Results 1 - 10 of 34

Individual recognition using gait energy image

by Ju Han, Student Member, Bir Bhanu - IEEE Trans. PAMI , 2006
"... Abstract—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 ..."
Abstract - Cited by 148 (11 self) - Add to MetaCart
Abstract—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. Index Terms—Gait recognition, real and synthetic templates, distortion analysis, feature fusion, performance evaluation, video. 1
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General tensor discriminant analysis and Gabor featuresforgaitrecognition,”IEEE Trans

by Dacheng Tao, Xuelong Li, Xindong Wu, Stephen J. Maybank - 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 ..."
Abstract - Cited by 105 (11 self) - Add to MetaCart
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
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Improved gait recognition by gait dynamics normalization

by Zongyi Liu, Sudeep Sarkar, Senior Member - 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 ..."
Abstract - Cited by 49 (1 self) - Add to MetaCart
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
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A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition

by Shiqi Yu, Daoliang Tan, Tieniu Tan - In International Conference on Pattern Recognition (ICPR , 2006
"... Gait recognition has gained increasing interest from re-searchers, but there is still no standard evaluation method to compare the performance of different gait recognition algorithms. In this paper, a framework is proposed in an attempt to tackle this problem. The framework consists of a large gait ..."
Abstract - Cited by 43 (1 self) - Add to MetaCart
Gait recognition has gained increasing interest from re-searchers, but there is still no standard evaluation method to compare the performance of different gait recognition algorithms. In this paper, a framework is proposed in an attempt to tackle this problem. The framework consists of a large gait database, a large set of well designed experi-ments and some evaluation metrics. There are 124 subjects in the database, and the gait data was captured from 11 views. Three variations, namely view angle, clothing and carrying condition changes, are separately considered in the database. The database is one of the largest database among the existing databases. Three sets of experiments, including a total of 363 experiments, are designed in the framework. Some metrics are proposed to evaluate gait recognition algorithms. 1.
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Gait components and their application to gender recognition

by Xuelong Li, Senior Member, Stephen J. Maybank, Senior Member, Shuicheng Yan, Dacheng Tao, Dong Xu - 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 ..."
Abstract - Cited by 37 (0 self) - Add to MetaCart
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.
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Human gait recognition with matrix representation

by Dong Xu, Shuicheng Yan, Dacheng Tao, Lei Zhang, Xuelong Li, Hong-jiang Zhang - 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 ..."
Abstract - Cited by 22 (4 self) - Add to MetaCart
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.
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Human carrying status in visual surveillance

by Dacheng Tao, Xuelong Li, Xindong Wu, Stephen J. Maybank - in Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition
"... A person’s gait changes when he or she is carrying an object such as a bag, suitcase or rucksack. As a result, human identification and tracking are made more difficult because the averaged gait image is too simple to represent the carrying status. Therefore, in this paper we first introduce a set o ..."
Abstract - Cited by 21 (2 self) - Add to MetaCart
A person’s gait changes when he or she is carrying an object such as a bag, suitcase or rucksack. As a result, human identification and tracking are made more difficult because the averaged gait image is too simple to represent the carrying status. Therefore, in this paper we first introduce a set of Gabor based human gait appearance models, because Gabor functions are similar to the receptive field profiles in the mammalian cortical simple cells. The very high dimensionality of the feature space makes training difficult. In order to solve this problem we propose a general tensor discriminant analysis (GTDA), which seamlessly incorporates the object (Gabor based human gait appearance model) structure information as a natural constraint. GTDA differs from the previous tensor based discriminant analysis methods in that the training converges. Existing methods fail to converge in the training stage. This makes them unsuitable for practical tasks. Experiments are carried out on the USF baseline data set to recognize a human’s ID from the gait silhouette. The proposed Gabor gait incorporated with GTDA is demonstrated to significantly outperform the existing appearance-based methods.
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Shape variation-based frieze pattern for robust gait recognition

by Seungkyu Lee, Yanxi Liu, Robert Collins - In Proc. of the International Conference on Computer Vision and Pattern Recognition (CVPR , 2007
"... Gait is an attractive biometric for vision-based human identification. Previous work on existing public data sets has shown that shape cues yield improved recognition rates compared to pure motion cues. However, shape cues are fragile to gross appearance variations of an individual, for example, wal ..."
Abstract - Cited by 15 (1 self) - Add to MetaCart
Gait is an attractive biometric for vision-based human identification. Previous work on existing public data sets has shown that shape cues yield improved recognition rates compared to pure motion cues. However, shape cues are fragile to gross appearance variations of an individual, for example, walking while carrying a ball or a backpack. We introduce a novel, spatiotemporal Shape Variation-Based Frieze Pattern (SVB frieze pattern) representation for gait, which captures motion information over time. The SVB frieze pattern represents normalized frame difference over gait cycles. Rows/columns of the vertical/horizontal SVB frieze pattern contain motion variation information augmented by key frame information with body shape. A temporal symmetry map of gait patterns is also constructed and combined with vertical/horizontal SVB frieze patterns for measuring the dissimilarity between gait sequences. Experimental results show that our algorithm improves gait recognition performance on sequences with and without gross differences in silhouette shape. We demonstrate superior performance of this computational framework over previous algorithms using shape cues alone on both CMU MoBo and UoS HumanID gait databases. 1.
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Towards scalable view-invariant gait recognition: Multilinear analysis for gait

by Chan-su Lee, Ahmed Elgammal - 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 ..."
Abstract - Cited by 14 (2 self) - Add to MetaCart
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
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Human activity recognition in thermal infrared imagery

by Ju Han, Bir Bhanu - Proceedings of the IEEE Workshop on Object Tracking and Classification Beyond the Visible Spectrum , 2005
"... In this paper, we investigate human repetitive activity properties from thermal infrared imagery, where human motion can be easily detected from the background regardless of lighting conditions and colors of the human surfaces and backgrounds. We employ an efficient spatiotemporal representation for ..."
Abstract - Cited by 13 (0 self) - Add to MetaCart
In this paper, we investigate human repetitive activity properties from thermal infrared imagery, where human motion can be easily detected from the background regardless of lighting conditions and colors of the human surfaces and backgrounds. We employ an efficient spatiotemporal representation for human repetitive activity recognition, which represents human motion sequence in a single image while preserving some temporal information. A statistical approach is used to extract features for activity recognition. Experimental results show that the proposed approach achieves good performance for human repetitive activity recognition. 1
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