporal (XYT) volume. Our algorithm detects this pattern, and fits it with a set of spatiotemporal snakes. The snakes can be used to find the bounding contours of the walker. The contours vary over time in a manner characteristic of each walker. Individual gaits can be recognized by applying standard pattern recognition techniques to the contour signals. >
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paper_title: Integrated face and gait recognition from multiple views

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We develop a view-normalization approach to multi-view face and gait recognition. An image-based visual hull (IBVH) is computed from a set of monocular views and used to render virtual views for tracking and recognition. We determine canonical viewpoints by examining the 3D structure, appearance (texture), and motion of the moving person. For optimal face recognition, we place virtual cameras to capture frontal face appearance; for gait recognition we place virtual cameras to capture a side-view of the person. Multiple cameras can be rendered simultaneously, and camera position is dynamically updated as the person moves through the workspace. Image sequences from each canonical view are passed to an unmodified face or gait recognition algorithm. We show that our approach provides greater recognition accuracy than is obtained using the unnormalized input sequences, and that integrated face and gait recognition provides improved performance over either modality alone. Canonical view estimation, rendering, and recognition have been efficiently implemented and can run at near real-time speeds.
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paper_title: A comprehensive review of past and present vision-based techniques for gait recognition

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Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available--for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance.
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paper_title: Individual recognition using gait energy image

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In this paper, we propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we also propose a novel approach for human recognition by combining statistical gait features from real and synthetic templates. We directly compute the real templates from training silhouette sequences, while we generate the synthetic templates from training sequences by simulating silhouette distortion. We use a statistical approach for learning effective features from real and synthetic templates. We compare the proposed GEI-based gait recognition approach with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches
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paper_title: Support vector regression for multi-view gait recognition based on local motion feature selection

paper_content:

Gait is a well recognized biometric feature that is used to identify a human at a distance. However, in real environment, appearance changes of individuals due to viewing angle changes cause many difficulties for gait recognition. This paper re-formulates this problem as a regression problem. A novel solution is proposed to create a View Transformation Model (VTM) from the different point of view using Support Vector Regression (SVR). To facilitate the process of regression, a new method is proposed to seek local Region of Interest (ROI) under one viewing angle for predicting the corresponding motion information under another viewing angle. Thus, the well constructed VTM is able to transfer gait information under one viewing angle into another viewing angle. This proposal can achieve view-independent gait recognition. It normalizes gait features under various viewing angles into a common viewing angle before similarity measurement is carried out. The extensive experimental results based on widely adopted benchmark dataset demonstrate that the proposed algorithm can achieve significantly better performance than the existing methods in literature.
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paper_title: The Recognition of Human Movement Using Temporal Templates

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A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms.
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paper_title: Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron

paper_content:

Highlights? One-view to one-view VTM solves across-view gait recognition. ?Multi-view to one-view VTM solves multi-view gait recognition. ?VTM is built up from regressions of local motion using multi-layer perceptron. ?Multi-view to one-view VTM can be claimed as implicit 3D gait model based method. ?Accuracy of 98% is achieved for multi-view gait recognition using 4 cameras. Gait has been shown to be an efficient biometric feature for human identification at a distance. However, performance of gait recognition can be affected by view variation. This leads to a consequent difficulty of cross-view gait recognition. A novel method is proposed to solve the above difficulty by using view transformation model (VTM). VTM is constructed based on regression processes by adopting multi-layer perceptron (MLP) as a regression tool. VTM estimates gait feature from one view using a well selected region of interest (ROI) on gait feature from another view. Thus, trained VTMs can normalize gait features from across views into the same view before gait similarity is measured. Moreover, this paper proposes a new multi-view gait recognition which estimates gait feature on one view using selected gait features from several other views. Extensive experimental results demonstrate that the proposed method significantly outperforms other baseline methods in literature for both cross-view and multi-view gait recognitions. In our experiments, particularly, average accuracies of 99%, 98% and 93% are achieved for multiple views gait recognition by using 5 cameras, 4 cameras and 3 cameras respectively.
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paper_title: A Review of Vision-Based Gait Recognition Methods for Human Identification

paper_content:

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field.
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paper_title: Model-Based Human Gait Recognition Via Deterministic Learning

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In this paper, we present a new model-based approach for human gait recognition in the sagittal plane via deterministic learning (DL) theory. Side silhouette lower limb joint angles characterize the gait system dynamics and are selected as the gait feature. Locally accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks through DL. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L 1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Therefore, the test gait pattern can be rapidly recognized according to the smallest error principle. In contrast to other existing approaches, the main focus of this paper is on obtaining and reusing the knowledge of the gait system dynamics. Finally, experiments are carried out on the CASIA-A and CASIA-B gait databases to benchmark the effectiveness of the proposed approach.
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paper_title: Variability and Similarity of Gait as Evaluated by Joint Angles: Implications for Forensic Gait Analysis

paper_content:

Closed-circuit television (CCTV) footage is used in criminal investigations to compare perpetrators with suspects. Usually, incomplete gait cycles are collected, making evidential gait analysis challenging. This study aimed to analyze the discriminatory power of joint angles throughout a gait cycle. Six sets from 12 men were collected. For each man, a variability range VR (mean ± 1SD) of a specific joint angle at a specific time point (a gait cycle was 100 time points) was calculated. In turn, each individual was compared with the 11 others, and whenever 1 of these 11 had a value within this individual's VR, it counted as positive. By adding the positives throughout the gait cycle, we created simple bar graphs; tall bars indicated a small discriminatory power, short bars indicated a larger one. The highest discriminatory power was at time points 60–80 in the gait cycle. We show how our data can assess gait data from an actual case.
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paper_title: Human Gait Recognition: A Silhouette Based Approach

paper_content:

Human gait has become an important biometric in recent years. A silhouette based method is suggested in this paper, to recognize human in video by their gait. We used averaged silhouette to represent the gait cycle. Principal Component Analysis has been used to reduce the dimensionality of the features. We applied Euclidean distance to measure the similarity of the averaged silhouettes. We implemented the algorithm on the TUM- IITKGP Gait Database which has been introduced recently. Although this method is sensitive to the appearance of the subject, it has low computational cost and it is simple. We implemented two experiments on the achieved averaged silhouettes. Our results illustrated that feature extracted from the averaged silhouettes which in them, the lower part of the body is eliminated are more suitable rather than those extracted from the complete averaged silhouettes. 
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paper_title: Automated person recognition by walking and running via model-based approaches

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Gait enjoys advantages over other biometrics in that it can be perceived from a distance and is di cult to disguise.Current approaches are mostly statistical and concentrate on walking only.By analysing leg motion we show how we can recognise people not only by the walking gait,but also by the running gait.This is achieved by either of two new modelling approaches which employ coupled oscillators and the biomechanics of human locomotion as the underlying concepts.These models give a plausible method for data reduction by providing estimates of the inclination of the thigh and of the leg,from the image data. Both approaches derive a phase-weighted Fourier description gait signature by automated non-invasive means.One approach is completely automated whereas the other requires speci cation of a single parameter to distinguish between walking and running.Results show that both gaits are potential biometrics,with running being more potent.By its basis in evidence gathering,this new technique can tolerate noise and low resolution.
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paper_title: Feature subset selection applied to model-free gait recognition

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In this paper, we tackle the problem of gait recognition based on the model-free approach. Numerous methods exist; they all lead to high dimensional feature spaces. To address the problem of high dimensional feature space, we propose the use of the Random Forest algorithm to rank features' importance. In order to efficiently search throughout subspaces, we apply a backward feature elimination search strategy. Our first experiments are carried out on unknown covariate conditions. Our first results suggest that the selected features contribute to increase the CCR of different existing classification methods. Secondary experiments are performed on unknown covariate conditions and viewpoints. Inspired by the location of our first experiments' features, we proposed a simple mask. Experimental results demonstrate that the proposed mask gives satisfactory results for all angles of the probe and consequently is not view specific. We also show that our mask performs well when an uncooperative experimental setup is considered as compared to the state-of-the art methods. As a consequence, we propose a panoramic gait recognition framework on unknown covariate conditions. Our results suggest that panoramic gait recognition can be performed under unknown covariate conditions. Our approach can greatly reduce the complexity of the classification problem while achieving fair correct classification rates when gait is captured with unknown conditions.
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paper_title: Gait recognition from time-normalized joint-angle trajectories in the walking plane

paper_content:

This paper demonstrates gait recognition using only the trajectories of lower body joint angles projected into the walking plane. For this work, we begin with the position of 3D markers as projected into the sagittal or walking plane. We show a simple method for estimating the planar offsets between the markers and the underlying skeleton and joints; given these offsets we compute the joint angle trajectories. To compensate for systematic temporal variations from one instance to the next-predominantly distance and speed of walk-we fix the number of footsteps and time-normalize the trajectories by a variance compensated time warping. We perform recognition on two walking databases of 18 people (over 150 walk instances) using simple nearest neighbor algorithm with Euclidean distance as a measurement criteria. We also use the expected confusion metric as a means to estimate how well joint-angle signals will perform in a larger population.
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paper_title: Robust view transformation model for gait recognition

paper_content:

Recent gait recognition systems often suffer from the challenges including viewing angle variation and large intra-class variations. In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method establishes a robust view transformation model via robust principal component analysis. Partial least square is used as feature selection method. Compared with the existing methods, the proposed method finds out a shared linear correlated low rank subspace, which brings the advantages that the view transformation model is robust to viewing angle variation, clothing and carrying condition changes. Conducted on the CASIA gait dataset, experimental results show that the proposed method outperforms the other existing methods.
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paper_title: Review on Pedestrian Gait Feature Expression and Recognition

paper_content:

Various methods for gait recognition are summarized by unique metods from anthropometry,spatial temporal,kinematics,kinetics,and video stream data forms.And video stream data are analyzed in detail.The expressions,meanings and characteristics including static,dynamic,and time-varying information among different energy image species methods are compared.Moreover,the fusion of gait features,gait and other biometric and extended gait recognition are reviewed.The assessment methods for gait recognition research are also presented.In addition,the future research directions of gait recognition are addressed.
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paper_title: Gait Analysis for Recognition and Classification

paper_content:

We describe a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple features such as moments extracted from orthogonal view video silhouettes of human walking motion. Despite its simplicity, the resulting feature vector contains enough information to perform well on human identification and gender classification tasks. We explore the recognition behaviors of two different methods to aggregate features over time under different recognition tasks. We demonstrate the accuracy of recognition using gait video sequences collected over different days and times and under varying lighting environments. In addition, we show results for gender classification based our gait appearance features using a support-vector machine.
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paper_title: Gait Recognition With Shifted Energy Image and Structural Feature Extraction

paper_content:

In this paper, we present a novel and efficient gait recognition system. The proposed system uses two novel gait representations, i.e., the shifted energy image and the gait structural profile, which have increased robustness to some classes of structural variations. Furthermore, we introduce a novel method for the simulation of walking conditions and the generation of artificial subjects that are used for the application of linear discriminant analysis. In the decision stage, the two representations are fused. Thorough experimental evaluation, conducted using one traditional and two new databases, demonstrates the advantages of the proposed system in comparison with current state-of-the-art systems.
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paper_title: Gait Recognition Under Various Viewing Angles Based on Correlated Motion Regression

paper_content:

It is well recognized that gait is an important biometric feature to identify a person at a distance, e.g., in video surveillance application. However, in reality, change of viewing angle causes significant challenge for gait recognition. A novel approach using regression-based view transformation model (VTM) is proposed to address this challenge. Gait features from across views can be normalized into a common view using learned VTM(s). In principle, a VTM is used to transform gait feature from one viewing angle (source) into another viewing angle (target). It consists of multiple regression processes to explore correlated walking motions, which are encoded in gait features, between source and target views. In the learning processes, sparse regression based on the elastic net is adopted as the regression function, which is free from the problem of overfitting and results in more stable regression models for VTM construction. Based on widely adopted gait database, experimental results show that the proposed method significantly improves upon existing VTM-based methods and outperforms most other baseline methods reported in the literature. Several practical scenarios of applying the proposed method for gait recognition under various views are also discussed in this paper.
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paper_title: Gait recognition using static, activity-specific parameters

paper_content:

A gait-recognition technique that recovers static body and stride parameters of subjects as they walk is presented. This approach is an example of an activity-specific biometric: a method of extracting identifying properties of an individual or of an individual's behavior that is applicable only when a person is performing that specific action. To evaluate our parameters, we derive an expected confusion metric (related to mutual information), as opposed to reporting a percent correct with a limited database. This metric predicts how well a given feature vector will filter identity in a large population. We test the utility of a variety of body and stride parameters recovered in different viewing conditions on a database consisting of 15 to 20 subjects walking at both an angled and frontal-parallel view with respect to the camera, both indoors and out. We also analyze motion-capture data of the subjects to discover whether confusion in the parameters is inherently a physical or a visual measurement error property.
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paper_title: Model-based human gait recognition using leg and arm movements

paper_content:

We have presented a model-based approach for human gait recognition, which is based on analyzing the leg and arm movements. An initial model is created based on anatomical proportions, and a posterior model is constructed upon the movements of the articulated parts of the body, using active contour models and the Hough transform. Fourier analysis is used to describe the motion patterns of the moving parts. The k-nearest neighbor rule applied to the phase-weighted Fourier magnitude of each segment's spectrum is used for classification. In contrast to the existing approaches, the main focus of this paper is on increasing the discrimination capability of the model through extra features produced from the motion of the arms. Experimental results indicate good performance of the proposed method. The technique has also proved to be able to reduce the adverse effects of self-occlusion, which is a common incident in human walking.
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paper_title: Moment Gait Energy Image Based Human Recognition at a Distance

paper_content:

A moment gait energy image(MGEI)based gait recognition algorithm is presented.Gait period is estimated from swing distances,and key moments of a gait cycle are specified.The mean of all the silhouette images at a key moment is called the mean moment image.The MGEI at each key moment is calculated from all the mean moment images in gait period.It improves sil- houette's quality by gait probability distribution at each key moment,and provides more motion features than the basic gait energy image(GEI).Then a method of gait feature extraction is proposed based on MGEI.The deviations of silhouette images from MGEI at every key moment are cumulated into an image,which is utilized together with GEI to represent a subject.The nearest neighbor classifier is adopted to recognize subjects.The proposed algorithm is evaluated on USF dataset,and the performance is compared with the baseline algorithm and two other new algorithms.Experimental result shows that this algorithm achieves higher overall recognition rate than the other algorithms.
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paper_title: General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

paper_content:

Traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis (LDA) because of the undersample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two-dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA, compared with existing preprocessing methods such as the principal components analysis (PCA) and 2DLDA, include the following: 1) the USP is reduced in subsequent classification by, for example, LDA, 2) the discriminative information in the training tensors is preserved, and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, whereas that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor-function-based image decompositions for image understanding and object recognition, we develop three different Gabor-function-based image representations: 1) GaborD is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS, and GaborSD representations are applied to the problem of recognizing people from their averaged gait images. A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS, or GaborSD image representation, then using GDTA to extract features and, finally, using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database. Experimental comparisons are made with nine state-of-the-art classification methods in gait recognition.
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paper_title: Simplest representation yet for gait recognition: averaged silhouette

paper_content:

We present a robust representation for gait recognition that is compact, easy to construct, and affords efficient matching. Instead of a time series based representation comprising of a sequence of raw silhouette frames or of features extracted therein, as has been the practice, we simply align and average the silhouettes over one gait cycle. We then base recognition on the Euclidean distance between these averaged silhouette representations. We show, using the recently formulated gait challenge problem (www.gaitchallenge.org), that the improvement in execution time is 30 times while possessing recognition power that is comparable to the gait baseline algorithm, which is becoming the comparison standard in gait recognition. Experiments with portions of the average silhouette representation show that recognition power is not entirely derived from upper body shape, rather the dynamics of the legs also contribute equally to recognition. However, this study does raise intriguing doubts about the need for accurate shape and dynamics representations for gait recognition.
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paper_title: Recognition of human actions using motion history information extracted from the compressed video

paper_content:

Human motion analysis is a recent topic of interest among the computer vision and video processing community. Research in this area is motivated by its wide range of applications such as surveillance and monitoring systems. In this paper we describe a system for recognition of various human actions from compressed video based on motion history information. We introduce the notion of quantifying the motion involved, through what we call Motion Flow History (MFH). The encoded motion information readily available in the compressed MPEG stream is used to construct the coarse Motion History Image (MHI) and the corresponding MFH. The features extracted from the static MHI and MFH compactly characterize the spatio-temporal and motion vector information of the action. Since the features are extracted from the partially decoded sparse motion data, the computational load is minimized to a great extent. The extracted features are used to train the KNN, Neural network, SVM and the Bayes classifiers for recognizing a set of seven human actions. The performance of each feature set with respect to various classifiers are analyzed. q 2003 Elsevier B.V. All rights reserved.
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paper_title: Individual recognition using gait energy image

paper_content:

In this paper, we propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we also propose a novel approach for human recognition by combining statistical gait features from real and synthetic templates. We directly compute the real templates from training silhouette sequences, while we generate the synthetic templates from training sequences by simulating silhouette distortion. We use a statistical approach for learning effective features from real and synthetic templates. We compare the proposed GEI-based gait recognition approach with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches
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paper_title: The Recognition of Human Movement Using Temporal Templates

paper_content:

A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms.
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paper_title: Statistical feature fusion for gait-based human recognition

paper_content:

This paper presents a novel approach for human recognition by combining statistical gait features from real and synthetic templates. Real templates are directly computed from training silhouette sequences, while synthetic templates are generated from training sequences by simulating silhouette distortion. A statistical feature extraction approach is used for learning effective features from real and synthetic templates. Features learned from real templates characterize human walking properties provided in training sequences, and features learned from synthetic templates predict gait properties under other conditions. A feature fusion strategy is therefore applied at the decision level to improve recognition performance. We apply the proposed approach to USF HumanID Database. Experimental results demonstrate that the proposed fusion approach not only achieves better performance than individual approaches, but also provides large improvement in performance with respect to the baseline algorithm.
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paper_title: Recognizing Humans Based on Gait Moment Image

paper_content:

This paper utilizes the periodicity of swing distances to estimate gait period. It shows good adaptability to low quality silhouette images. Gait moment image (GMI) is implemented based on the estimated gait period. GMI is the gait probability image at each key moment in gait period. It reduces the noise of the silhouettes extracted from low quality videos by gait probability distribution at each key moment. Moment deviation image (MDI) is generated by using silhouette images and GMIs. As a good complement of gait energy image (GEI), MDI provides more motion features than the basic GEI. MDI is utilized together with GEI to represent a subject. The nearest neighbor classifier is adopted to recognize subjects. The proposed algorithm is evaluated on the USF gait database, and the performance is compared with the baseline algorithm and two other algorithms. Experimental results show that this algorithm achieves a higher total recognition rate than the other algorithms.
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paper_title: Gait Recognition Based on Sparse Representation

paper_content:

A method based on sparse representation for gait recognition was proposed using the CASIA-B and CUSD database.The gait silhouette was centralized and normalized first,then the AEI(active energy image) was calculated based on the previous operation and used as the feature image for gait recognition.Two methods were used to establish the dictionary and calculate the decomposition coefficients for the sparse representation: one for reconstruction,and the other for discrimination.OMP(orthogonal matching pursuit) algorithm was used for coefficients decomposition.The result of experiment shows that the proposed method can effectively recognize the gait,and the accuracy of the recognition is high and recognizing speed is much faster.
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paper_title: Gait History Image: A Novel Temporal Template for Gait Recognition

paper_content:

In this paper, an improved temporal template called gait history image (GHI) is proposed for human gait representation and recognition. Comparing with other temporal template methods, GHI models gait more comprehensively: Static and dynamic characteristics, as well as spatial and temporal variations, can be represented. The time duration of the GHI template is controlled by a finer period resolution (1/4 of a gait cycle). We use statistical approach to learn the discriminating features from GHIs, and gait recognition experiments were performed on the CASIA and USF gait databases. The methods using GHI template presented better recognition performances than the baseline algorithm and gait energy image (GEI) method.
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paper_title: A new representation for human gait recognition : Motion silhouettes image (MSI)

paper_content:

Recently, gait recognition for human identification has received substantial attention from biometrics researchers. Compared with other biometrics, it is more difficult to disguise. In addition, gait can be captured in a distance by using low-resolution capturing devices. In this paper, we proposed a new representation for human gait recognition which is called Motion Silhouettes Image (MSI). MSI is a grey-level image which embeds the critical spatio-temporal information. Experiments showed that MSI has a high discriminative power for gait recognition. The recognition rate is around 87% in SOTON dataset by using MSI for recognition. The recognition rate is quite promising. In addition, MSI can also reduce the storage size of the dataset. After using MSI, the storage size of SOTON has reduced to 4.2MB.
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paper_title: General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

paper_content:

Traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis (LDA) because of the undersample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two-dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA, compared with existing preprocessing methods such as the principal components analysis (PCA) and 2DLDA, include the following: 1) the USP is reduced in subsequent classification by, for example, LDA, 2) the discriminative information in the training tensors is preserved, and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, whereas that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor-function-based image decompositions for image understanding and object recognition, we develop three different Gabor-function-based image representations: 1) GaborD is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS, and GaborSD representations are applied to the problem of recognizing people from their averaged gait images. A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS, or GaborSD image representation, then using GDTA to extract features and, finally, using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database. Experimental comparisons are made with nine state-of-the-art classification methods in gait recognition.
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paper_title: Chrono-gait image: a novel temporal template for gait recognition

paper_content:

In this paper, we propose a novel temporal template, called Chrono-Gait Image (CGI), to describe the spatio-temporal walking pattern for human identification by gait. The CGI temporal template encodes the temporal information among gait frames via color mapping to improve the recognition performance. Our method starts with the extraction of the contour in each gait image, followed by utilizing a color mapping function to encode each of gait contour images in the same gait sequence and compositing them to a single CGI. We also obtain the CGI-based real templates by generating CGI for each period of one gait sequence and utilize contour distortion to generate the CGI-based synthetic templates. In addition to independent recognition using either of individual templates, we combine the real and synthetic temporal templates for refining the performance of human recognition. Extensive experiments on the USF HumanID database indicate that compared with the recently published gait recognition approaches, our CGI-based approach attains better performance in gait recognition with considerable robustness to gait period detection.
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paper_title: Gait recognition without subject cooperation

paper_content:

The strength of gait, compared to other biometrics, is that it does not require cooperative subjects. In previous work gait recognition approaches were evaluated using a gallery set consisting of gait sequences of people under similar covariate conditions (e.g. clothing, surface, carrying, and view conditions). This evaluation procedure, however, implies that the gait data are collected in a cooperative manner so that the covariate conditions are known a priori. In this work, gait recognition approaches are evaluated without the assumption on cooperative subjects, i.e. both the gallery and the probe sets consist of a mixture of gait sequences under different and unknown covariate conditions. The results indicate that the performance of the existing approaches would drop drastically under this more realistic experimental setup. We argue that selecting the most relevant gait features that are invariant to changes in gait covariate conditions is the key to develop a gait recognition system that works without subject cooperation. To this end, Gait Entropy Image (GEnI) is proposed to perform automatic feature selection on each pair of gallery and probe gait sequences. Moreover, an Adaptive Component and Discriminant Analysis (ACDA) is formulated which seamlessly integrates our feature selection method with subspace analysis for robust recognition, and importantly is computationally much more efficient compared to the conventional Component and Discriminant Analysis. Experiments are carried out on two comprehensive benchmarking databases: the CASIA database and the Southampton Human ID at a distance gait database (SOTON database). Our results demonstrate that the proposed approach significantly outperforms the existing techniques particularly when gait is captured with variable and unknown covariate conditions.
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paper_title: Multiple HOG templates for gait recognition

paper_content:

In gait recognition field, template-based approaches such as Gait Energy Image (GEI) and Chrono-Gait Image (CGI) can achieve good recognition performance with low computational cost. Meanwhile, CGI can preserve temporal information better than GEI. However, they pay less attention to the local shape features. To preserve temporal information and generate more abundant local shape features, we generate multiple HOG templates by extracting Histogram of Oriented Gradients (HOG) of GEI and CGI templates. Experiments show that compared with several published approaches, our proposed multiple HOG templates achieve better performance for gait recognition.
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paper_title: Gait Recognition Using Gait Entropy Image

paper_content:

Gait as a behavioural biometric is concerned with how people walk. However, most existing gait representations capture both motion and appearance information. They are thus sensitive to changes in various covariate conditions such as carrying and clothing. In this paper, a novel gait representation termed as Gait Entropy Image (GEnI) is proposed. Based on computing entropy, a GEnI encodes in a single image the randomness of pixel values in the silhouette images over a complete gait cycle. It thus captures mostly motion information and is robust to covariate condition changes that affect appearance. Extensive experiments on the USF HumanID dataset, CASIA dataset and the SOTON dataset have been carried out to demonstrate that the proposed gait representation outperforms existing methods, especially when there are significant appearance changes. Our experiments also show clear advantage of GEnI over the alternatives without the assumption on cooperative subjects, i.e. both the gallery and the probe sets consist of a mixture of gait sequences under different and unknown covariate conditions. (6 pages)
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paper_title: Personal identification combining modified gait flow image and view

paper_content:

A method combined the modified Lucas-Kanade Gait Flow Image(LK-GFI) with the view was proposed to solve the problem that personal identification based on a gait is sensitive to view change.The Lacus-Kanade optical method was used to compute the optical flow between two silhouettes to construct LK-GFI,and the view was obtained according to the walking direction of the person.The LK-GFI database for the target at different views was established,then the new person's view and LK-GFI were extracted.At last,the similarity between the new person's LK-GFI and the target's LK-GFI at the same view was computed by the Euclidian distance method.The performance of this method was evaluated on the data in the CASIA database and the data obtained in indoor lab environment,and the False Rejection Rate(FRR) is 7.95% and 9.12% respectively.It is reduced by 12.5% and 14.45% respectively compared with that of the Gait Energy Image(GEI),and by 7.77% and 6.74% respectively compared with that of the Gait Flow Image(GFI).The proposed method has high recognition accuracy,strong real-time and the robustness to view changes.
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paper_title: Human Identification Using Temporal Information Preserving Gait Template

paper_content:

Gait Energy Image (GEI) is an efficient template for human identification by gait. However, such a template loses temporal information in a gait sequence, which is critical to the performance of gait recognition. To address this issue, we develop a novel temporal template, named Chrono-Gait Image (CGI), in this paper. The proposed CGI template first extracts the contour in each gait frame, followed by encoding each of the gait contour images in the same gait sequence with a multichannel mapping function and compositing them to a single CGI. To make the templates robust to a complex surrounding environment, we also propose CGI-based real and synthetic temporal information preserving templates by using different gait periods and contour distortion techniques. Extensive experiments on three benchmark gait databases indicate that, compared with the recently published gait recognition approaches, our CGI-based temporal information preserving approach achieves competitive performance in gait recognition with robustness and efficiency.
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paper_title: Frame difference energy image for gait recognition with incomplete silhouettes

paper_content:

The quality of human silhouettes has a direct effect on gait recognition performance. This paper proposes a robust dynamic gait representation scheme, frame difference energy image (FDEI), to suppress the influence of silhouette incompleteness. A gait cycle is first divided into clusters. The average image of each cluster is denoised and becomes the dominant energy image (DEI). FDEI representation of a frame is constructed by adding the corresponding cluster's DEI and the positive portion of the frame difference between the former frame and the current frame. FDEI representation can preserve the kinetic and static information of each frame, even when the silhouettes are incomplete. This proposed representation scheme is tested on the CMU Mobo gait database with synthesized occlusions and the CASIA gait database (dataset B). The frieze and wavelet features are adopted and hidden Markov model (HMM) is employed for recognition. Experimental results show the superiority of FDEI representation over binary silhouettes and some other algorithms when occlusion or body portion lost appears in the gait sequences.
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paper_title: Gait flow image: A silhouette-based gait representation for human identification

paper_content:

In this paper, we propose a novel gait representation-gait flow image (GFI) for use in gait recognition. This representation will further improve recognition rates. The basis of GFI is the binary silhouette sequence. GFI is generated by using an optical flow field without constructing any model. The performance of the proposed representation was evaluated and compared with the other representations, such as gait energy image (GEI), experimentally on the USF data set. The USF data set is a public data set in which the image sequences were captured outdoors. The experimental results show that the proposed representation is efficient for human identification. The average recognition rate of GFI is better than that of the other representations in direct matching and dimensional reduction approaches. In the direct matching approach, GFI achieved an average identification rate 42.83%, which is better than GEI by 3.75%. In the dimensional reduction approach, GFI achieved an average identification rate 43.08%, which is better than GEI by 1.5%. The experimental result showed that GFI is stronger in resisting the difference of the carrying condition compared with other gait representations.
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paper_title: Gait recognition based on improved dynamic Bayesian networks

paper_content:

In this paper, we proposed an improved two-level dynamic Bayesian network layered time series model (LTSM), which aims to solve the limitations hindering the application of available dynamic Bayesian networks, the hidden Markov model (HMM) and the dynamic texture (DT) model to gait recognition. In the first level, a gait silhouette or feature cycle is divided into several temporally adjacent clusters. Each cluster is modeled by a DT or logistic DT (LDT). In the second level, HMM is built to describe the relationship among the DTs/LDTs. Besides LTSM, LDT is also an improved dynamic Bayesian network presented in this paper to describe the binary image sequence, which introduces the logistic principle component analysis (PCA) to learning its parameters. We demonstrated the validity of LTSM with experiments on both the CMU Mobo gait database and CASIA gait database (dataset B), and that of LDT on the CMU Mobo gait database. Experimental results showed the superiority of the improved dynamic Bayesian networks.
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paper_title: Gabor-Based DCV Gait Recognition Using Enhanced Gait Energy Image

paper_content:

This paper presented a novel approach for gait recognition by analyzing the dynamics of walking patterns.Inspired by intuitive inference,a dynamic weight mask(DWM) is created by the variance of averaged gait energy images(GEIs) representing each class.A dynamics and shape enhanced gait representation called EGEI is obtained from DWM.To increase discriminative information an ensemble of Gabor kernels is convolved with EGEIs.Gabor gait is represented in the lower dimensional space using discriminative common vectors analysis.The proposed algorithm was tested on the USF HumanID Database.The experimental results demonstrate that each step in the proposed algorithm is effective and the recognition performance is improved.
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paper_title: Factorial HMM and Parallel HMM for Gait Recognition

paper_content:

Information fusion offers a promising solution to the development of a high-performance classification system. In this paper, the problem of multiple gait features fusion is explored with the framework of the factorial hidden Markov model (FHMM). The FHMM has a multiple-layer structure and provides an alternative to combine several gait features without concatenating them into a single augmented feature. Besides, the feature concatenation is used to directly concatenate the features and the parallel HMM (PHMM) is introduced as a decision-level fusion scheme, which employs traditional fusion rules to combine the recognition results at decision level. To evaluate the recognition performances, McNemar's test is employed to compare the FHMM feature-level fusion scheme with the feature concatenation and the PHMM decision-level fusion scheme. Statistical numerical experiments are carried out on the Carnegie Mellon University motion of body and the Institute of Automation of the Chinese Academy of Sciences gait databases. The experimental results demonstrate that the FHMM feature-level fusion scheme and the PHMM decision-level fusion scheme outperform feature concatenation. The FHMM feature-level fusion scheme tends to perform better than the PHMM decision-level fusion scheme when only a few gait cycles are available for recognition.
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paper_title: View-independent human motion classification using image-based reconstruction

paper_content:

We introduce in this paper a novel method for employing image-based rendering to extend the range of applicability of human motion and gait recognition systems. Much work has been done in the field of human motion and gait recognition, and many interesting methods for detecting and classifying motion have been developed. However, systems that can robustly recognize human behavior in real-world contexts have yet to be developed. A significant reason for this is that the activities of humans in typical settings are unconstrained in terms of the motion path. People are free to move throughout the area of interest in any direction they like. While there have been many good classification systems developed in this domain, the majority of these systems have used a single camera providing input to a training-based learning method. Methods that rely on a single camera are implicitly view-dependent. In practice, the classification accuracy of these systems often becomes increasingly poor as the angle between the camera and the direction of motion varies away from the training view angle. As a result, these methods have limited real-world applications, since it is often impossible to limit the direction of motion of people so rigidly. We demonstrate the use of image-based rendering to adapt the input to meet the needs of the classifier by automatically constructing the proper view (image), that matches the training view, from a combination of arbitrary views taken from several cameras. We tested the method on 162 sequences of video data of human motions taken indoors and outdoors, and promising results were obtained.
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paper_title: Human gait recognition based on X-T plane energy images

paper_content:

In this paper, we propose a novel algorithm for gait recognition. Binarized silhouette of a motion object is first segmented from color image, and then, spatio-temporal (XYT) volume is constructed by using these binarized silhouettes, and cut at knee and hip height. Next, energy images are extracted by projecting these three individual XYT volumes onto X-T plane, respectively. Fourier transform is employed as a processing step to achieve translation invariant for the silhouette sequences which are captured from the subjects walk in different speed. Then three frequency-domain feature vectors are fused. AdaBoost is used to select a small set of critical features from all of the features. Nearest neighbor and support vector machine (SVM) classifier are finally executed to produce final decision, respectively. The experiments are carried on one of the largest public gait database: the CASIA database. The experimental results show that the proposed algorithm is efficient for human gait recognition, and achieves competitive performance.
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paper_title: Biometric Recognition using Feature Selection and Combination

paper_content:

Most of the prior work in biometric literature has only emphasized on the issue of feature extraction and classification. However, the critical issue of examining the usefulness of extracted biometric features has been largely ignored. Feature evaluation/selection helps to identify and remove much of the irrelevant and redundant features. The small dimension of feature set reduces the hypothesis space, which is critical for the success of online implementation in personal recognition. This paper focuses on the issue of feature subset selection and its effectiveness in a typical bimodal biometric system. The feature level fusion has not received adequate attention in the literature and therefore the performance improvement in feature level fusion using feature subset selection is also investigated. Our experimental results demonstrate that while majority of biometric features are useful in predicting the subjects identity, only a small subset of these features are necessary in practice for building an accurate model for identification. The comparison and combination of features extracted from hand images is evaluated on the diverse classification schemes; naive Bayes (normal, estimated, multinomial), decision trees (C4.5, LMT), k-NN, SVM, and FFN.
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paper_title: Informative Shape Representations for Human Action Recognition

paper_content:

Shape and kinematics are two important cues in human movement analysis. Due to real difficulties in extracting kinematics from videos accurately, this paper proposes to address the problem of human action recognition by spatiotemporal shape analysis. Without explicit feature tracking and complex probabilistic modeling of human movements, we directly convert an associated sequence of human silhouettes derived from videos into two types of computationally efficient representations, i.e., average motion energy and mean motion shape, to characterize actions. Supervised pattern classification techniques using various distance measures are used for recognition. The encouraging experimental results are obtained on a recent dataset including 10 different actions from 9 subjects.
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paper_title: Human Identification by Using the Motion and Static Characteristic of Gait

paper_content:

In this paper, we propose a gait recognition algorithm that fuses motion and static spatio-temporal templates of sequences of silhouette images, the Motion Silhouette Contour Templates (MSCTs) and Static Silhouette Templates (SSTs). The performance of the proposed algorithm is evaluated experimentally using the SOTON dataset and the USF dataset. The proposed algorithm has a recognition rate of around 80% in intrinsic difference group (Probe A-C) of USF dataset which is 14% higher than the baseline algorithm.
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paper_title: Human gait recognition by the fusion of motion and static spatio-temporal templates

paper_content:

In this paper, we propose a gait recognition algorithm that fuses motion and static spatio-temporal templates of sequences of silhouette images, the motion silhouette contour templates (MSCTs) and static silhouette templates (SSTs). MSCTs and SSTs capture the motion and static characteristic of gait. These templates would be computed from the silhouette sequence directly. The performance of the proposed algorithm is evaluated experimentally using the SOTON data set and the USF data set. We compared our proposed algorithm with other research works on these two data sets. Experimental results show that the proposed templates are efficient for human identification in indoor and outdoor environments. The proposed algorithm has a recognition rate of around 85% on the SOTON data set. The recognition rate is around 80% in intrinsic difference group (probes A-C) of USF data set.
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paper_title: A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition

paper_content:

Gait recognition has gained increasing interest from researchers, but there is still no standard evaluation method to compare the performance of different gait recognition algorithms. In this paper, a framework is proposed in an attempt to tackle this problem. The framework consists of a large gait database, a large set of well designed experiments and some evaluation metrics. There are 124 subjects in the database, and the gait data was captured from 11 views. Three variations, namely view angle, clothing and carrying condition changes, are separately considered in the database. The database is one of the largest database among the existing databases. Three sets of experiments, including a total of 363 experiments, are designed in the framework. Some metrics are proposed to evaluate gait recognition algorithms.
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paper_title: The humanID gait challenge problem: data sets, performance, and analysis

paper_content:

Identification of people by analysis of gait patterns extracted from video has recently become a popular research problem. However, the conditions under which the problem is "solvable" are not understood or characterized. To provide a means for measuring progress and characterizing the properties of gait recognition, we introduce the humanlD gait challenge problem. The challenge problem consists of a baseline algorithm, a set of 12 experiments, and a large data set. The baseline algorithm estimates silhouettes by background subtraction and performs recognition by temporal correlation of silhouettes. The 12 experiments are of increasing difficulty, as measured by the baseline algorithm, and examine the effects of five covariates on performance. The covariates are: change in viewing angle, change in shoe type, change in walking surface, carrying or not carrying a briefcase, and elapsed time between sequences being compared. Identification rates for the 12 experiments range from 78 percent on the easiest experiment to 3 percent on the hardest. All five covariates had statistically significant effects on performance, with walking surface and time difference having the greatest impact. The data set consists of 1,870 sequences from 122 subjects spanning five covariates (1.2 gigabytes of data). This infrastructure supports further development of gait recognition algorithms and additional experiments to understand the strengths and weaknesses of new algorithms. The experimental results are presented, the more detailed is the possible meta-analysis and greater is the understanding. It is this potential from the adoption of this challenge problem that represents a radical departure from traditional computer vision research methodology.
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paper_title: The humanID gait challenge problem: data sets, performance, and analysis

paper_content:

Identification of people by analysis of gait patterns extracted from video has recently become a popular research problem. However, the conditions under which the problem is "solvable" are not understood or characterized. To provide a means for measuring progress and characterizing the properties of gait recognition, we introduce the humanlD gait challenge problem. The challenge problem consists of a baseline algorithm, a set of 12 experiments, and a large data set. The baseline algorithm estimates silhouettes by background subtraction and performs recognition by temporal correlation of silhouettes. The 12 experiments are of increasing difficulty, as measured by the baseline algorithm, and examine the effects of five covariates on performance. The covariates are: change in viewing angle, change in shoe type, change in walking surface, carrying or not carrying a briefcase, and elapsed time between sequences being compared. Identification rates for the 12 experiments range from 78 percent on the easiest experiment to 3 percent on the hardest. All five covariates had statistically significant effects on performance, with walking surface and time difference having the greatest impact. The data set consists of 1,870 sequences from 122 subjects spanning five covariates (1.2 gigabytes of data). This infrastructure supports further development of gait recognition algorithms and additional experiments to understand the strengths and weaknesses of new algorithms. The experimental results are presented, the more detailed is the possible meta-analysis and greater is the understanding. It is this potential from the adoption of this challenge problem that represents a radical departure from traditional computer vision research methodology.
---
paper_title: A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition

paper_content:

Gait recognition has gained increasing interest from researchers, but there is still no standard evaluation method to compare the performance of different gait recognition algorithms. In this paper, a framework is proposed in an attempt to tackle this problem. The framework consists of a large gait database, a large set of well designed experiments and some evaluation metrics. There are 124 subjects in the database, and the gait data was captured from 11 views. Three variations, namely view angle, clothing and carrying condition changes, are separately considered in the database. The database is one of the largest database among the existing databases. Three sets of experiments, including a total of 363 experiments, are designed in the framework. Some metrics are proposed to evaluate gait recognition algorithms.
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paper_title: General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

paper_content:

Traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis (LDA) because of the undersample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two-dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA, compared with existing preprocessing methods such as the principal components analysis (PCA) and 2DLDA, include the following: 1) the USP is reduced in subsequent classification by, for example, LDA, 2) the discriminative information in the training tensors is preserved, and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, whereas that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor-function-based image decompositions for image understanding and object recognition, we develop three different Gabor-function-based image representations: 1) GaborD is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS, and GaborSD representations are applied to the problem of recognizing people from their averaged gait images. A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS, or GaborSD image representation, then using GDTA to extract features and, finally, using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database. Experimental comparisons are made with nine state-of-the-art classification methods in gait recognition.
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paper_title: Histograms of oriented gradients for human detection

paper_content:

We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
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Title: Class Energy Image Analysis for Video Sensor-Based Gait Recognition: A Review

Section 1: Introduction
Description 1: Introduce the concept of gait recognition, its advantages, and the focus on video sensor-based systems. Provide an overview of the paper's organization.

Section 2: The Class Energy Image Approach
Description 2: Discuss the different methods of feature extraction and Class Energy Image generation, categorizing them into gait information accumulation approach, gait information introduction approach, and gait information fusion approach.

Section 3: Gait Information Accumulation Approach
Description 3: Describe the method of accumulating gait information to generate a Class Energy Image, including various techniques and examples such as MEI, MHI, and GEI.

Section 4: Gait Information Introduction Approach
Description 4: Explain methods that introduce dynamic information into static silhouette images to enhance gait recognition, including MIEI, FDEI, and CGI.

Section 5: Gait Information Fusion Approach
Description 5: Describe the fusion of static, dynamic, and temporal information through methods like X-T PEI and CGHI, and the benefits of combining multiple types of features.

Section 6: Experiments and Analysis
Description 6: Provide details on the experimental settings, datasets used (such as CASIA B and USF), and analyze the performance of various Class Energy Image approaches through experimental results.

Section 7: Recognition Performance Analysis on the USF Dataset
Description 7: Analyze the recognition performance of different approaches on the USF dataset under various conditions and summarize the findings.

Section 8: The Recognition Performance Analysis on CASIA B Dataset
Description 8: Compare and analyze the performance of Class Energy Image approaches under different walking states using the CASIA B dataset.

Section 9: Conclusions and Comments for Further Research
Description 9: Summarize the findings, highlight the strengths of Class Energy Image approaches, and suggest areas for future research.