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52
General tensor discriminant analysis and Gabor featuresforgaitrecognition,”IEEE Trans
- Pattern Anal. Mach. Intell
, 2007
"... Abstract — The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by ..."
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Cited by 105 (11 self)
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Abstract — The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample 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, e.g., principal component analysis (PCA) and 2DLDA, include 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, while 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) the GaborD representation 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
Hierarchical spatio-temporal context modeling for action recognition
- in proc. CVPR
, 2009
"... The problem of recognizing actions in realistic videos is challenging yet absorbing owing to its great potentials in many practical applications. Most previous research is limited due to the use of simplified action databases un-der controlled environments or focus on excessively local-ized features ..."
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Cited by 86 (2 self)
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The problem of recognizing actions in realistic videos is challenging yet absorbing owing to its great potentials in many practical applications. Most previous research is limited due to the use of simplified action databases un-der controlled environments or focus on excessively local-ized features without sufficiently encapsulating the spatio-temporal context. In this paper, we propose to model the spatio-temporal context information in a hierarchical way, where three levels of context are exploited in ascending or-der of abstraction: 1) point-level context (SIFT average de-scriptor), 2) intra-trajectory context (trajectory transition descriptor), and 3) inter-trajectory context (trajectory prox-imity descriptor). To obtain efficient and compact repre-sentations for the latter two levels, we encode the spatio-temporal context information into the transition matrix of a Markov process, and then extract its stationary distribu-tion as the final context descriptor. Building on the multi-channel nonlinear SVMs, we validate this proposed hierar-chical framework on the realistic action (HOHA) and event (LSCOM) recognition databases, and achieve 27 % and 66 % relative performance improvements over the state-of-the-art results, respectively. We further propose to employ the Multiple Kernel Learning (MKL) technique to prune the kernels towards speedup in algorithm evaluation. 1.
Improved gait recognition by gait dynamics normalization
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... Abstract—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 mo ..."
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Cited by 49 (1 self)
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Abstract—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. Index Terms—Gait recognition, biometrics, LDA, gait shape, population HMM. 1
Discriminant locally linear embedding with high-order tensor data
- IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, PART B: CYBERNETICS
, 2008
"... Graph-embedding along with its linearization and kernelization provides a general framework that unifies most traditional dimensionality reduction algorithms. From this frame-work, we propose a new manifold learning technique called discriminant locally linear embedding (DLLE), in which the local ge ..."
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Cited by 44 (12 self)
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Graph-embedding along with its linearization and kernelization provides a general framework that unifies most traditional dimensionality reduction algorithms. From this frame-work, we propose a new manifold learning technique called discriminant locally linear embedding (DLLE), in which the local geometric properties within each class are preserved according to the locally linear embedding (LLE) criterion, and the separabil-ity between different classes is enforced by maximizing margins between point pairs on different classes. To deal with the out-of-sample problem in visual recognition with vector input, the linear version of DLLE, i.e., linearization of DLLE (DLLE/L), is directly proposed through the graph-embedding framework. Moreover, we propose its multilinear version, i.e., tensorization of DLLE, for the out-of-sample problem with high-order tensor input. Based on DLLE, a procedure for gait recognition is described. We conduct comprehensive experiments on both gait and face recognition, and observe that: 1) DLLE along its linearization and tensorization outperforms the related versions of linear discriminant analysis, and DLLE/L demonstrates greater effectiveness than the linearization of LLE; 2) algorithms based on tensor representations are generally superior to linear algorithms when dealing with intrinsically high-order data; and 3) for human gait recognition, DLLE/L generally obtains higher accuracy than state-of-the-art gait recognition algorithms on the standard University of South Florida gait database.
Gait components and their application to gender recognition
- IEEE Trans. Syst., Man, Cybern. C, Appl. Rev
, 2008
"... Abstract—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 sil ..."
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Cited by 37 (0 self)
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Abstract—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. Index Terms—Biometrics, gender recognition, human gait recognition, visual surveillance. I.
Marginal Fisher Analysis and Its Variants for Human Gait Recognition and Content- Based Image Retrieval
"... Abstract—Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based image retrieval (CBIR). In this paper, we present extensions of our recently proposed marginal Fisher anal ..."
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Cited by 35 (5 self)
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Abstract—Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based image retrieval (CBIR). In this paper, we present extensions of our recently proposed marginal Fisher analysis (MFA) to address these problems. For human gait recognition, we first present a direct application of MFA, then inspired by recent advances in matrix and tensor-based dimensionality reduction algorithms, we present matrix-based MFA for directly handling 2-D input in the form of gray-level averaged images. For CBIR, we deal with the relevance feedback problem by extending MFA to marginal biased analysis, in which within-class compactness is characterized only by the distances between each positive sample and its neighboring positive samples. In addition, we present a new technique to acquire a direct optimal solution for MFA without resorting to objective function modification as done in many previous algorithms. We conduct comprehensive experiments on the USF HumanID gait database and the Corel image retrieval database. Experimental results demonstrate that MFA and its extensions outperform related algorithms in both applications. Index Terms—Content-based image retrieval (CBIR), dimensionality reduction, gait recognition, marginal Fisher analysis (MFA), relevance feedback. I.
Human gait recognition with matrix representation
- 2006) 896–903. ARTICLE IN PRESS
, 2009
"... Abstract—Human gait is an important biometric feature. It can be perceived from a great distance and has recently attracted greater attention in video-surveillance-related applications, such as closed-circuit television. We explore gait recognition based on a matrix representation in this paper. Fir ..."
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Cited by 22 (4 self)
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Abstract—Human gait is an important biometric feature. It can be perceived from a great distance and has recently attracted greater attention in video-surveillance-related applications, such as closed-circuit television. We explore gait recognition based on a matrix representation in this paper. First, binary silhouettes over one gait cycle are averaged. As a result, each gait video sequence, containing a number of gait cycles, is represented by a series of gray-level averaged images. Then, a matrix-based unsupervised algorithm, namely coupled subspace analysis (CSA), is employed as a preprocessing step to remove noise and retain the most rep-resentative information. Finally, a supervised algorithm, namely discriminant analysis with tensor representation, is applied to further improve classification ability. This matrix-based scheme demonstrates a much better gait recognition performance than state-of-the-art algorithms on the standard USF HumanID Gait database. Index Terms—Coupled subspaces analysis (CSA), dimension-ality reduction, discriminant analysis with tensor representation (DATER), human gait recognition, object representation. I.
A Survey of Biometric Gait Recognition: Approaches, Security and Challenges”, presented at the NIK – 2007 conference
"... Biometric systems are becoming increasingly important, since they provide more reliable and efficient means of identity verification. Biometric gait recognition (i.e. recognizing people from the way they walk) is one of the recent attractive topics in biometric research. This paper presents biometri ..."
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Cited by 16 (0 self)
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Biometric systems are becoming increasingly important, since they provide more reliable and efficient means of identity verification. Biometric gait recognition (i.e. recognizing people from the way they walk) is one of the recent attractive topics in biometric research. This paper presents biometric user recognition based on gait. Biometric gait recognition is categorized into three groups based on: machine vision, floor sensor and wearable sensor. An overview of each gait recognition category is presented. In addition, factors that may influence gait recognition are outlined. Furthermore, the security evaluations of biometric gait under various attack scenarios are also presented. 1
Face and human gait recognition using image-to-class distance
- IEEE Trans. Circuits Syst. Video Technol
, 2010
"... Abstract—We propose a new distance measure for face recognition and human gait recognition. Each probe image (a face image or an average human silhouette image) is represented as a set of local features uniformly sampled over a grid with fixed spacing, and each gallery image is represented as a set ..."
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Cited by 14 (3 self)
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Abstract—We propose a new distance measure for face recognition and human gait recognition. Each probe image (a face image or an average human silhouette image) is represented as a set of local features uniformly sampled over a grid with fixed spacing, and each gallery image is represented as a set of local features sampled at each pixel. We formulate an integer programming problem to compute the distance (referred to as the image-toclass distance) from one probe image to all the gallery images belonging to a certain class, in which any feature of the probe image can be matched to only one feature from one of the gallery images. Considering computational efficiency as well as the fact that face images or average human silhouette images are roughly aligned in the preprocessing step, we also enforce a spatial neighborhood constraint by only allowing neighboring features that are within a given spatial distance to be considered for feature matching. The integer programming problem is further treated as a classical minimum-weight bipartite graph matching problem, which can be efficiently solved with the Kuhn–Munkres algorithm. We perform comprehensive experiments on three benchmark face databases: 1) the CMU PIE database; 2) the FERET database; and 3) the FRGC database, as well as the USF Human ID gait database. The experiments clearly demonstrate the effectiveness of our image-to-class distance. Index Terms—Face recognition, human gait recognition, image-to-class distance. I.
Towards scalable view-invariant gait recognition: Multilinear analysis for gait
- in Proc. Int. Conf. on Audio and Video-Based Biometric Person Authentication
, 2005
"... Abstract. In this paper we introduce a novel approach for learning view-invariant gait representation that does not require synthesizing particular views or any camera calibration. Given walking sequences captured from multiple views for multiple people, we fit a multilinear generative model using h ..."
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Cited by 14 (2 self)
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Abstract. In this paper we introduce a novel approach for learning view-invariant gait representation that does not require synthesizing particular views or any camera calibration. Given walking sequences captured from multiple views for multiple people, we fit a multilinear generative model using higher-order singular value decomposition which decomposes view factors, body configuration factors, and gait-style factors. Gait-style is a view-invariant, time-invariant, and speedinvariant gait signature that can then be used in recognition. In the recognition phase, a new walking cycle of unknown person in unknown view is automatically aligned to the learned model and then iterative procedure is used to solve for both the gait-style parameter and the view. The proposed framework allows for scalability to add a new person to already learned model even if a single cycle of a single view is available. 1
