人体步态识别研究
|
摘要
生物特征识别技术是一种利用人的生理或行为特征进行身份识别的技术。随着机场、车站、银行等安全敏感场合对大范围视觉监控系统的需求提升,远距离的身份识别研究近来受到了计算机视觉研究者们的大量关注。脸像、指纹和虹膜等生物特征通常需要近距离或者接触性的感知,因此在实际应用中受到了诸多限制。步态是远距离情况下唯一可以感知的生物特征,因此从视觉监控的观点来看,步态识别具有广泛的应用前景。
步态识别是一个新兴的研究领域,它利用人走路期间的行为特征来进行身份识别。围绕这个主题,本文开展了如下几个方面的研究工作：
1.步态轮廓提取是步态识别系统的重要步骤,它包括了背景建模和运动分割两个部分。本文采用核高斯模型(Kernel Gaussian Model)来对步态场景进行建模,同时提出了一种高效的高斯核带宽估计方法。步态图像通过背景模型被映射为背景概率图像之后,EM算法联合运动段分析的方法将自动地分割出概率图像中的人体运动区域。实验表明,该方法在复杂场景中的步态轮廓提取效果要明显优于传统的步态轮廓提取方法,并且它的运算效率能够满足动态场景的实时建模要求。
2.在步态轮廓较为清晰的情况下,本文采用模型化的方法,通过骨架模型来提取人体的步态识别信息。一种改进的基于距离变换的骨架算法首先被用来提取人体骨架；然后,通过骨架的腐蚀与还原技术从骨架模型中提取人体的躯干和腿部骨架枝；最后,躯干和腿部骨架的周期性运动特征作为步态的识别特征被用来进行个体身份识别。实验结果表明,该方法在室内环境下有着理想的识别成功率。特别在多视角的情况下,该方法可以通过对骨架模型进行视角修正来极大地提高其正确识别率。
3.在步态轮廓不甚理想的情况下,本文采用全局的方法,通过对步态的空时数据进行连续的特征子空间学习来提取识别信息。第一次特征子空间学习对步态的频域数据进行主成分分析,并将步态数据转化为周期特征矢量形式；第二次特征子空间学习对步态的周期特征矢量形式继续进行主成分分析加线性判别分析的联合处理,最终将步态数据转化为步态特征矢量。步态特征矢量既包含了人体的形态特征又包含了人体的运动特征,因此具有很强的识别能力。实验结果表明,该方法不仅获得了令人满意的正确识别率,而且还拥有相对较低的计算与存储代价。
Biometrics makes use of the physiological or behavioral charactristics of people to authenticate their identities. With the growing need for a full range of visual surveillance and monitoring system in security-sensitive envirenments such as airports, bus stops and banks, human identification at a distance has recently gained increasing interest from computer vision researchers. To operate successfully, the established biometrics such as face, fingerprints and iris usually require proximal sensing or physical contact. However they are hardly applicable at a distance. Fortunately, gait is still visible and can be easily perceived unobtrusively. So, gait is a very attractive modality from the visual surveillance perspective.
Gait recognition is a relatively new research area. It aims to seek distinguishable variations between the same actions of walking from different people for the purpose of automatic identity verification. Focusing on this topic, this dissertation mainly includes the following issues:
1. Silhouette extraction is an important procedure in gait recognition, which includes two steps:background modeling and motion extraction. In this dissertation, Kernel Gaussian Model is used to model the gait background, and an effective kernel width estimation method is proposed. After gait images are mapped into probability images by the statistical background model, foreground pixels are extracted from these probability images by the EM algrithem and motion slice analysis. Experiment result shows that the proposed method achieves obvious improvement than traditional methods for the complicated scenes. And at the meantime, it can satisfy the requirement for real-time background modeling.
2. Gait features are extracted by the model-based method of skeletonization under the situation that silhouette quality is relatively good. First, an improved distance transform method is proposed to extract the raw skeleton; then, the main branches of human body are extracted by the skeleton erosion and restoration technique; lastly, the periodic motion charactaristcs of human main skeleton are treated as the final gait feature to identify people. Experiment result shows that the skeleton feature of gait can achieve highly satisfying performance in indoor environment. Especially for multipul view settings, the recognition rate can be significantly increased by fixing the view angle of skeletons.
3. A novel gait recognition method based on spatiotemporal feature extraction is proposed under the situation of bad silhouette quality. In the first subspace learning, the periodic dynamic feature of gait is extracted by Principal Component Analysis and sequence data is represented in the Periodicity Feature Vector form; in the second subspace learning, Principal Component Analysis integrated with Linear Discriminant Analysis are applied to the Periodicity Feature Vector representation of gait and sequence data is compressed into Gait Feature Vector. Gait Feature Vector contains both the shape and the dynamic information of human gait, which shows strong discriminative ability. Experimental result shows that the proposed method achieves highly competitive performance and it takes less storeage and computational cost.
步态识别是一个新兴的研究领域,它利用人走路期间的行为特征来进行身份识别。围绕这个主题,本文开展了如下几个方面的研究工作：
1.步态轮廓提取是步态识别系统的重要步骤,它包括了背景建模和运动分割两个部分。本文采用核高斯模型(Kernel Gaussian Model)来对步态场景进行建模,同时提出了一种高效的高斯核带宽估计方法。步态图像通过背景模型被映射为背景概率图像之后,EM算法联合运动段分析的方法将自动地分割出概率图像中的人体运动区域。实验表明,该方法在复杂场景中的步态轮廓提取效果要明显优于传统的步态轮廓提取方法,并且它的运算效率能够满足动态场景的实时建模要求。
2.在步态轮廓较为清晰的情况下,本文采用模型化的方法,通过骨架模型来提取人体的步态识别信息。一种改进的基于距离变换的骨架算法首先被用来提取人体骨架；然后,通过骨架的腐蚀与还原技术从骨架模型中提取人体的躯干和腿部骨架枝；最后,躯干和腿部骨架的周期性运动特征作为步态的识别特征被用来进行个体身份识别。实验结果表明,该方法在室内环境下有着理想的识别成功率。特别在多视角的情况下,该方法可以通过对骨架模型进行视角修正来极大地提高其正确识别率。
3.在步态轮廓不甚理想的情况下,本文采用全局的方法,通过对步态的空时数据进行连续的特征子空间学习来提取识别信息。第一次特征子空间学习对步态的频域数据进行主成分分析,并将步态数据转化为周期特征矢量形式；第二次特征子空间学习对步态的周期特征矢量形式继续进行主成分分析加线性判别分析的联合处理,最终将步态数据转化为步态特征矢量。步态特征矢量既包含了人体的形态特征又包含了人体的运动特征,因此具有很强的识别能力。实验结果表明,该方法不仅获得了令人满意的正确识别率,而且还拥有相对较低的计算与存储代价。
Biometrics makes use of the physiological or behavioral charactristics of people to authenticate their identities. With the growing need for a full range of visual surveillance and monitoring system in security-sensitive envirenments such as airports, bus stops and banks, human identification at a distance has recently gained increasing interest from computer vision researchers. To operate successfully, the established biometrics such as face, fingerprints and iris usually require proximal sensing or physical contact. However they are hardly applicable at a distance. Fortunately, gait is still visible and can be easily perceived unobtrusively. So, gait is a very attractive modality from the visual surveillance perspective.
Gait recognition is a relatively new research area. It aims to seek distinguishable variations between the same actions of walking from different people for the purpose of automatic identity verification. Focusing on this topic, this dissertation mainly includes the following issues:
1. Silhouette extraction is an important procedure in gait recognition, which includes two steps:background modeling and motion extraction. In this dissertation, Kernel Gaussian Model is used to model the gait background, and an effective kernel width estimation method is proposed. After gait images are mapped into probability images by the statistical background model, foreground pixels are extracted from these probability images by the EM algrithem and motion slice analysis. Experiment result shows that the proposed method achieves obvious improvement than traditional methods for the complicated scenes. And at the meantime, it can satisfy the requirement for real-time background modeling.
2. Gait features are extracted by the model-based method of skeletonization under the situation that silhouette quality is relatively good. First, an improved distance transform method is proposed to extract the raw skeleton; then, the main branches of human body are extracted by the skeleton erosion and restoration technique; lastly, the periodic motion charactaristcs of human main skeleton are treated as the final gait feature to identify people. Experiment result shows that the skeleton feature of gait can achieve highly satisfying performance in indoor environment. Especially for multipul view settings, the recognition rate can be significantly increased by fixing the view angle of skeletons.
3. A novel gait recognition method based on spatiotemporal feature extraction is proposed under the situation of bad silhouette quality. In the first subspace learning, the periodic dynamic feature of gait is extracted by Principal Component Analysis and sequence data is represented in the Periodicity Feature Vector form; in the second subspace learning, Principal Component Analysis integrated with Linear Discriminant Analysis are applied to the Periodicity Feature Vector representation of gait and sequence data is compressed into Gait Feature Vector. Gait Feature Vector contains both the shape and the dynamic information of human gait, which shows strong discriminative ability. Experimental result shows that the proposed method achieves highly competitive performance and it takes less storeage and computational cost.
引文
[1]A. Jain, R. Bolle and S. Pankanti. Biometrics:personal identification in networked society [M]. Kluwer Academic Publishers, Boston,1999.
[2]景英娟,董育宁.生物特征识别技术综述[J].电子学报,2001,29(12A)：1744-1748.
[3]J. L. Wayman. Digital signal processing in biometric identification:a review [C]. International Conference on Image processing,2002, pp:37-40.
[4]X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, Face recognition using laplacianfaces, IEEE Transaction Pattern Analysis and Machine Intelligence [J], 2005,27(3),328-340.
[5]J. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Transaction Pattern Analysis and Machine Intelligence [J], 1993,15(11):1148-1161.
[6]L. Ma, T. Tan, Y. Wang, and D. Zhang, Personal identification based on iris texture analysis, IEEE Transaction Pattern Analysis and Machine Intelligence [J],2003, 25(12):1519-1533.
[7]D. J. Hurley, M. S. Nixon, J. N. Carter, Force field feature extraction for ear biometrics, Computer Vision and Image Understanding [J],2005.
[8]A. K. Jain, L. Hong, S. Pankanti and R. Bolle, An identity verification system using fingerprints. IEEE Proceedings [C],1999,85(9), pp:1365-1388.
[9]Y. Qi and B. R. Hunt, A multi-resolution approach to computer verification of handwritten signatures. IEEE Transaction Image Processing [J],1995,4(6): 870-874.
[10]M.S. Nixon and J.N. Carter, Advances in automatic gait recognition. Proc. Int. Conf. Automatic Face and Gesture Recognition [C],2004, pp:139-146.
[11]N. V. Boulgouris, D. Hatzinakos and K. N. Plataniotis, Gait Recognition:A challenging signal processing technology for biometric identification. IEEE Signal Processing [J],2005,22(6):78-90.
[12]J. E. Cutting and L. T. Kozlowski, Recognizing friends by their walk:gait perception without familiarity cues, Psychonomic Society [J],1977,9(5):353-360.
[131 Available:http://www.darpa.mil/iao/HID.htm.
[14]S. Ounpuu, The biomechanics of walking and running, Clin. Sports Med. [J],1994, 13(4):843-863.
[15]M. P. Murray, A. B. Drought and R. C. Kory, Walking patterns of normal men, Journal of Bone and Joint Surgery [J],1964,46-A(2):335-360.
[16]M. P. Murray, Gait as a total pattern of movement, American Journal of Physical Medicine [J],1967,46(1):290-332.
[17]M.W. Whittle, Clinical gait analysis:a review, Human Movement Science [J],1996, Vol.15, pp:369-387.
[18]D. Winter, The biomechanics and motor control of human gait,2nd Ed [M]. Ottawa, Canada:Waterloo Biomechanics,1991.
[19]J. Aggarwal and Q. Cai, Human motion analysis:a review, Computer Vision and Image Understanding [J],1999, Vol.73, pp:428-440.
[20]T. Moeslund and E. Granum, A survey of computer vision-based human motion capture, Computer Vision and Image Understanding,2001, Vol.81, pp:231-268.
[21]D. Gavrila, The visual analysis of human movement:a survey, Computer Vision and Image Understanding [J],1999,73(1):82-98.
[22]王亮,胡卫明,谭铁牛.人运动的视觉分析综述[J].计算机学报,2002,25(3)：225-237.
[23]R. Collins et. al., A system for video surveillance and monitoring:VSAM final report, Carnegie Mellon University, Technical Report:CMU-RI-TR-00-12,2000.
[24]P. J. Phillips, S. Sarkar, I. Robledo, et. al., The gait identification challenge problem: data sets and baseline algorithm, International Conference on Pattern Recognition [C],2002, pp:385-388.
[25]S. Sarkar, P.J. Phillips, Z. Liu, I. R. Vega, P. Grother, and K.W. Bowyer,The human ID gait challenge problem:data sets, performance, and analysis. IEEE Transacyion Pattern Analysis and Machine Intelligence [J],2005,27 (2):162-177.
[26]R. Gross, J. Shi,The CMU motion of body (MOBO) database, Technical Report CMU-RI-01-18, Robotics Institute, Carnegie Mellon University,2001.
[27]S. Yu, D. Tan, T. Tan, et. al., A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition[C]. International Conference on Pattern Recognition,2006, pp:441-444.
[28]G. Johansson, Visual perception of biological motion and a model for its analysis, Perception Psychophysics [J],1973,14(2):201-211.
[29]C. Cedras and M. Shah, A survey of motion analysis from moving light displays, Proc. of Int. Conf. on Computer Vision and Pattern Recognition [C],1994, pp: 214-221.
[30]C. Barclay, J. Cutting and L. Kozlowski, Temporal and spatial factors in gait perception that influence gender recognition, Perception and Psychophysics [J], 1978,23(2):145-152.
[31]X.L. Li, S.J. Maybank, S.C. Yan, D.C. Tao and D. Xu, Gait components and their application to gender recognition[J], IEEE T-SMC, Part C,2008,38(2):145-155.
[32]G. Mather and L. Murdock, Gender discrimination in biological motion displays based on dynamic cues [C], Proceedings of Royal Society London,1994, B258: 273-279.
[33]J. E. Cutting, D. R. Proffitt and L. Kozlowski, A biochemical invariant for gait perception and performance, Journal of Experimental Psychology:Human Perception 1978, Vol.4, pp:357-372.
[34]D. Hoffman and B. Flinchbaugh, The interpretation of biological motion, Biological Cybernetics [J],1982.
[35]K. Akita, Image sequence analysis of real world human motion, Pattern Recognition [J],1984,17(1):73-83.
[36]L. Wang, W. M. Hu and T. N. Tan, Recent developments in human motion analysis, Pattern Recognition [J],2003,36(3):585-601.
[37]K. Fukunaga, Introduction to statistical pattern recognition [M], New York Academic Press, New York, NY, USA,1990.
[38]R. Duda, P. Hart, and D. Stork, Pattern classification [M], John Wiley & Sons,2001.
[39]M. Turk and A. Pentland, Face recognition using eigenfaces. Proceedings Conf. Computer Vision and Pattern Recognition [C],1991, pp:586-591.
[40]P. Belhumeur, J. Hespanha, and D. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Transaction Pattern Analysis and Machine Intelligence [J],1997,19(7),711-720.
[41]D. Meyer, Human gait classification based on Hidden Markov Models, in "3D Image Analysis and Synthesis" edited by H.P. Seidel, B. Girod and H. Niemann, Erlangen,1997, pp:39-146.
[42]A. Bissacco, A. Chiuso, Y. Ma and S. Soatto, Recognition of human gaits, in Proc. of Intl. Conf. on Computer Vision and Pattern Recognition,2001.
[43]J. Han and B. Bhanu, Individual recognition using gait energy image [J], IEEE Transaction on Pattern Analysis and Machine Intelligence,2006,28 (2):316-322.
[44]T.H. Zhang, D.C. Tao and J. Yang. Discriminative Locality Alignment. ECCV Part Ⅰ,
pp:725-738,2008.
[45]D.C. Tao, X.L. Li, X.D. Wu, S.J. Maybank, General tensor analysis and Gabor features for gait recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(10):1700-1715.
[46]D.C. Tao, X.L. Li, X.D. Wu, S.J. Maybank, Geometric mean for subspace selection[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2009, 31(2):260-274.
[47]S. V. Stevenage, M. S. Nixon and K. Vince, Visual analysis of gait as a cue to identity, Applied Cognitive Psychology [J],1999,13:513-526.
[48]H. Lee and Z. Chen, Determination of 3D human body postures from a single view[J], Computer Vision, Graphics and Image Processing,1985,30:148-168.
[49]J.A. Webb and J.K. Aggarwal, Structure from motion of rigid and jointed objects[J], Artificial Intelligence,1982, Vol.19, pp:107-130.
[50]K. Rohr, Towards model-based recognition of human movements in image sequences, Computer Vision, Graphics and Image Processing [J],59(1):1994,94-115.
[51]A. Kakadiaris and D. Metaxas, Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection, in Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition [C],1996, pp:81-87.
[52]C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, Pfinder:real-time tracking of the human body, IEEE Transaction on Pattern Analysis and Machine Intelligence, 1997,19(7):780-785.
[53]T.H. Zhang, X.L. Li, D.C. Tao and J. Yang, Multimodal biometrics using geometry preserving projections [J], Pattern Recognition,2008,41(3):805-813.
[54]J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas, On combining classifiers, IEEE Transaction Pattern Analysis and Machine Intelligence [J],1998,20(3):226-239.
[55]G. Shakhnarovich, L. Lee, and T. Darrell, Integrated face and gait recognition from multiple views, Proc. IEEE Computer Vision and Pattern Recognition,2001, pp: 439-476.
[56]R. Chellappa, A. K. Roy-Chowdhury, A. Kale, Identification using gait and face[J], IEEE Conference on Computer Vision and Pattern Recognition,2007, pp:147-152.
[57]X. Zhou, B. Bhanu, Integrating face and gait for human recognition at a distance, IEEE Transactions on Systems, Man, and Cybernetics,2007,37(5):1119-1137.
[58]Z. liu, S. Sarkar, Outdoor recognition at a distanee by fusing gait and face[J], Image and Vision Computing,2007,25(6):817-532.
[59]D. M. Gavrila and L. S. Davis,3-D model-based tracking of humans in action:a multi-view approach, in Proceedings of International Conference on Computer Vision and Pattern Recognition [C],1996, pp:73-80.
[60]B. Flinchbaugh and B. Chandrasekaran, A theory of spatio-temporal aggregation for vision [J], Artificial Intelligence,1981, Vol.17, pp:387-407.
[61]A. Hilton and P. Fua, Modeling people toward vision-based understanding of a person's shape, Appearance, and Movement [J], Computer Vision and Image Understanding,2001, Vol.81, pp:227-230.
[62]R. Collins, R. Gross, and J. Shi, Silhouette-based human identification from body shape and gait, in Proceedings of IEEE International Conference Face and Gesture Recognition,2002, pp:366-371.
[63]Y. Liu, R. Collins, and Y. Tsin, Gait sequence analysis using frieze patterns, in Proceedings Europe Conference Computer Vision,2002, pp:657-671.
[64]R. Urtasun, P. Fua,3D tracking for gait characterization and recognition [C], IEEE International Conferenee on Automatic Face and Gesture Recognition,2004, pp: 17-22.
[65]A. Kale, A.K.R. Chowdhury, R. Chellappa, Towards a view invariant gait recognition algorithm [C], IEEE Conferenee on Advanced Video and Signal Based Surveillance,2003, pp:143-150.
[66]A. Johnson and A. Bobick, A multi-view method for gait recognition using static body parameters[C], in Proceedings of International Conference on Audio-and Video-based Biometric Person Authentication,2001, pp:301-311.
[67]A. F. Bobick and A. Y. Johnson, Gait recognition using static, activity-specific parameters [C], Proceedings of IEEE Computer Vision and Pattern Recognition, 2001, Vol.1,pp:423-430.
[68]J. Little and J. Boyd, Recognizing people by their gait:the shape of motion, Videre: Journal of Computer Vision Research [J],1998,1(2):2-32.
[69]J. Boyd, Video phase-locked loops in gait recognition, in Proceedings of International Conference on Computer Vision [C],2001.
[70]G. Zhao, R. Chen, G. Liu, and L. Hua, Amplitude spectrum-based gait recognition [C], in Proceedings of IEEE International Conference Face and Gesture Recognition 2004, pp:23-28.
[71]R. Tanawongsuwan and A. Bobick, Gait recognition from time-normalized joint-angle trajectories in the walking plane [C], Proceedings of IEEE Computer Vision and Pattern Recognition 2001, pp:726-731.
[72]R. G. Cutler, On the detection, analysis, and applications of oscillatory motions in video sequences, Ph.D. thesis, University of Maryland, College Park, Md, USA, 2000.
[73]C. BenAbdelkader, R. Culter and L. Davis, Person identification using automatic height and stride estimation, in Proceedings of IEEE International Conference on Pattern Recognition [C],2002.
[74]K. Karman and A. Brandt, Moving object recognition using an adaptive background memory [M], In:V Cappellini, Time-varying Image Processing and Moving Object Recognition.2. Elsevier,1990.
[75]M. Kilger, A shadow handler in a video-based real-time traffic monitoring system [C], Proceedings of IEEE Workshop on Applications of Computer Vision, Palm Springs, CA,1992, pp:1060-1066.
[76]Dieter Koller, Joseph Weber, and Jitendra Malik, Robust multiple car tracking with occlusion reasoning [C], Europe Conference of Computer Vinsion,1994, pp: 189-196, Stockholm, Sweden.
[77]K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, Wallflower:principles and practice of background maintenance [C], International Conference of Computer Vision,1999, pp:255-261, Kerkyra, Greece.
[78]N.M. Oliver, B. Rosario, and A.P. Pentland, A bayesian computer vision system for modeling human interactions [J], IEEE Transaction on Pattern Analysis and Machine Intelligence,2000,22(8):831-843.
[79]J. Zhong and S. Sclaroff, Segmenting foreground objects from a dynamic, textured background via a robust kalman filter [C], International Conference of Computer Vision,2003, pp:44-50, Nice, France.
[80]G. Doretto, A. Chiuso, Y.N. Wu, and S. Soatto. Dynamic textures [C], International Journal Computer Vision,2003,51(2):91-109.
[81]A. Monnet, A. Mittal, N. Paragios, and V. Ramesh, Background modeling and subtraction of dynamic scenes [C], International Conference of Computer Vision, 2003, pp:1305-1312, Nice, France.
[82]I. Haritaoglu, D. Harwood, and L. S. Davis, W4s:A real time system for detecting and tracking people in 21/2 d [C], European Conference on Computer Vision Computer Vision, Freiburg, Germany,1998.
[83]C. Anderson, P. Bert and G.W. Vander, Change detection and tracking using pyramids transformation techniques [C]. Proceedings SPIE Conference on Intelligent Robots and Computer Vision, Cambridge, MA,1985,579:72-78.
[84]A. Lipton, H. Fujiyoshi and R. Patil, Moving target classification and tracking from real-time video [C], Proceedings of IEEE Workshop on Applications of Computer Vision, Princeton, NJ,1998, pp:8-14.
[85]D. Meyer, J. Denzler and H. Niemann, Model based extraction of articulated objects in image sequences for gait analysis [C], Proceedings IEEE International Conference on Image Processing, Santa Barbara, California 1997, pp:78-81.
[86]J. Barron, D. Fleet and S. Beauchemin, Performance of optical flow techniques [J], International Journal of Computer Vision,1994,12 (1):42-77.
[87]A. Verri, S. Uras and E. DeMicheli, Motion segmentation from optical flow [C], Proceedings of the 5th Alvey Vision Conference, Brighton, UK,1989, pp:209-214.
[88]N. Friedman and S. Russell, Image segmentation in video sequences:a probabilistic approach [C], In Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI), Rhode Island, USA,1997.
[89]W.E.L. Grimson, C. Stauffer, R. Romano, and L. Lee, Using adaptive tracking to classify and monitor activities in a site [C], CVPR, Santa Barbara, CA, June 1998.
[90]C. Stauffer, W.E.L. Grimson, Learning patterns of activity using real-time tracking [J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8): 747-57.
[91]M. Harville, A framework for high-level feedback to adaptive, per-pixel, mixture of gaussian background models [C], ECCV, page Ⅲ:543 ff., Copenhagen, Denmark, May 2002.
[92]Z. Zoran, V. H. Ferdinand, Efficient adaptive density estimation per image pixel for the task of background subtraction [J], Pattern Recognition Letters,2006,27(7): 773-780.
[93]J. Ma, S. Fu, On the correct convergence of the em algorithm for gaussian mixtures [J], Pattern Recognition,2005,38(12):2602-2611.
[94]A. Elgammal, D. Harwood, and L. Davis, Non-parametric model for background subtraction, ECCV, pages Ⅱ:751-767, Dublin, Ireland, May 2000.
[95]A. Mittal, N. Paragios, Motion-based background subtraction using adaptive kernel density estimation [C], Proceedings of the Conference on Computer Vision and Pattern Recognition,2004.
[96]M. Isard and A. Blake, Condensation conditional density propagation for visual tracking [J], International Journal of Computer Vision,1998,29 (1):5-28.
[97]A. Lipton, H. Fujiyoshi and R. Patil, Moving target classification and tracking from real-time video [C], Proceedings IEEE Workshop on Applications of Computer Vision, Princeton, NJ,1998, pp:8-14.
[98]Y. Kuno, T. Watanabe, Y. Shimosakoda and S. Nakagawa, Automated detection of human for visual surveillance system [C], Proceedings of IEEE International Conference on Pattern Recognition, Vienna,1996, pp:865-869.
[99]R. Cutlerand L. Davis, Robust real-time periodic motion detection, analysis, and applications [J], IEEE Transaction Pattern Analysis and Machine Intelligence,2000, 22 (8):781-796.
[100]A. Selinger and L. Wixson, Classifying moving objects as rigid or non-rigid without correspondences [C], Proceedings of DAPRA Image Understanding Workshop, Monterey, CA,1998,1:341-358.
[101]I. Haritaoglu, D. Harwood and L. Davis, W4:real-time surveillance of people and their activities [J], IEEE Transaction Pattern Analysis and Machine Intelligence, 2000,22 (8):809-830.
[102]B. Bhanu and J. Han, Individual recognition by kinematic-based gait analysis [C], Proceedings of International Conference on Pattern Recognition, Quebec, Canada, 2002, pp:343-346.
[103]B. Bhanu and J. Han, Human recognition on combining kinematic and stationary features [C], Proceedings International Conference Audio-and Video-Based Biometric Person Authentication,2003, Vol.2688, Lecture Notes in Computer Science, pp:600-608.
[104]L. Wang, T. Tan, H. Ning, and W. Hu, Fusion of static and dynamic body biometrics for gait recognition, IEEE Transaction Circuits System Video Technology [J],2004,14(2),149-158.
[105]D.K. Wagg and M.S. Nixon, On automated model-based extraction and analysis of gait[C], Proceedings International Conference Automatic Face and Gesture Recognition,2004, pp:11-16.
[106]C. BenAbdelkader, R. Cutler and L. Davis, Stride and cadence as a biometric in automatic person identification and verification [C], Proceedings International Conference on Automatic Face and Gesture Recognition,2002, pp:372-377.
[107]J. Princen, J. Illingworth, J. Kittler, A hierarchical approach to line extraction based on the Hough transform [J], Computer Vision, Graphics Image Processings, 1990(52):57-77.
[108]J. M. Nash, J. N. Carter and M. S. Nixon, Dynamic feature extraction via the velocity Hough transform, Pattern Recognition Letters [J],1997,18(10):1035-1047.
[109]D. Cunado, M. S. Nixon and J. N. Carter, Automatic gait recognition via model-based evidence gathering [C], Proceedings of IEEE Workshop on Automated ID Technologies,1999, pp:27-30.
[110]D. Cunado, M. S. Nixon, and J. N. Carter, Automatic extraction and description of human gait models for recognition purposes [J], Computer Vision Image Understanding,2003,90 (1):1-41.
[111]C. Y. Yam, M. S. Nixon and J. N. Carter, Extended model-based automatic gait recognition of walking and running [C], Proceedings of International Conferrence on Audio-and Video-based Biometric Person Authentication [C],2001, pp:278-283.
[112]C. Y. Yam, M. S. Nixon, and J. N. Carter, Automated person recognition by walking and running via model-based approaches, Pattern Recognition [J],2004,37(5): 1057-1072.
[113]A. Kale, A. Rajagopalan, N. Cuntoor and V. Kruger, Gait-based recognition of humans using continuous HMMs [C], Proceedings of International Conferrence on Automatic Face and Gesture Recognition,2002.
[114]A. Kale, A. N. Rajagopalan, A. Sundaresan, N. Cuntoor, A. Roy-Chowdhury, V. Kruger, and R. Chellappa, Identification of humans using gait [J], IEEE Transaction Image Processing,2004, Vol.13, no.9, pp:1163-1173.
[115]A. Sundaresan, A. Roy-Chowdhury, and R. Chellappa, A hidden Markov model based framework for recognition of humans from gait sequences [J], Proceedings IEEE International Conferrence Image Processing,2003, pp:143-150.
[116]C. BenAbdelkader, R. Cutler, H. Nanda and L. Davis, Eigen Gait:motion based recognition of people using image self-similarity [C], Proceedings of International Conferrence on Audio-and Video-based Biometric Person Authentication,2001, pp: 284-294.
[117]C. BenAbdelkader, Motion-based recognition of people in EigenGait space [C], Proceedings of IEEE Face and Gesture Recognition,2002, pp:254-259.
[118]I. R. Vega and S. Sarkar, Statistical motion model based on the change of feature relationships:human gait-based recognition, IEEE Transaction Pattern Analysis and Machine Intelligence,2003,25 (10):1323-1328.
[119]Z. Liu and S. Sarkar, Simplest representation yet for gait recognition:Averaged silhouette [C], Proceedings 17th International Conferrence Pattern Recognition, 2004, Vol.4, pp:211-214.
[120]D. Tolliver and R. Collins, Gait shape estimation for identification[C], Proceedings International Conferrence Audio and Video-Based Biometric Person Authentication, 2003, Vol.2688, Lecture Notes in Computer Science, pp:734-742.
[121]J. D. Shutler and M. S. Nixon, Zernike velocity moments for description and recognition of moving shapes [C], Proceedings BMVC,2001, pp:705-714.
[122]J. B. Hayfron-Acquah, M. S. Nixon, and J. N. Carter, Automatic gait recognition by symmetry analysis [J], Pattern Recognition Letter,2003,24(13), pp:2175-2183.
[123]J. P. Foster, M. S. Nixon, and A. Prugel-Bennet, Automatic gait recognition using area-based metrics [J], Pattern Recognit. Letter,2003, Vol.24, pp:2489-2497.
[124]L. Wang, W. M. Hu and T. N. Tan, A new attempt to gait-based human identification [C], Proceedings of International Conferrence on Pattern Recognition, 2002.
[125]L. Wang, T. N. Tan, W. M. Hu, and H. Z. Ning, Automatic gait recognition based on statistical shape analysis [J], IEEE Transaction Image Processing,2003,12(9), pp: 1120-1131.
[126]L. Wang, T. Tan, H. Ning, and W. Hu, Silhouette analysis-based gait recognition for human identification [J], IEEE Transaction Pattern Analysis and Machine Intelligence,2003,25 (12):1505-1518.
[127]L. Lee and W. Crimson, Gait analysis for recognition and classification [C], Proceeding of International Conferrence on Automatic Face and Gesture Recognition,2002.
[128]D. Cunado, M. S. Nixon and J. N.Carter, Using gait as a biometric:via phase-weighted magnitude spectra [C], Lecture Notes in Computer Science, vol 1206,1997, pp:95.
[129]P.S. Huang, C.J. Harris, and M.S. Nixon, Recognizing humans by gait via parameteric canonical space [J]. Artificial Intelligence in Engineering,1999, Vol.13, pp:359-366.
[130]A.M. Martinez and A.C. Kak, PCA versus LDA [J], IEEE Transaction on Pattern Analysis and Machine Intelligence,2001,23(2), pp:228-233.
[131]X.L. Li, S. Lin, S.C. Yan and D. Xu, Discriminant locally linear embedding with high-order tensor data [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2008,38(2):342-352.
[132]D.C. Tao, X.L. Li, X.D. Wu and S.J. Maybank. General tensor analysis and Gabor features for gait recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(10):1700-1715.
[133]I. Joliffe, Principal Component Analysis [M]. Springer-Verlag,1986.
[134]H. Murase and R. Sakai, Moving object recognition in eigenspace representation: gait analysis and lip reading, Pattern Recognition Letters [J],1996,17:155-162.
[135]S. A. Niyogi and E. H. Adelson, Analyzing gait with spatiotemporal surface, in Proc. of IEEE Workshop on Motion of Non-rigid and Articulated Objects [C],1994, pp: 64-69.
[136]R. G. Cutler, On the detection, analysis, and applications of oscillatory motions in video sequences, Ph.D. thesis, University of Maryland, College Park, Md, USA, 2000.
[137]S. A. Niyogi and E. H. Adelson, Analyzing and recognizing walking figures in XYT [C], in Proceedings of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition,1994, pp.469-474.
[138]F. Liu, R. W. Picard. Periodicity, directionality, and randomness:wold features for image modeling and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence,18(7):722-733, July,1996.
[139]L. J. Latecki and R. Lakamper. Convexity rule for shape decomposition based on discrete contour evolution [J]. Computer Vision and Image Understanding, vol.73, pp.441-454,1999.
[140]Y. Y. Zhang and P. S. P. Wang, A parallel thinning algorithm with two-subiteration that generates one-pixel-wide skeletons [C], ICPR, pp:457-461,1996.
[141]C. W. Niblack, P. B. Gibbons, and D. W. Capson. Generating skeletons and centerlines from the distance transform [J]. CVGIP:Graphical Models and Image Processing, vol.54, no.5, pp.420-437, Sept.1992.
[142]Y. Ge and J.M. Fitzpatrick, On the generation of skeletons from discrete euclidean distance maps [J], IEEE Trans. Pattern Analysis and Machine Intelligence, vol.18, no.11, pp.1055-1066, Nov.1996.
[143]W. P. Choi, K. M. Lam, and W. C. Siu. Extraction of the euclidean skeleton based on a connectivity criterion [J]. Pattern Recognition, vol.36, pp.721-729,2003.
[144]T. Moeslund and E. Granum. A survey of computer vision-based human motion capture[J]. Computer Vision and Image Understanding, vol.81, pp.231-268,2001.
[145]G. Veres, J. N. Carter, and M. S. Nixon, What image information is important in silhouette-based gait recognition [C], in Proc. IEEE Computer Vision and Pattern Recognition 2004, vol.2, pp.776-782.
[146]H. Blum. A transformation for extracting new descriptors of shape, models for the perception of speech and visual form [M]. MIT Press, pp.363-380,1967.
[147]H. Blum. Biological Shape and Visual Science:Part I. J. Theoretical Biology,1973, 38:205-287.
[148]J. Serra. Image Analysis and Mathematical Morphology, vol.1. San Diego: Academic Press,1982.
[149]丁颐.基于距离变换的连通骨架算法研究[硕士论文].武汉：华中科技大学,2004.
[150]J.A. Webb and J.K. Aggarwal, Structure from Motion of Rigid and Jointed Objects [J], Artificial Intelligence, vol.19, pp.107-130,1982.
[151]R. Tanawongsuwan and A. Bobick, Gait recognition from time-normalized joint-angle trajectories in the walking plane [C], Proc. IEEE Computer Vision and Pattern Recognition 2001, pp.726-731.
[152]D. Shaken and A.M. Bruckstein, Pruning Medial Axes [J], Computer Vision and Image Understanding, vol.69, no.2, pp.156-169,1998.
[153]A. D. Ward and G. Hamarneh, The Groupwise Medial Axis Transform for Fuzzy Skeletonization and Pruning [J], IEEE Trans. Pattern Analysis and Machine Intelligence,2009.
[154]X. Bai, L. J. Latecki, and W.-Y. Liu, Skeleton pruning by contour partitioning with discrete curve evolution [J], IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, no.3, pp.449-462,2007.
[155]A. Veeraraghavan, A.R. Chowdhury, and R. Chellappa, Matching shape sequences in video with applications in human movement analysis [J]. IEEE Trans. Pattern Analysis and Machine Intelligence,2005,27 (12):1896-1909.
[156]Q. He and C. Debrunner, Individual recognition from periodic activity using Hidden Markov Models [C], in Proc. of IEEE Workshop on Human Motion,2000.
[157]S.D. Mowbry and M.S. Nixon, Automatic gait recognition via Fourier descriptors of deformable objects [C]. Proc. Conf. Audio Visual Biometric Person Authentication, 2003,566-573.
[158]A.F. Bobick and J.W. Davis, The Recognition of Human Movement Using Temporal Templates [J], IEEE Trans. Pattern Analysis and Machine Intelligence, 2001,23(3):257-267.
[159]A.F. Bobick and J.W. Davis, Extraction and Recognition of Periodically Deforming Objects by Continuous, Spatio-temporal Shape Description [C], ICPR,2004, pp: 157-162.
[160]A. Veeraraghavan, A. Roy-Chowdhury, and R. Chellappa, Role of shape and kinematics in human movement analysis [C], Proc. IEEE Computer Vision and Pattern Recognition 2004, vol.1, pp.730-737.
[161]M. Turk and A. Pentland, Face recognition using eigenfaces [C], Proc. Conf. Computer Vision and Pattern Recognition,1991,586-591.
[162]P. Belhumeur, J. Hespanha, and D. Kriegman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection [J], IEEE Trans. Pattern Analysis and Machine Intelligence [J],1997,19(7),711-720.
[163]C. Angeloni, P.O. Riley, D.E. Krebs, Frequency content of whole body gait kinematic data [J], IEEE Trans. Rehabilitation Eng.2 (1) (1994) 40-46.
[164]J. Boyd, Video phase-locked loops in gait recognition, in Proc. of Intl. Conf. on Computer Vision [C],2001.
[165]Dacheng Tao, Xuelong Li, X.D. Wu, S.J. Maybank. Geometric mean for subspace selection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009, 31(2):260-274.
[166]Wei Liu, Dacheng Tao, J.Z. Liu. Transductive Component Analysis [C]. IEEE International Conference on Data Mining, ICDM, pp.433-442,2008.
[167]Dong Xu, Shuicheng Yan, S. Lin and H.J. Zhang. Marginal fisher analysis and its variants for human gait recognition and content-based image retrieval [J]. IEEE Transactions on Image Processing,2007,16(11):2811-2821.
[168]Dacheng Tao, Xuelong Li, X.D. Wu, S.J. Maybank. Elapsed Time in Human Gait Recognition:a New Approach [C]. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, pp.177-180,2006.
[169]R.A. Fisher, The Use of Multiple Measurements in Taxonomic Problems [J], Annals of Eugenics,1936, vol.7:179-188.
[170]Tianhao Zhang, Xuelong Li, Dacheng Tao and Jie Yang. Multimodal Biometrics Using Geometry Preserving Projections [J], Pattern Recognition,2008,41(3): 805-813.
[2]景英娟,董育宁.生物特征识别技术综述[J].电子学报,2001,29(12A)：1744-1748.
[3]J. L. Wayman. Digital signal processing in biometric identification:a review [C]. International Conference on Image processing,2002, pp:37-40.
[4]X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, Face recognition using laplacianfaces, IEEE Transaction Pattern Analysis and Machine Intelligence [J], 2005,27(3),328-340.
[5]J. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Transaction Pattern Analysis and Machine Intelligence [J], 1993,15(11):1148-1161.
[6]L. Ma, T. Tan, Y. Wang, and D. Zhang, Personal identification based on iris texture analysis, IEEE Transaction Pattern Analysis and Machine Intelligence [J],2003, 25(12):1519-1533.
[7]D. J. Hurley, M. S. Nixon, J. N. Carter, Force field feature extraction for ear biometrics, Computer Vision and Image Understanding [J],2005.
[8]A. K. Jain, L. Hong, S. Pankanti and R. Bolle, An identity verification system using fingerprints. IEEE Proceedings [C],1999,85(9), pp:1365-1388.
[9]Y. Qi and B. R. Hunt, A multi-resolution approach to computer verification of handwritten signatures. IEEE Transaction Image Processing [J],1995,4(6): 870-874.
[10]M.S. Nixon and J.N. Carter, Advances in automatic gait recognition. Proc. Int. Conf. Automatic Face and Gesture Recognition [C],2004, pp:139-146.
[11]N. V. Boulgouris, D. Hatzinakos and K. N. Plataniotis, Gait Recognition:A challenging signal processing technology for biometric identification. IEEE Signal Processing [J],2005,22(6):78-90.
[12]J. E. Cutting and L. T. Kozlowski, Recognizing friends by their walk:gait perception without familiarity cues, Psychonomic Society [J],1977,9(5):353-360.
[131 Available:http://www.darpa.mil/iao/HID.htm.
[14]S. Ounpuu, The biomechanics of walking and running, Clin. Sports Med. [J],1994, 13(4):843-863.
[15]M. P. Murray, A. B. Drought and R. C. Kory, Walking patterns of normal men, Journal of Bone and Joint Surgery [J],1964,46-A(2):335-360.
[16]M. P. Murray, Gait as a total pattern of movement, American Journal of Physical Medicine [J],1967,46(1):290-332.
[17]M.W. Whittle, Clinical gait analysis:a review, Human Movement Science [J],1996, Vol.15, pp:369-387.
[18]D. Winter, The biomechanics and motor control of human gait,2nd Ed [M]. Ottawa, Canada:Waterloo Biomechanics,1991.
[19]J. Aggarwal and Q. Cai, Human motion analysis:a review, Computer Vision and Image Understanding [J],1999, Vol.73, pp:428-440.
[20]T. Moeslund and E. Granum, A survey of computer vision-based human motion capture, Computer Vision and Image Understanding,2001, Vol.81, pp:231-268.
[21]D. Gavrila, The visual analysis of human movement:a survey, Computer Vision and Image Understanding [J],1999,73(1):82-98.
[22]王亮,胡卫明,谭铁牛.人运动的视觉分析综述[J].计算机学报,2002,25(3)：225-237.
[23]R. Collins et. al., A system for video surveillance and monitoring:VSAM final report, Carnegie Mellon University, Technical Report:CMU-RI-TR-00-12,2000.
[24]P. J. Phillips, S. Sarkar, I. Robledo, et. al., The gait identification challenge problem: data sets and baseline algorithm, International Conference on Pattern Recognition [C],2002, pp:385-388.
[25]S. Sarkar, P.J. Phillips, Z. Liu, I. R. Vega, P. Grother, and K.W. Bowyer,The human ID gait challenge problem:data sets, performance, and analysis. IEEE Transacyion Pattern Analysis and Machine Intelligence [J],2005,27 (2):162-177.
[26]R. Gross, J. Shi,The CMU motion of body (MOBO) database, Technical Report CMU-RI-01-18, Robotics Institute, Carnegie Mellon University,2001.
[27]S. Yu, D. Tan, T. Tan, et. al., A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition[C]. International Conference on Pattern Recognition,2006, pp:441-444.
[28]G. Johansson, Visual perception of biological motion and a model for its analysis, Perception Psychophysics [J],1973,14(2):201-211.
[29]C. Cedras and M. Shah, A survey of motion analysis from moving light displays, Proc. of Int. Conf. on Computer Vision and Pattern Recognition [C],1994, pp: 214-221.
[30]C. Barclay, J. Cutting and L. Kozlowski, Temporal and spatial factors in gait perception that influence gender recognition, Perception and Psychophysics [J], 1978,23(2):145-152.
[31]X.L. Li, S.J. Maybank, S.C. Yan, D.C. Tao and D. Xu, Gait components and their application to gender recognition[J], IEEE T-SMC, Part C,2008,38(2):145-155.
[32]G. Mather and L. Murdock, Gender discrimination in biological motion displays based on dynamic cues [C], Proceedings of Royal Society London,1994, B258: 273-279.
[33]J. E. Cutting, D. R. Proffitt and L. Kozlowski, A biochemical invariant for gait perception and performance, Journal of Experimental Psychology:Human Perception 1978, Vol.4, pp:357-372.
[34]D. Hoffman and B. Flinchbaugh, The interpretation of biological motion, Biological Cybernetics [J],1982.
[35]K. Akita, Image sequence analysis of real world human motion, Pattern Recognition [J],1984,17(1):73-83.
[36]L. Wang, W. M. Hu and T. N. Tan, Recent developments in human motion analysis, Pattern Recognition [J],2003,36(3):585-601.
[37]K. Fukunaga, Introduction to statistical pattern recognition [M], New York Academic Press, New York, NY, USA,1990.
[38]R. Duda, P. Hart, and D. Stork, Pattern classification [M], John Wiley & Sons,2001.
[39]M. Turk and A. Pentland, Face recognition using eigenfaces. Proceedings Conf. Computer Vision and Pattern Recognition [C],1991, pp:586-591.
[40]P. Belhumeur, J. Hespanha, and D. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Transaction Pattern Analysis and Machine Intelligence [J],1997,19(7),711-720.
[41]D. Meyer, Human gait classification based on Hidden Markov Models, in "3D Image Analysis and Synthesis" edited by H.P. Seidel, B. Girod and H. Niemann, Erlangen,1997, pp:39-146.
[42]A. Bissacco, A. Chiuso, Y. Ma and S. Soatto, Recognition of human gaits, in Proc. of Intl. Conf. on Computer Vision and Pattern Recognition,2001.
[43]J. Han and B. Bhanu, Individual recognition using gait energy image [J], IEEE Transaction on Pattern Analysis and Machine Intelligence,2006,28 (2):316-322.
[44]T.H. Zhang, D.C. Tao and J. Yang. Discriminative Locality Alignment. ECCV Part Ⅰ,
pp:725-738,2008.
[45]D.C. Tao, X.L. Li, X.D. Wu, S.J. Maybank, General tensor analysis and Gabor features for gait recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(10):1700-1715.
[46]D.C. Tao, X.L. Li, X.D. Wu, S.J. Maybank, Geometric mean for subspace selection[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2009, 31(2):260-274.
[47]S. V. Stevenage, M. S. Nixon and K. Vince, Visual analysis of gait as a cue to identity, Applied Cognitive Psychology [J],1999,13:513-526.
[48]H. Lee and Z. Chen, Determination of 3D human body postures from a single view[J], Computer Vision, Graphics and Image Processing,1985,30:148-168.
[49]J.A. Webb and J.K. Aggarwal, Structure from motion of rigid and jointed objects[J], Artificial Intelligence,1982, Vol.19, pp:107-130.
[50]K. Rohr, Towards model-based recognition of human movements in image sequences, Computer Vision, Graphics and Image Processing [J],59(1):1994,94-115.
[51]A. Kakadiaris and D. Metaxas, Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection, in Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition [C],1996, pp:81-87.
[52]C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, Pfinder:real-time tracking of the human body, IEEE Transaction on Pattern Analysis and Machine Intelligence, 1997,19(7):780-785.
[53]T.H. Zhang, X.L. Li, D.C. Tao and J. Yang, Multimodal biometrics using geometry preserving projections [J], Pattern Recognition,2008,41(3):805-813.
[54]J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas, On combining classifiers, IEEE Transaction Pattern Analysis and Machine Intelligence [J],1998,20(3):226-239.
[55]G. Shakhnarovich, L. Lee, and T. Darrell, Integrated face and gait recognition from multiple views, Proc. IEEE Computer Vision and Pattern Recognition,2001, pp: 439-476.
[56]R. Chellappa, A. K. Roy-Chowdhury, A. Kale, Identification using gait and face[J], IEEE Conference on Computer Vision and Pattern Recognition,2007, pp:147-152.
[57]X. Zhou, B. Bhanu, Integrating face and gait for human recognition at a distance, IEEE Transactions on Systems, Man, and Cybernetics,2007,37(5):1119-1137.
[58]Z. liu, S. Sarkar, Outdoor recognition at a distanee by fusing gait and face[J], Image and Vision Computing,2007,25(6):817-532.
[59]D. M. Gavrila and L. S. Davis,3-D model-based tracking of humans in action:a multi-view approach, in Proceedings of International Conference on Computer Vision and Pattern Recognition [C],1996, pp:73-80.
[60]B. Flinchbaugh and B. Chandrasekaran, A theory of spatio-temporal aggregation for vision [J], Artificial Intelligence,1981, Vol.17, pp:387-407.
[61]A. Hilton and P. Fua, Modeling people toward vision-based understanding of a person's shape, Appearance, and Movement [J], Computer Vision and Image Understanding,2001, Vol.81, pp:227-230.
[62]R. Collins, R. Gross, and J. Shi, Silhouette-based human identification from body shape and gait, in Proceedings of IEEE International Conference Face and Gesture Recognition,2002, pp:366-371.
[63]Y. Liu, R. Collins, and Y. Tsin, Gait sequence analysis using frieze patterns, in Proceedings Europe Conference Computer Vision,2002, pp:657-671.
[64]R. Urtasun, P. Fua,3D tracking for gait characterization and recognition [C], IEEE International Conferenee on Automatic Face and Gesture Recognition,2004, pp: 17-22.
[65]A. Kale, A.K.R. Chowdhury, R. Chellappa, Towards a view invariant gait recognition algorithm [C], IEEE Conferenee on Advanced Video and Signal Based Surveillance,2003, pp:143-150.
[66]A. Johnson and A. Bobick, A multi-view method for gait recognition using static body parameters[C], in Proceedings of International Conference on Audio-and Video-based Biometric Person Authentication,2001, pp:301-311.
[67]A. F. Bobick and A. Y. Johnson, Gait recognition using static, activity-specific parameters [C], Proceedings of IEEE Computer Vision and Pattern Recognition, 2001, Vol.1,pp:423-430.
[68]J. Little and J. Boyd, Recognizing people by their gait:the shape of motion, Videre: Journal of Computer Vision Research [J],1998,1(2):2-32.
[69]J. Boyd, Video phase-locked loops in gait recognition, in Proceedings of International Conference on Computer Vision [C],2001.
[70]G. Zhao, R. Chen, G. Liu, and L. Hua, Amplitude spectrum-based gait recognition [C], in Proceedings of IEEE International Conference Face and Gesture Recognition 2004, pp:23-28.
[71]R. Tanawongsuwan and A. Bobick, Gait recognition from time-normalized joint-angle trajectories in the walking plane [C], Proceedings of IEEE Computer Vision and Pattern Recognition 2001, pp:726-731.
[72]R. G. Cutler, On the detection, analysis, and applications of oscillatory motions in video sequences, Ph.D. thesis, University of Maryland, College Park, Md, USA, 2000.
[73]C. BenAbdelkader, R. Culter and L. Davis, Person identification using automatic height and stride estimation, in Proceedings of IEEE International Conference on Pattern Recognition [C],2002.
[74]K. Karman and A. Brandt, Moving object recognition using an adaptive background memory [M], In:V Cappellini, Time-varying Image Processing and Moving Object Recognition.2. Elsevier,1990.
[75]M. Kilger, A shadow handler in a video-based real-time traffic monitoring system [C], Proceedings of IEEE Workshop on Applications of Computer Vision, Palm Springs, CA,1992, pp:1060-1066.
[76]Dieter Koller, Joseph Weber, and Jitendra Malik, Robust multiple car tracking with occlusion reasoning [C], Europe Conference of Computer Vinsion,1994, pp: 189-196, Stockholm, Sweden.
[77]K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, Wallflower:principles and practice of background maintenance [C], International Conference of Computer Vision,1999, pp:255-261, Kerkyra, Greece.
[78]N.M. Oliver, B. Rosario, and A.P. Pentland, A bayesian computer vision system for modeling human interactions [J], IEEE Transaction on Pattern Analysis and Machine Intelligence,2000,22(8):831-843.
[79]J. Zhong and S. Sclaroff, Segmenting foreground objects from a dynamic, textured background via a robust kalman filter [C], International Conference of Computer Vision,2003, pp:44-50, Nice, France.
[80]G. Doretto, A. Chiuso, Y.N. Wu, and S. Soatto. Dynamic textures [C], International Journal Computer Vision,2003,51(2):91-109.
[81]A. Monnet, A. Mittal, N. Paragios, and V. Ramesh, Background modeling and subtraction of dynamic scenes [C], International Conference of Computer Vision, 2003, pp:1305-1312, Nice, France.
[82]I. Haritaoglu, D. Harwood, and L. S. Davis, W4s:A real time system for detecting and tracking people in 21/2 d [C], European Conference on Computer Vision Computer Vision, Freiburg, Germany,1998.
[83]C. Anderson, P. Bert and G.W. Vander, Change detection and tracking using pyramids transformation techniques [C]. Proceedings SPIE Conference on Intelligent Robots and Computer Vision, Cambridge, MA,1985,579:72-78.
[84]A. Lipton, H. Fujiyoshi and R. Patil, Moving target classification and tracking from real-time video [C], Proceedings of IEEE Workshop on Applications of Computer Vision, Princeton, NJ,1998, pp:8-14.
[85]D. Meyer, J. Denzler and H. Niemann, Model based extraction of articulated objects in image sequences for gait analysis [C], Proceedings IEEE International Conference on Image Processing, Santa Barbara, California 1997, pp:78-81.
[86]J. Barron, D. Fleet and S. Beauchemin, Performance of optical flow techniques [J], International Journal of Computer Vision,1994,12 (1):42-77.
[87]A. Verri, S. Uras and E. DeMicheli, Motion segmentation from optical flow [C], Proceedings of the 5th Alvey Vision Conference, Brighton, UK,1989, pp:209-214.
[88]N. Friedman and S. Russell, Image segmentation in video sequences:a probabilistic approach [C], In Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI), Rhode Island, USA,1997.
[89]W.E.L. Grimson, C. Stauffer, R. Romano, and L. Lee, Using adaptive tracking to classify and monitor activities in a site [C], CVPR, Santa Barbara, CA, June 1998.
[90]C. Stauffer, W.E.L. Grimson, Learning patterns of activity using real-time tracking [J], IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8): 747-57.
[91]M. Harville, A framework for high-level feedback to adaptive, per-pixel, mixture of gaussian background models [C], ECCV, page Ⅲ:543 ff., Copenhagen, Denmark, May 2002.
[92]Z. Zoran, V. H. Ferdinand, Efficient adaptive density estimation per image pixel for the task of background subtraction [J], Pattern Recognition Letters,2006,27(7): 773-780.
[93]J. Ma, S. Fu, On the correct convergence of the em algorithm for gaussian mixtures [J], Pattern Recognition,2005,38(12):2602-2611.
[94]A. Elgammal, D. Harwood, and L. Davis, Non-parametric model for background subtraction, ECCV, pages Ⅱ:751-767, Dublin, Ireland, May 2000.
[95]A. Mittal, N. Paragios, Motion-based background subtraction using adaptive kernel density estimation [C], Proceedings of the Conference on Computer Vision and Pattern Recognition,2004.
[96]M. Isard and A. Blake, Condensation conditional density propagation for visual tracking [J], International Journal of Computer Vision,1998,29 (1):5-28.
[97]A. Lipton, H. Fujiyoshi and R. Patil, Moving target classification and tracking from real-time video [C], Proceedings IEEE Workshop on Applications of Computer Vision, Princeton, NJ,1998, pp:8-14.
[98]Y. Kuno, T. Watanabe, Y. Shimosakoda and S. Nakagawa, Automated detection of human for visual surveillance system [C], Proceedings of IEEE International Conference on Pattern Recognition, Vienna,1996, pp:865-869.
[99]R. Cutlerand L. Davis, Robust real-time periodic motion detection, analysis, and applications [J], IEEE Transaction Pattern Analysis and Machine Intelligence,2000, 22 (8):781-796.
[100]A. Selinger and L. Wixson, Classifying moving objects as rigid or non-rigid without correspondences [C], Proceedings of DAPRA Image Understanding Workshop, Monterey, CA,1998,1:341-358.
[101]I. Haritaoglu, D. Harwood and L. Davis, W4:real-time surveillance of people and their activities [J], IEEE Transaction Pattern Analysis and Machine Intelligence, 2000,22 (8):809-830.
[102]B. Bhanu and J. Han, Individual recognition by kinematic-based gait analysis [C], Proceedings of International Conference on Pattern Recognition, Quebec, Canada, 2002, pp:343-346.
[103]B. Bhanu and J. Han, Human recognition on combining kinematic and stationary features [C], Proceedings International Conference Audio-and Video-Based Biometric Person Authentication,2003, Vol.2688, Lecture Notes in Computer Science, pp:600-608.
[104]L. Wang, T. Tan, H. Ning, and W. Hu, Fusion of static and dynamic body biometrics for gait recognition, IEEE Transaction Circuits System Video Technology [J],2004,14(2),149-158.
[105]D.K. Wagg and M.S. Nixon, On automated model-based extraction and analysis of gait[C], Proceedings International Conference Automatic Face and Gesture Recognition,2004, pp:11-16.
[106]C. BenAbdelkader, R. Cutler and L. Davis, Stride and cadence as a biometric in automatic person identification and verification [C], Proceedings International Conference on Automatic Face and Gesture Recognition,2002, pp:372-377.
[107]J. Princen, J. Illingworth, J. Kittler, A hierarchical approach to line extraction based on the Hough transform [J], Computer Vision, Graphics Image Processings, 1990(52):57-77.
[108]J. M. Nash, J. N. Carter and M. S. Nixon, Dynamic feature extraction via the velocity Hough transform, Pattern Recognition Letters [J],1997,18(10):1035-1047.
[109]D. Cunado, M. S. Nixon and J. N. Carter, Automatic gait recognition via model-based evidence gathering [C], Proceedings of IEEE Workshop on Automated ID Technologies,1999, pp:27-30.
[110]D. Cunado, M. S. Nixon, and J. N. Carter, Automatic extraction and description of human gait models for recognition purposes [J], Computer Vision Image Understanding,2003,90 (1):1-41.
[111]C. Y. Yam, M. S. Nixon and J. N. Carter, Extended model-based automatic gait recognition of walking and running [C], Proceedings of International Conferrence on Audio-and Video-based Biometric Person Authentication [C],2001, pp:278-283.
[112]C. Y. Yam, M. S. Nixon, and J. N. Carter, Automated person recognition by walking and running via model-based approaches, Pattern Recognition [J],2004,37(5): 1057-1072.
[113]A. Kale, A. Rajagopalan, N. Cuntoor and V. Kruger, Gait-based recognition of humans using continuous HMMs [C], Proceedings of International Conferrence on Automatic Face and Gesture Recognition,2002.
[114]A. Kale, A. N. Rajagopalan, A. Sundaresan, N. Cuntoor, A. Roy-Chowdhury, V. Kruger, and R. Chellappa, Identification of humans using gait [J], IEEE Transaction Image Processing,2004, Vol.13, no.9, pp:1163-1173.
[115]A. Sundaresan, A. Roy-Chowdhury, and R. Chellappa, A hidden Markov model based framework for recognition of humans from gait sequences [J], Proceedings IEEE International Conferrence Image Processing,2003, pp:143-150.
[116]C. BenAbdelkader, R. Cutler, H. Nanda and L. Davis, Eigen Gait:motion based recognition of people using image self-similarity [C], Proceedings of International Conferrence on Audio-and Video-based Biometric Person Authentication,2001, pp: 284-294.
[117]C. BenAbdelkader, Motion-based recognition of people in EigenGait space [C], Proceedings of IEEE Face and Gesture Recognition,2002, pp:254-259.
[118]I. R. Vega and S. Sarkar, Statistical motion model based on the change of feature relationships:human gait-based recognition, IEEE Transaction Pattern Analysis and Machine Intelligence,2003,25 (10):1323-1328.
[119]Z. Liu and S. Sarkar, Simplest representation yet for gait recognition:Averaged silhouette [C], Proceedings 17th International Conferrence Pattern Recognition, 2004, Vol.4, pp:211-214.
[120]D. Tolliver and R. Collins, Gait shape estimation for identification[C], Proceedings International Conferrence Audio and Video-Based Biometric Person Authentication, 2003, Vol.2688, Lecture Notes in Computer Science, pp:734-742.
[121]J. D. Shutler and M. S. Nixon, Zernike velocity moments for description and recognition of moving shapes [C], Proceedings BMVC,2001, pp:705-714.
[122]J. B. Hayfron-Acquah, M. S. Nixon, and J. N. Carter, Automatic gait recognition by symmetry analysis [J], Pattern Recognition Letter,2003,24(13), pp:2175-2183.
[123]J. P. Foster, M. S. Nixon, and A. Prugel-Bennet, Automatic gait recognition using area-based metrics [J], Pattern Recognit. Letter,2003, Vol.24, pp:2489-2497.
[124]L. Wang, W. M. Hu and T. N. Tan, A new attempt to gait-based human identification [C], Proceedings of International Conferrence on Pattern Recognition, 2002.
[125]L. Wang, T. N. Tan, W. M. Hu, and H. Z. Ning, Automatic gait recognition based on statistical shape analysis [J], IEEE Transaction Image Processing,2003,12(9), pp: 1120-1131.
[126]L. Wang, T. Tan, H. Ning, and W. Hu, Silhouette analysis-based gait recognition for human identification [J], IEEE Transaction Pattern Analysis and Machine Intelligence,2003,25 (12):1505-1518.
[127]L. Lee and W. Crimson, Gait analysis for recognition and classification [C], Proceeding of International Conferrence on Automatic Face and Gesture Recognition,2002.
[128]D. Cunado, M. S. Nixon and J. N.Carter, Using gait as a biometric:via phase-weighted magnitude spectra [C], Lecture Notes in Computer Science, vol 1206,1997, pp:95.
[129]P.S. Huang, C.J. Harris, and M.S. Nixon, Recognizing humans by gait via parameteric canonical space [J]. Artificial Intelligence in Engineering,1999, Vol.13, pp:359-366.
[130]A.M. Martinez and A.C. Kak, PCA versus LDA [J], IEEE Transaction on Pattern Analysis and Machine Intelligence,2001,23(2), pp:228-233.
[131]X.L. Li, S. Lin, S.C. Yan and D. Xu, Discriminant locally linear embedding with high-order tensor data [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2008,38(2):342-352.
[132]D.C. Tao, X.L. Li, X.D. Wu and S.J. Maybank. General tensor analysis and Gabor features for gait recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(10):1700-1715.
[133]I. Joliffe, Principal Component Analysis [M]. Springer-Verlag,1986.
[134]H. Murase and R. Sakai, Moving object recognition in eigenspace representation: gait analysis and lip reading, Pattern Recognition Letters [J],1996,17:155-162.
[135]S. A. Niyogi and E. H. Adelson, Analyzing gait with spatiotemporal surface, in Proc. of IEEE Workshop on Motion of Non-rigid and Articulated Objects [C],1994, pp: 64-69.
[136]R. G. Cutler, On the detection, analysis, and applications of oscillatory motions in video sequences, Ph.D. thesis, University of Maryland, College Park, Md, USA, 2000.
[137]S. A. Niyogi and E. H. Adelson, Analyzing and recognizing walking figures in XYT [C], in Proceedings of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition,1994, pp.469-474.
[138]F. Liu, R. W. Picard. Periodicity, directionality, and randomness:wold features for image modeling and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence,18(7):722-733, July,1996.
[139]L. J. Latecki and R. Lakamper. Convexity rule for shape decomposition based on discrete contour evolution [J]. Computer Vision and Image Understanding, vol.73, pp.441-454,1999.
[140]Y. Y. Zhang and P. S. P. Wang, A parallel thinning algorithm with two-subiteration that generates one-pixel-wide skeletons [C], ICPR, pp:457-461,1996.
[141]C. W. Niblack, P. B. Gibbons, and D. W. Capson. Generating skeletons and centerlines from the distance transform [J]. CVGIP:Graphical Models and Image Processing, vol.54, no.5, pp.420-437, Sept.1992.
[142]Y. Ge and J.M. Fitzpatrick, On the generation of skeletons from discrete euclidean distance maps [J], IEEE Trans. Pattern Analysis and Machine Intelligence, vol.18, no.11, pp.1055-1066, Nov.1996.
[143]W. P. Choi, K. M. Lam, and W. C. Siu. Extraction of the euclidean skeleton based on a connectivity criterion [J]. Pattern Recognition, vol.36, pp.721-729,2003.
[144]T. Moeslund and E. Granum. A survey of computer vision-based human motion capture[J]. Computer Vision and Image Understanding, vol.81, pp.231-268,2001.
[145]G. Veres, J. N. Carter, and M. S. Nixon, What image information is important in silhouette-based gait recognition [C], in Proc. IEEE Computer Vision and Pattern Recognition 2004, vol.2, pp.776-782.
[146]H. Blum. A transformation for extracting new descriptors of shape, models for the perception of speech and visual form [M]. MIT Press, pp.363-380,1967.
[147]H. Blum. Biological Shape and Visual Science:Part I. J. Theoretical Biology,1973, 38:205-287.
[148]J. Serra. Image Analysis and Mathematical Morphology, vol.1. San Diego: Academic Press,1982.
[149]丁颐.基于距离变换的连通骨架算法研究[硕士论文].武汉：华中科技大学,2004.
[150]J.A. Webb and J.K. Aggarwal, Structure from Motion of Rigid and Jointed Objects [J], Artificial Intelligence, vol.19, pp.107-130,1982.
[151]R. Tanawongsuwan and A. Bobick, Gait recognition from time-normalized joint-angle trajectories in the walking plane [C], Proc. IEEE Computer Vision and Pattern Recognition 2001, pp.726-731.
[152]D. Shaken and A.M. Bruckstein, Pruning Medial Axes [J], Computer Vision and Image Understanding, vol.69, no.2, pp.156-169,1998.
[153]A. D. Ward and G. Hamarneh, The Groupwise Medial Axis Transform for Fuzzy Skeletonization and Pruning [J], IEEE Trans. Pattern Analysis and Machine Intelligence,2009.
[154]X. Bai, L. J. Latecki, and W.-Y. Liu, Skeleton pruning by contour partitioning with discrete curve evolution [J], IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, no.3, pp.449-462,2007.
[155]A. Veeraraghavan, A.R. Chowdhury, and R. Chellappa, Matching shape sequences in video with applications in human movement analysis [J]. IEEE Trans. Pattern Analysis and Machine Intelligence,2005,27 (12):1896-1909.
[156]Q. He and C. Debrunner, Individual recognition from periodic activity using Hidden Markov Models [C], in Proc. of IEEE Workshop on Human Motion,2000.
[157]S.D. Mowbry and M.S. Nixon, Automatic gait recognition via Fourier descriptors of deformable objects [C]. Proc. Conf. Audio Visual Biometric Person Authentication, 2003,566-573.
[158]A.F. Bobick and J.W. Davis, The Recognition of Human Movement Using Temporal Templates [J], IEEE Trans. Pattern Analysis and Machine Intelligence, 2001,23(3):257-267.
[159]A.F. Bobick and J.W. Davis, Extraction and Recognition of Periodically Deforming Objects by Continuous, Spatio-temporal Shape Description [C], ICPR,2004, pp: 157-162.
[160]A. Veeraraghavan, A. Roy-Chowdhury, and R. Chellappa, Role of shape and kinematics in human movement analysis [C], Proc. IEEE Computer Vision and Pattern Recognition 2004, vol.1, pp.730-737.
[161]M. Turk and A. Pentland, Face recognition using eigenfaces [C], Proc. Conf. Computer Vision and Pattern Recognition,1991,586-591.
[162]P. Belhumeur, J. Hespanha, and D. Kriegman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection [J], IEEE Trans. Pattern Analysis and Machine Intelligence [J],1997,19(7),711-720.
[163]C. Angeloni, P.O. Riley, D.E. Krebs, Frequency content of whole body gait kinematic data [J], IEEE Trans. Rehabilitation Eng.2 (1) (1994) 40-46.
[164]J. Boyd, Video phase-locked loops in gait recognition, in Proc. of Intl. Conf. on Computer Vision [C],2001.
[165]Dacheng Tao, Xuelong Li, X.D. Wu, S.J. Maybank. Geometric mean for subspace selection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009, 31(2):260-274.
[166]Wei Liu, Dacheng Tao, J.Z. Liu. Transductive Component Analysis [C]. IEEE International Conference on Data Mining, ICDM, pp.433-442,2008.
[167]Dong Xu, Shuicheng Yan, S. Lin and H.J. Zhang. Marginal fisher analysis and its variants for human gait recognition and content-based image retrieval [J]. IEEE Transactions on Image Processing,2007,16(11):2811-2821.
[168]Dacheng Tao, Xuelong Li, X.D. Wu, S.J. Maybank. Elapsed Time in Human Gait Recognition:a New Approach [C]. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, pp.177-180,2006.
[169]R.A. Fisher, The Use of Multiple Measurements in Taxonomic Problems [J], Annals of Eugenics,1936, vol.7:179-188.
[170]Tianhao Zhang, Xuelong Li, Dacheng Tao and Jie Yang. Multimodal Biometrics Using Geometry Preserving Projections [J], Pattern Recognition,2008,41(3): 805-813.