A Review of Vision-Based Gait Recognition Methods for Human Identification
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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: A hidden Markov model based framework for recognition of humans from gait sequences

paper_content:

In this paper we propose a generic framework based on hidden Markov models (HMMs) for recognition of individuals from their gait. The HMM framework is suitable, because the gait of an individual can be visualized as his adopting postures from a set, in a sequence which has an underlying structured probabilistic nature. The postures that the individual adopts can be regarded as the states of the HMM and are typical to that individual and provide a means of discrimination. The framework assumes that, during gait, the individual transitions between N discrete postures or states but it is not dependent on the particular feature vector used to represent the gait information contained in the postures. The framework, thus, provides flexibility in the selection of the feature vector. The statistical nature of the HMM lends robustness to the model. In this paper we use the binarized background-subtracted image as the feature vector and use different distance metrics, such as those based on the L/sub 1/ and L/sub 2/ norms of the vector difference, and the normalized inner product of the vectors, to measure the similarity between feature vectors. The results we obtain are better than the baseline recognition rates reported before.
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paper_title: Technology review - Biometrics-Technology, Application, Challenge, and Computational Intelligence Solutions

paper_content:

Biometrics is the science of the measurement of unique human characteristics, both physical and behavioral. Various biometric technologies are available for identifying or verifying an individual by measuring fingerprint, hand, face, signature, voice, or a combination of these traits. This paper aims to assist readers as they consider biometric solutions by examining common biometric technologies, introducing different biometric applications, and reviewing recent CI solutions presented at the 2006 IEEE WCCI
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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: Silhouette Analysis-Based Gait Recognition for Human Identification

paper_content:

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: 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: Motion-based recognition of people in EigenGait space

paper_content:

A motion-based, correspondence-free technique or human gait recognition in monocular video is presented. We contend that the planar dynamics of a walking person are encoded in a 2D plot consisting of the pairwise image similarities of the sequence of images of the person, and that gait recognition can be achieved via standard pattern classification of these plots. We use background modelling to track the person for a number of frames and extract a sequence of segmented images of the person. The self-similarity plot is computed via correlation of each pair of images in this sequence. For recognition, the method applies principal component analysis to reduce the dimensionality of the plots, then uses the k-nearest neighbor rule in this reduced space to classify an unknown person. This method is robust to tracking and segmentation errors, and to variation in clothing and background. It is also invariant to small changes in camera viewpoint and walking speed. The method is tested on outdoor sequences of 44 people with 4 sequences of each taken on two different days, and achieves a classification rate of 77%. It is also tested on indoor sequences of 7 people walking on a treadmill, taken from 8 different viewpoints and on 7 different days. A classification rate of 78% is obtained for near-fronto-parallel views, and 65% on average over all view.
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paper_title: Gait recognition using active shape model and motion prediction

paper_content:

This study presents a novel, robust gait recognition algorithm for human identification from a sequence of segmented noisy silhouettes in a low-resolution video. The proposed recognition algorithm enables automatic human recognition from model-based gait cycle extraction based on the prediction-based hierarchical active shape model (ASM). The proposed algorithm overcomes drawbacks of existing works by extracting a set of relative model parameters instead of directly analysing the gait pattern. The feature extraction function in the proposed algorithm consists of motion detection, object region detection and ASM, which alleviate problems in the baseline algorithm such as background generation, shadow removal and higher recognition rate. Performance of the proposed algorithm has been evaluated by using the HumanID Gait Challenge data set, which is the largest gait benchmarking data set with 122 objects with different realistic parameters including viewpoint, shoe, surface, carrying condition and time.
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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: Automatic extraction and description of human gait models for recognition purposes

paper_content:

Using gait as a biometric is of emerging interest. We describe a new model-based moving feature extraction analysis is presented that automatically extracts and describes human gait for recognition. The gait signature is extracted directly from the evidence gathering process. This is possible by using a Fourier series to describe the motion of the upper leg and apply temporal evidence gathering techniques to extract the moving model from a sequence of images. Simulation results highlight potential performance benefits in the presence of noise. Classification uses the k-nearest neighbour rule applied to the Fourier components of the motion of the upper leg. Experimental analysis demonstrates that an improved classification rate is given by the phase-weighted Fourier magnitude information over the use of the magnitude information alone. The improved classification capability of the phase-weighted magnitude information is verified using statistical analysis of the separation of clusters in the feature space. Furthermore, the technique is shown to be able to handle high levels of occlusion, which is of especial importance in gait as the human body is self-occluding. As such, a new technique has been developed to automatically extract and describe a moving articulated shape, the human leg, and shown its potential in gait as a biometric.
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paper_title: Background subtraction techniques: a review

paper_content:

Background subtraction is a widely used approach for detecting moving objects from static cameras. Many different methods have been proposed over the recent years and both the novice and the expert can be confused about their benefits and limitations. In order to overcome this problem, this paper provides a review of the main methods and an original categorisation based on speed, memory requirements and accuracy. Such a review can effectively guide the designer to select the most suitable method for a given application in a principled way. Methods reviewed include parametric and non-parametric background density estimates and spatial correlation approaches.
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paper_title: Human gait recognition based on matching of body components

paper_content:

This paper presents a novel approach for gait recognition based on the matching of body components. The human body components are studied separately and are shown to have unequal discrimination power. Several approaches are presented for the combination of the results obtained from different body components into a common distance metric for the evaluation of similarity between gait sequences. A method is also proposed for the determination of the weighting of the various body components based on their contribution to recognition performance. Using the best performing of the proposed methods, improved recognition performance is achieved.
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paper_title: Gait Components and Their Application to Gender Recognition

paper_content:

Human gait is a promising biometrics resource. In this paper, the information about gait is obtained from the motions of the different parts of the silhouette. The human silhouette is segmented into seven components, namely head, arm, trunk, thigh, front-leg, back-leg, and feet. The leg silhouettes for the front-leg and the back-leg are considered separately because, during walking, the left leg and the right leg are in front or at the back by turns. Each of the seven components and a number of combinations of the components are then studied with regard to two useful applications: human identification (ID) recognition and gender recognition. More than 500 different experiments on human ID and gender recognition are carried out under a wide range of circumstances. The effectiveness of the seven human gait components for ID and gender recognition is analyzed.
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paper_title: Automated Human Recognition by Gait using Neural Network

paper_content:

We describe a new method for recognizing humans by their gait using back-propagation neural network. Here, the gait motion is described as rhythmic and periodic motion, and a 2D stick figure is extracted from gait silhouette by motion information with topological analysis guided by anatomical knowledge. A sequential set of 2D stick figures is used to represent the gait signature that is primitive data for the feature extraction based on motion parameters. Then, a back-propagation neural network algorithm is used to recognize humans by their gait patterns. In experiments, higher gait recognition performances have been achieved.
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paper_title: Automated person recognition by walking and running via model-based approaches

paper_content:

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: 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: Fusion of static and dynamic body biometrics for gait recognition

paper_content:

Human identification at a distance has recently gained growing interest from computer vision researchers. This paper aims to propose a visual recognition algorithm based upon fusion of static and dynamic body biometrics. For each sequence involving a walking figure, pose changes of the segmented moving silhouettes are represented as an associated sequence of complex vector configurations, and are then analyzed using the Procrustes shape analysis method to obtain a compact appearance representation, called static information of body. Also, a model-based approach is presented under a condensation framework to track the walker and to recover joint-angle trajectories of lower limbs, called dynamic information of gait. Both static and dynamic cues are respectively used for recognition using the nearest exemplar classifier. They are also effectively fused on decision level using different combination rules to improve the performance of both identification and verification. Experimental results on a dataset including 20 subjects demonstrate the validity of the proposed algorithm.
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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: Automatic extraction and description of human gait models for recognition purposes

paper_content:

Using gait as a biometric is of emerging interest. We describe a new model-based moving feature extraction analysis is presented that automatically extracts and describes human gait for recognition. The gait signature is extracted directly from the evidence gathering process. This is possible by using a Fourier series to describe the motion of the upper leg and apply temporal evidence gathering techniques to extract the moving model from a sequence of images. Simulation results highlight potential performance benefits in the presence of noise. Classification uses the k-nearest neighbour rule applied to the Fourier components of the motion of the upper leg. Experimental analysis demonstrates that an improved classification rate is given by the phase-weighted Fourier magnitude information over the use of the magnitude information alone. The improved classification capability of the phase-weighted magnitude information is verified using statistical analysis of the separation of clusters in the feature space. Furthermore, the technique is shown to be able to handle high levels of occlusion, which is of especial importance in gait as the human body is self-occluding. As such, a new technique has been developed to automatically extract and describe a moving articulated shape, the human leg, and shown its potential in gait as a biometric.
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paper_title: Stride and Cadence as a Biometric in Automatic Person Identification and Verification

paper_content:

Presents a correspondence-free method to automatically estimate the spatio-temporal parameters of gait (stride length and cadence) of a walking person from video. Stride and cadence are functions of body height, weight and gender, and we use these biometrics for identification and verification of people. The cadence is estimated using the periodicity of a walking person. Using a calibrated camera system, the stride length is estimated by first tracking the person and estimating their distance travelled over a period of time. By counting the number of steps (again using periodicity) and assuming constant-velocity walking, we are able to estimate the stride to within 1 cm for a typical outdoor surveillance configuration (under certain assumptions). With a database of 17 people and eight samples of each, we show that a person is verified with an equal error rate (EER) of 11%, and correctly identified with a probability of 40%. This method works with low-resolution images of people and is robust to changes in lighting, clothing and tracking errors. It is view-invariant, though performance is optimal in a near-fronto-parallel configuration.
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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: 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: Silhouette Analysis-Based Gait Recognition for Human Identification

paper_content:

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: Silhouette quality quantification for gait sequence analysis and recognition

paper_content:

Most gait analysis and recognition algorithms are designed based on silhouette data. Their recognition performances may therefore be impaired by the poor quality silhouettes extracted from outdoor environments. In this paper, a silhouette quality quantification (SQQ) method is proposed to assess the quality of silhouette sequence. SQQ analyzes the sequence quality based on 1D foreground-sum signal modeling and signal processing technique. As an immediate application of SQQ, a general enhancement framework namely silhouette quality weighting (SQW) is designed toward improving most of the current gait recognition algorithms by taking into consideration sequence quality. The experiments are performed on the USF HumanID gait dataset v1.7 (with 71 subjects). Investigation using the SQQ criterion has revealed the baseline algorithm's mechanism of silhouette quality consideration. Two instantiations of the SQW algorithm based on gait energy image (GEI) are implemented. Improved recognition performances compared to the original GEI and baseline methods are obtained, which verifies the effectiveness of the proposed SQQ, SQW methods.
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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: Gait Recognition Using Wavelet Packet Silhouette Representation and Transductive Support Vector Machines

paper_content:

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: Automatic gait recognition based on statistical shape analysis

paper_content:

Gait recognition has recently gained significant attention from computer vision researchers. This interest is strongly motivated by the need for automated person identification systems at a distance in visual surveillance and monitoring applications. The paper proposes a simple and efficient automatic gait recognition algorithm using statistical shape analysis. For each image sequence, an improved background subtraction procedure is used to extract moving silhouettes of a walking figure from the background. Temporal changes of the detected silhouettes are then represented as an associated sequence of complex vector configurations in a common coordinate frame, and are further analyzed using the Procrustes shape analysis method to obtain mean shape as gait signature. Supervised pattern classification techniques, based on the full Procrustes distance measure, are adopted for recognition. This method does not directly analyze the dynamics of gait, but implicitly uses the action of walking to capture the structural characteristics of gait, especially the shape cues of body biometrics. The algorithm is tested on a database consisting of 240 sequences from 20 different subjects walking at 3 viewing angles in an outdoor environment. Experimental results are included to demonstrate the encouraging performance of the proposed algorithm.
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paper_title: Gait Recognition Using Radon Transform and Linear Discriminant Analysis

paper_content:

A new feature extraction process is proposed for gait representation and recognition. The new system is based on the Radon transform of binary silhouettes. For each gait sequence, the transformed silhouettes are used for the computation of a template. The set of all templates is subsequently subjected to linear discriminant analysis and subspace projection. In this manner, each gait sequence is described using a low-dimensional feature vector consisting of selected Radon template coefficients. Given a test feature vector, gait recognition and verification is achieved by appropriately comparing it to feature vectors in a reference gait database. By using the new system on the Gait Challenge database, very considerable improvements in recognition performance are seen in comparison to state-of-the-art methods for gait recognition
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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-based recognition of humans using continuous HMMs

paper_content:

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: Identification of humans using gait

paper_content:

We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: the width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the high-dimensional image feature is transformed to a lower dimensional space by generating what we call the frame to exemplar (FED) distance. The FED vector captures both structural and dynamic traits of each individual. For compact and effective gait representation and recognition, the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In the second method, referred to as the direct approach, we work with the feature vector directly (as opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically the observation probability B) based on the distance between the exemplars and the image features. In this way, we avoid learning high-dimensional probability density functions. The statistical nature of the HMM lends overall robustness to representation and recognition. The performance of the methods is illustrated using several databases.
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paper_title: AN ANGULAR TRANSFORM OF GAIT SEQUENCES FOR GAIT ASSISTED RECOGNITION

paper_content:

A new system is proposccd for gait analysis and recognition applications. 'The new system is based on a dcnoising process and a new angular transform that are applicd on hinary silhouettes. Each human silhouette in a pait sequencc is transformed into a low dinicnsional feature vector consisting of average pixel distances from the center OS the silhouette. The sequence of feature vectors corresponding to a gait sequence is used for identification based on a minimum-distance criterion between test and refercncc scqucnces. By using the new system on the Gait Challenge database. improvements in rccognition performance are seen in comparison to other methods of similar or higher complexity.
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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: Infrared gait recognition based on wavelet transform and support vector machine

paper_content:

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: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

paper_content:

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

paper_content:

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: Biologically inspired feature manifold for gait recognition

paper_content:

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: 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: Orthogonal Diagonal Projections for Gait Recognition

paper_content:

Gait has received much attention from researchers in the vision field due to its utility in walker identification. One of the key issues in gait recognition is how to extract discriminative shape features from 2D human silhouette images. This paper deals with the problem of gait-based walker recognition using statistical shape features. First, we normalize walkers' silhouettes (to facilitate gait feature comparison) into a square form and use the orthogonal projections in the positive and negative diagonal directions to draw personal signatures contained in gait patterns. Then principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to reduce the dimensionality of original gait features and to improve the topological structure in the feature space. Finally, this paper accomplishes the recognition of unknown gait features based on the nearest neighbor rule, with the discussion of the effect of distance metrics and scales on discriminating performance. Experimental results justify the potential of our method.
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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: Active energy image plus 2DLPP for gait recognition

paper_content:

This paper proposes a novel active energy image (AEI) method for gait recognition. Existing human gait feature representation methods, however, usually suffer from low quality of human silhouettes and insufficient dynamic characteristics. To this end, we apply the proposed AEI for gait representation. Given a gait silhouette sequence, we first extract the active regions by calculating the difference of two adjacent silhouette images, and construct an AEI by accumulating these active regions. Then, we project each AEI to a low-dimensional feature subspace via the newly proposed two-dimensional locality preserving projections (2DLPP) method to further improve the discriminative power of the extracted features. Experimental results on the CASIA gait database (dataset B and C) demonstrate the effectiveness of the proposed method.
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paper_title: Gait Feature Subset Selection by Mutual Information

paper_content:

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: Recursive spatiotemporal subspace learning for gait recognition

paper_content:

In this paper, we propose a new gait recognition method using recursive spatiotemporal subspace learning. In the first stage, periodic dynamic feature of gait over time is extracted by Principal Component Analysis (PCA) and gait sequences are represented in the form of Periodicity Feature Vector (PFV). In the second stage, shape feature of gait over space is extracted by Discriminative Locality Alignment (DLA) based on the PFV representation of gait sequences. After the recursive subspace learning, gait sequence data is compressed into a very compact vector named Gait Feature Vector (GFV) which is used for individual recognition. Compared to other gait recognition methods, GFV is an effective representation of gait because the recursive spatiotemporal subspace learning technique extracts both the shape features and the dynamic features. And at the same time, representing gait sequences in PFV form is an efficient way to save storage space and computational time. Experimental result shows that the proposed method achieves highly competitive performance with respect to the published gait recognition approaches on the USF HumanID gait database.
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paper_title: Silhouette-based human identification from body shape and gait

paper_content:

Our goal is to establish a simple baseline method for human identification based on body shape and gait. This baseline recognition method provides a lower bound against which to evaluate more complicated procedures. We present a viewpoint-dependent technique based on template matching of body silhouettes. Cyclic gait analysis is performed to extract key frames from a test sequence. These frames are compared to training frames using normalized correlation, and subject classification is performed by nearest-neighbor matching among correlation scores. The approach implicitly captures biometric shape cues such as body height, width, and body-part proportions, as well as gait cues such as stride length and amount of arm swing. We evaluate the method on four databases with varying viewing angles, background conditions (indoors and outdoors), walking styles and pixels on target.
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paper_title: Gait Recognition Using Wavelet Packet Silhouette Representation and Transductive Support Vector Machines

paper_content:

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: Automatic extraction and description of human gait models for recognition purposes

paper_content:

Using gait as a biometric is of emerging interest. We describe a new model-based moving feature extraction analysis is presented that automatically extracts and describes human gait for recognition. The gait signature is extracted directly from the evidence gathering process. This is possible by using a Fourier series to describe the motion of the upper leg and apply temporal evidence gathering techniques to extract the moving model from a sequence of images. Simulation results highlight potential performance benefits in the presence of noise. Classification uses the k-nearest neighbour rule applied to the Fourier components of the motion of the upper leg. Experimental analysis demonstrates that an improved classification rate is given by the phase-weighted Fourier magnitude information over the use of the magnitude information alone. The improved classification capability of the phase-weighted magnitude information is verified using statistical analysis of the separation of clusters in the feature space. Furthermore, the technique is shown to be able to handle high levels of occlusion, which is of especial importance in gait as the human body is self-occluding. As such, a new technique has been developed to automatically extract and describe a moving articulated shape, the human leg, and shown its potential in gait as a biometric.
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paper_title: Infrared gait recognition based on wavelet transform and support vector machine

paper_content:

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: 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: Gait analysis for human identification in frequency domain

paper_content:

In this paper, we analyze the spatio-temporal human characteristic of moving silhouettes in frequency domain, and find key Fourier descriptors that have better discriminatory capability for recognition than the other Fourier descriptors. A large number of experimental results and analysis show that the proposed algorithm based on the key Fourier descriptors can not only greatly reduce the gait data dimensionality, but also lighten the computation cost, with a satisfactory CCR. Besides that, classification performance can be further improved using feature fusion.
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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.
---
paper_title: Silhouette Analysis-Based Gait Recognition for Human Identification

paper_content:

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: The gait identification challenge problem: data sets and baseline algorithm

paper_content:

Recognition of people through gait analysis is an important research topic, with potential applications in video surveillance, tracking, and monitoring. Recognizing the importance of evaluating and comparing possible competing solutions to this problem, we previously introduced the HumanID challenge problem consisting of a set of experiments of increasing difficulty, a baseline algorithm, and a large set of video sequences (about 300 GB of data related to 452 sequences from 74 subjects) acquired to investigate important dimensions to this problem, such as variations due to viewpoint, footwear and walking surface. In this paper we present a detailed investigation of the baseline algorithm, quantify the dependence of the various covariates on gait-based identification, and update the previous baseline performance with optimized ones. We establish that the performance of the baseline algorithm is robust with respect to its various parameters. The overall identification performance is also stable with respect to the quality of the silhouettes. We find that the approximately lower 20% of the silhouette accounts for most of the recognition achieved. Viewpoint has barely statistically significant effect on identification rates, whereas footwear and surface-type does have significant effects with the effect due to surface-type being approximately 5 times that of shoe-type.
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paper_title: Matching shape sequences in video with applications in human movement analysis

paper_content:

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: Statistical Motion Model Based on the Change of Feature Relationships: Human gait-based recognition

paper_content:

We offer a novel representation scheme for view-based motion analysis using just the change in the relational statistics among the detected image features, without the need for object models, perfect segmentation, or part-level tracking. We model the relational statistics using the probability that a random group of features in an image would exhibit a particular relation. To reduce the representational combinatorics of these relational distributions, we represent them in a Space of Probability Functions (SoPF), where the Euclidean distance is related to the Bhattacharya distance between probability functions. Different motion types sweep out different traces in this space. We demonstrate and evaluate the effectiveness of this representation in the context of recognizing persons from gait. In particular, on outdoor sequences: (1) we demonstrate the possibility of recognizing persons from not only walking gait, but running and jogging gaits as well; (2) we study recognition robustness with respect to view-point variation; and (3) we benchmark the recognition performance on a database of 71 subjects walking on soft grass surface, where we achieve around 90 percent recognition rates in the presence of viewpoint variation.
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paper_title: A hidden Markov model based framework for recognition of humans from gait sequences

paper_content:

In this paper we propose a generic framework based on hidden Markov models (HMMs) for recognition of individuals from their gait. The HMM framework is suitable, because the gait of an individual can be visualized as his adopting postures from a set, in a sequence which has an underlying structured probabilistic nature. The postures that the individual adopts can be regarded as the states of the HMM and are typical to that individual and provide a means of discrimination. The framework assumes that, during gait, the individual transitions between N discrete postures or states but it is not dependent on the particular feature vector used to represent the gait information contained in the postures. The framework, thus, provides flexibility in the selection of the feature vector. The statistical nature of the HMM lends robustness to the model. In this paper we use the binarized background-subtracted image as the feature vector and use different distance metrics, such as those based on the L/sub 1/ and L/sub 2/ norms of the vector difference, and the normalized inner product of the vectors, to measure the similarity between feature vectors. The results we obtain are better than the baseline recognition rates reported before.
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paper_title: Discriminative feature selection for hidden Markov models using Segmental Boosting

paper_content:

We address the feature selection problem for hidden Markov models (HMMs) in sequence classification. Temporal correlation in sequences often causes difficulty in applying feature selection tech niques. Inspired by segmental k-means segmentation (SKS) [B. Juang and L. Rabiner, 1990], we propose Segmentally Boosted HMMs (SBHMMs), where the state-optimized features are constructed in a segmental and discriminative manner. The contributions are twofold. First, we introduce a novel feature selection algorithm, where the temporal dynamics are decoupled from the static learning procedure by assuming that the sequential data are piecewise independent and identically distributed. Second, we show that the SBHMM consistently improves traditional HMM recognition in various domains. The reduction of error compared to traditional HMMs ranges from 17% to 70% in American Sign Language recognition, human gait identification, lip reading, and speech recognition.
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paper_title: HMM based hand gesture recognition: A review on techniques and approaches

paper_content:

Gesture is one of the most natural and expressive ways of communications between human and computer in a virtual reality system. We naturally use various gestures to express our own intentions in everyday life. Hand gesture is one of the important methods of non-verbal communication for human beings for its freer in movements and much more expressive than any other body parts. Hand gesture recognition has a number of potential applications in human-computer interaction, machine vision, virtual reality, machine control in industry, and so on. As a gesture is a continuous motion on a sequential time series, the HMMs (Hidden Markov Models) must be a prominent recognition tool. The most important thing in hand gesture recognition is what the input features are that best represent the characteristics of the moving hand gesture.This paper presents part of literature review on ongoing research and findings on different technique and approaches in gesture recognition using HMMs for vision-based approach.
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paper_title: Gait Analysis For Human Identification Through Manifold Learning and HMM

paper_content:

With the increasing demands of visual surveillance systems, human identification at a distance has gained more interest. Gait is often used as an unobtrusive biometric offering the possibility to identify individuals at a distance without any interaction or co-operation with the subject. This paper presents a novel effectively method for automatic viewpoint and person identification by using only the sequence of gait silhouette. The gait silhouettes are nonlinearly transformed into low dimensional embedding and the dynamics in time-series images are modeled by HMM in the corresponding embedding space. The experimental results demonstrate that the proposed algorithm is an encouraging progress for automatic human identification.
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paper_title: Improved gait recognition by gait dynamics normalization

paper_content:

Potential sources for gait biometrics can be seen to derive from two aspects: gait shape and gait dynamics. We show that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information. We normalize for gait dynamics using a generic walking model, as captured by a population hidden Markov model (pHMM) defined for a set of individuals. The states of this pHMM represent gait stances over one gait cycle and the observations are the silhouettes of the corresponding gait stances. For each sequence, we first use Viterbi decoding of the gait dynamics to arrive at one dynamics-normalized, averaged, gait cycle of fixed length. The distance between two sequences is the distance between the two corresponding dynamics-normalized gait cycles, which we quantify by the sum of the distances between the corresponding gait stances. Distances between two silhouettes from the same generic gait stance are computed in the linear discriminant analysis space so as to maximize the discrimination between persons, while minimizing the variations of the same subject under different conditions. The distance computation is constructed so that it is invariant to dilations and erosions of the silhouettes. This helps us handle variations in silhouette shape that can occur with changing imaging conditions. We present results on three different, publicly available, data sets. First, we consider the HumanID gait challenge data set, which is the largest gait benchmarking data set that is available (122 subjects), exercising five different factors, i.e., viewpoint, shoe, surface, carrying condition, and time. We significantly improve the performance across the hard experiments involving surface change and briefcase carrying conditions. Second, we also show improved performance on the UMD gait data set that exercises time variations for 55 subjects. Third, on the CMU Mobo data set, we show results for matching across different walking speeds. It is worth noting that there was no separate training for the UMD and CMU data sets.
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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: A tutorial on hidden Markov models and selected applications in speech recognition

paper_content:

This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. >
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paper_title: Individual recognition from periodic activity using hidden Markov models

paper_content:

We present a method for recognizing individuals from their walking and running gait. The method is based on Hu moments of the motion segmentation in each frame. Periodicity is detected in such a sequence of feature vectors by minimizing the sum of squared differences, and the individual is recognized from the feature vector sequence using hidden Markov models. Comparisons are made to earlier periodicity detection approaches and to earlier individual recognition approaches. Experiments show the successful recognition of individuals (and their gait) in frontoparallel sequences.
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paper_title: Gait-based recognition of humans using continuous HMMs

paper_content:

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: Analyzing Human Movements from Silhouettes Using Manifold Learning

paper_content:

A novel method for learning and recognizing sequential image data is proposed, and promising applications to vision-based human movement analysis are demonstrated. To find more compact representations of high-dimensional silhouette data, we exploit locality preserving projections (LPP) to achieve low-dimensional manifold embedding. Further, we present two kinds of methods to analyze and recognize learned motion manifolds. One is correlation matching based on the Hausdorrf distance, and the other is a probabilistic method using continuous hidden Markov models (HMM). Encouraging results are obtained in two representative experiments in the areas of human activity recognition and gait-based human identification.
---
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: Identification of humans using gait

paper_content:

We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: the width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the high-dimensional image feature is transformed to a lower dimensional space by generating what we call the frame to exemplar (FED) distance. The FED vector captures both structural and dynamic traits of each individual. For compact and effective gait representation and recognition, the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In the second method, referred to as the direct approach, we work with the feature vector directly (as opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically the observation probability B) based on the distance between the exemplars and the image features. In this way, we avoid learning high-dimensional probability density functions. The statistical nature of the HMM lends overall robustness to representation and recognition. The performance of the methods is illustrated using several databases.
---
paper_title: Silhouette Analysis-Based Gait Recognition for Human Identification

paper_content:

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.
---
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.
---
paper_title: Active energy image plus 2DLPP for gait recognition

paper_content:

This paper proposes a novel active energy image (AEI) method for gait recognition. Existing human gait feature representation methods, however, usually suffer from low quality of human silhouettes and insufficient dynamic characteristics. To this end, we apply the proposed AEI for gait representation. Given a gait silhouette sequence, we first extract the active regions by calculating the difference of two adjacent silhouette images, and construct an AEI by accumulating these active regions. Then, we project each AEI to a low-dimensional feature subspace via the newly proposed two-dimensional locality preserving projections (2DLPP) method to further improve the discriminative power of the extracted features. Experimental results on the CASIA gait database (dataset B and C) demonstrate the effectiveness of the proposed method.
---
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: Adaptive Fusion of Gait and Face for Human Identification in Video

paper_content:

Most work on multi-biometric fusion is based on static fusion rules which cannot respond to the changes of the environment and the individual users. This paper proposes adaptive multi-biometric fusion, which dynamically adjusts the fusion rules to suit the real-time external conditions. As a typical example, the adaptive fusion of gait and face in video is studied. Two factors that may affect the relationship between gait and face in the fusion are considered, i.e., the view angle and the subject-to-camera distance. Together they determine the way gait and face are fused at an arbitrary time. Experimental results show that the adaptive fusion performs significantly better than not only single biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX.
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paper_title: Uncorrelated discriminant simplex analysis for view-invariant gait signal computing

paper_content:

Human gait is a useful biometric signature and has recently gained growing interest from computer vision researchers. This interest is strongly driven by the need for automatic human identification and gender recognition at a distance in many surveillance applications. Existing human gait analysis methods, however, are sensitive to the view of the gait sequences, and their performances are poor when the view of the training gait sequences is different from that of the testing ones. In this paper, we propose a new supervised manifold learning algorithm, called uncorrelated discriminant simplex analysis (UDSA), for view-invariant gait signal computing. The aim of UDSA is to seek a mapping to project human gait sequences collected from different views into a low-dimensional feature subspace, such that intraclass geometrical structures are preserved and interclass distances of gait sequences are maximized simultaneously. Moreover, we impose an uncorrelated constraint to make the extracted features statistically uncorrelated. Experimental results are presented to demonstrate the efficacy of the proposed approach.
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paper_title: Infrared gait recognition based on wavelet transform and support vector machine

paper_content:

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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Title: A Review of Vision-Based Gait Recognition Methods for Human Identification

Section 1: Introduction
Description 1: Introduce the importance of individual identification, various biometric technologies, and the unique advantages of gait recognition. Outline the general framework of automatic gait recognition and the scope of the paper.

Section 2: Model-based Approaches
Description 2: Discuss model-based approaches for gait recognition, which involve obtaining static or dynamic body parameters through modeling or tracking body components. Highlight their advantages, challenges, and primary methods discussed in this section.

Section 3: Model-Free Approaches
Description 3: Explain model-free approaches that focus on silhouettes or overall motion rather than modeling specific body parts. Discuss their relative computational efficiency and robustness, as well as detailed methods within this category.

Section 4: Feature Dimensionality Reduction
Description 4: Address the necessity of reducing the high dimensionality of gait features. Review various linear and non-linear dimensionality reduction methods typically used in gait recognition.

Section 5: Direct Classification
Description 5: Present methods that classify gait based on single representations or key frames extracted from gait sequences. Introduce various classifiers such as nearest neighbor and support vector machines.

Section 6: Similarity of Temporal Sequences
Description 6: Discuss classification methods that measure the similarity of temporal gait sequences. Include methods like dynamic time warping (DTW) and frequency analysis.

Section 7: State-space Model: HMM
Description 7: Elaborate on the use of Hidden Markov Models (HMM) in gait recognition. Explain how HMMs model gait as a sequence of states and review various HMM-based recognition methods.

Section 8: Public Gait Datasets
Description 8: Describe standard publicly available gait datasets used for comparing and evaluating gait recognition algorithms. Provide details about notable datasets such as the USF Dataset, CMU Mobo Dataset, Southampton Dataset, and CASIA Gait Dataset.

Section 9: Conclusions and Future work
Description 9: Summarize the key points of the paper, the major issues in gait recognition, and the comparative strengths of different approaches. Discuss future research directions and potential improvements in the field.