实用步态数据库的建立和步态特征提取与表征方法
|
摘要
步态识别是进行远距离非侵犯性视觉监控、进行身份识别的理想技术之一。步态特征的提取质量对识别效果来说至关重要,在实用步态识别系统中如何构建步态数据库也是目前没有得到很好解决的问题。围绕步态识别技术与实用之间存在的这两个突出的问题,本文进行了以下几个方面的研究：
(1)提出了一种建立实用步态数据库的方法,详细设计了步态特征数据库的建立方法、数据结构、步态信息的获取方法以及身高查询条件的确定方法,建立了百人规模的多信息步态数据库。
(2)提出了一种结构化步态特征表示方法,结合关系数据库技术设计了一个基于数据库技术的步态识别方法,使步态识别能够利用关系数据库查询的高效性来快速缩小识别范围,提高运行效率和准确性。并通过实验验证了基于数据库技术的步态识别方法良好的身份识别效果。
(3)提出一种基于时空能量图的步态特征弱周期性表示方法,给出了步态特征弱周期性和步态序列的时空能量图的定义,以统计分析的方式证明了这种特征表示对于噪声的干扰不敏感。将这种方法与论文提出的几种步态特征提取算法相结合,实验结果表明,这种方法大大简化了步态识别的预处理过程,减少步态特征的数据存储空间,降低了特征提取对于步速、摄像机采样频率等因素具体条件的依赖性,同时,识别性能也具有一定的实用参考价值。
(4)利用小波多分辨率分析表征信号局部特征的优势,结合互信息熵的判据,提出一种基于小波分析和互信息熵的步态特征表示和识别算法,将小波变换得到的相似子图和各个具有局部方向特性的细节子图都利用到组合步态特征中,求得最大互信息熵时得到对应的组合特征参数组,这些参数突出了步态中行走习惯的细节差异,使提取的步态特征更集中在有利于分类的关键信息点上,从而步态识别过程更为接近人类视觉的智能识别过程。在步态识别阶段提出了互信息度量的改进方法,即沿用了能量分析和互信息度量的有效思路,又提高了识别速度。
Gait recognition is one of the ideal technologies for unobtrusive security surveillance and human identification at a distance. The quantity of the extracted gait features is important to recognition. And it has not been figured out how to construct the gait database for a practical gait recognition system. Focusing on these two prominent questions between the gait recognition technology and the practical system, this dissertation mainly includes the following issues:
①A method of constructing a practicality gait Database is presented. The Database constructed procedure, data structure and the obtained method of the gait are discussed in detail. Thanks for young students'passional participation, A gait database with multifarious gait information of110individuals is established. It is used for all studies in this paper.
②A gait recognition method based on the database technology is proposed. The method of constructing the gait database combining with the relational database technology is presented. And the method that used the high efficien-cy of query in the relational database to reduce the individual set for the gait recognition algorithm to identify is presented. The particular design of the gait feature database, including the construction method and the data structure, the gait sampling method and the determine of the query condition about the in-dividual height range are depicted. Experimental results demonstrate that this practical gait database could availably support the gait recognition algorithm to identify the individuals more quickly and more exactly.
③A gait feature expression with the semi-periodicity based on the Spatio-Temporal Energy image is described, in which the semi-periodicity and the Spatio-Temporal Energy image are defined formally. And it has been proved insensitive to noise by the statistical analysis. Experiments combining this expression with other gait feature extraction method show that this gait expression simplified the pretreatment of the gait recognition greatly, depressed the storage of the gait features, and reduced the dependence of the gait feature extraction on the step speed and the video sampling frequency, while the recognition performance possessed some practical merit.
④Making use of the advantage of wavelets analysis which could express signal local feature well, and combined the mutual information merit, a bionic gait recognition based the wavelets analysis and the mutual information is proposed. The similar subimage and three detail subimages containing local orientation characters from wavelets transformation are all utilized into the combined gait feature. When the maximum mutual information gained, the optimal coefficients for the extracted combined gait feature are determined. these coefficients give prominence to particular walking habit differences of a individual, which make the combined gait feature focus on the key information discriminative for human identification and the procedure of gait recognition by computer system is more closed to the intelligent human vision.
(1)提出了一种建立实用步态数据库的方法,详细设计了步态特征数据库的建立方法、数据结构、步态信息的获取方法以及身高查询条件的确定方法,建立了百人规模的多信息步态数据库。
(2)提出了一种结构化步态特征表示方法,结合关系数据库技术设计了一个基于数据库技术的步态识别方法,使步态识别能够利用关系数据库查询的高效性来快速缩小识别范围,提高运行效率和准确性。并通过实验验证了基于数据库技术的步态识别方法良好的身份识别效果。
(3)提出一种基于时空能量图的步态特征弱周期性表示方法,给出了步态特征弱周期性和步态序列的时空能量图的定义,以统计分析的方式证明了这种特征表示对于噪声的干扰不敏感。将这种方法与论文提出的几种步态特征提取算法相结合,实验结果表明,这种方法大大简化了步态识别的预处理过程,减少步态特征的数据存储空间,降低了特征提取对于步速、摄像机采样频率等因素具体条件的依赖性,同时,识别性能也具有一定的实用参考价值。
(4)利用小波多分辨率分析表征信号局部特征的优势,结合互信息熵的判据,提出一种基于小波分析和互信息熵的步态特征表示和识别算法,将小波变换得到的相似子图和各个具有局部方向特性的细节子图都利用到组合步态特征中,求得最大互信息熵时得到对应的组合特征参数组,这些参数突出了步态中行走习惯的细节差异,使提取的步态特征更集中在有利于分类的关键信息点上,从而步态识别过程更为接近人类视觉的智能识别过程。在步态识别阶段提出了互信息度量的改进方法,即沿用了能量分析和互信息度量的有效思路,又提高了识别速度。
Gait recognition is one of the ideal technologies for unobtrusive security surveillance and human identification at a distance. The quantity of the extracted gait features is important to recognition. And it has not been figured out how to construct the gait database for a practical gait recognition system. Focusing on these two prominent questions between the gait recognition technology and the practical system, this dissertation mainly includes the following issues:
①A method of constructing a practicality gait Database is presented. The Database constructed procedure, data structure and the obtained method of the gait are discussed in detail. Thanks for young students'passional participation, A gait database with multifarious gait information of110individuals is established. It is used for all studies in this paper.
②A gait recognition method based on the database technology is proposed. The method of constructing the gait database combining with the relational database technology is presented. And the method that used the high efficien-cy of query in the relational database to reduce the individual set for the gait recognition algorithm to identify is presented. The particular design of the gait feature database, including the construction method and the data structure, the gait sampling method and the determine of the query condition about the in-dividual height range are depicted. Experimental results demonstrate that this practical gait database could availably support the gait recognition algorithm to identify the individuals more quickly and more exactly.
③A gait feature expression with the semi-periodicity based on the Spatio-Temporal Energy image is described, in which the semi-periodicity and the Spatio-Temporal Energy image are defined formally. And it has been proved insensitive to noise by the statistical analysis. Experiments combining this expression with other gait feature extraction method show that this gait expression simplified the pretreatment of the gait recognition greatly, depressed the storage of the gait features, and reduced the dependence of the gait feature extraction on the step speed and the video sampling frequency, while the recognition performance possessed some practical merit.
④Making use of the advantage of wavelets analysis which could express signal local feature well, and combined the mutual information merit, a bionic gait recognition based the wavelets analysis and the mutual information is proposed. The similar subimage and three detail subimages containing local orientation characters from wavelets transformation are all utilized into the combined gait feature. When the maximum mutual information gained, the optimal coefficients for the extracted combined gait feature are determined. these coefficients give prominence to particular walking habit differences of a individual, which make the combined gait feature focus on the key information discriminative for human identification and the procedure of gait recognition by computer system is more closed to the intelligent human vision.
引文
[1]景英娟,董育宁.生物特征识别技术综述.电子学报,2001,29(12A)：1744-1748.
[2]Jain A K, Ross A, Prabhaker S. An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology,2004, 14(1):4-20.
[3]Delac K, Grgic M. A Survey of Biometric Recognition Methods. International Symposium Electronics in Marine,2004:184-193.
[4]张敏贵,周德龙生物特征识别及研究现状.生物物理学报,2002,18(2)：156-162.
[5]Solayappan N, Latifi S. A Survey of Unimodal Biometric Methods. Interna-tional Conference on Security and Management,2006:57-63.
[6]Wayman J L. Digital signal processing in biometric identification: a review. International Conference on Image Processing 2002:37-40.
[7]Shakhnarovich G, Lee L, Darrell T. Integrated face and gait recognition from multiple views. IEEE Conference on Computer Vision and Pattern Recognition,2001:439-446.
[8]Zhao G, Liu G, Li H, Pietikainen M.3D gait recognition using Multiple Cam-eras. International Conference on Automatic Face and Gesture Recognition, 2006:529-534.
[9]Urtasun R, Fua P.3D tracking for gait characterization and recognition. IEEE International Conference on Automatic Face and Gesture Recognition, 2004:17-22.
[10]Johnson A Y, Bobick A F. A multi-view method for gait recognition using static body parameters. International Conference on Auto-and Video-Based Biometric Person Authentication,2001:301-311.
[11]Bobick A F, Johnson A Y. Gait recognition using static, activity-specific parameters. IEEE Conference on Computer Vision and Pattern Recognition, 2001:423-430.
[12]Han J, Bhanu B, Roy-Chowdhury A K. A study on view-insensitive gait recognition. International Conference on Image Processing,2005:3561-3564.
[13]Gomatain A N M, Sasi S. Gait recognition based on isoluminance line and 3D template matching. International Conference on Intelligent Sensing and Information Processing,2005:156-160.
[14]Kale A, Chowdhury A K R, Chellappa R. Towards a view invariant gait recognition algorithm. IEEE Conference on Advanced Video and Signal Based Surveillance,2003:143-150.
[15]Lee C S, Elgammal A. Towards scalable view-invariant gait recognition: multilinear analysis for gait. International Conference on Audio-and Video-Based Biometric Person Authentication,2005:395-405.
[16]Zhao G, Chen R, Liu G, Li H. Amplitude spectrum-based gait recognition. International Conference on Automatic Face and Gesture Recognition,2004: 23-28.
[17]Tolliver D, Collins R T. Gait shape estimation for identification. Internation-al Conference on Audio-and Video-Based Biometric Person Authentication, 2003:734-742.
[18]Tanawongsuwan R, Bobick A. Performance analysis of time-distance gait parameters under different speeds. International Conference on Audio-and Video-Based Biometric Person Authentication,2003:715-724.
[19]Collins R,T, Gross R, Shi J. Silhouette-based human identification from body shape and gait. International Conference on Automatic Face and Gesture Recognition,2002:351-356.
[20]Boulgouris N V, Hatzinakos D, Plataniotis K N. Gait recognition:a chal-lenging signal Processing technology for biometricidentification. IEEE Signal Proccessing Magazine,2005,22(6):78-90.
[21]陈昌红,梁继民,赵恒等.步态表征和步态融合方法新进展.计算机科学,2010,37(8)：15-20.
[22]Huang P S, Harris C J. Canonical space representation for recognizing hu-mans by gait and face. IEEE Southwest Symposium on Image Analysis and Interpretation,1998:180-185.
[23]Zhou X, Bhanu B. Feature fusion of face and gait for human recognition at a distance in video. International Conference on Pattern Recognition,2006: 529-532.
[24]Kale A, Roychowdhury A K, Chellappa R. Fusion of gait and face for human identification. IEEE International Conference on Acoustics, Speech, and Signal Processing,2004, V.901-4 vol.5:147-152.
[25]Zhou X, Bhanu B. Integrating face and gait for human recognition. Confer-ence on Computer Vision and Pattern Recognition Workshop,2006:55-55.
[26]Zhou X, Bhanu B. Feature fusion of side face and gait for video-based human identification. Pattern Recognition,2008,41(3):778-795.
[27]Zhou X, Bhanu B. Integrating face and gait for human recognition at a distance in video. IEEE Transaction on Systems, Man, and Cybernetics, 2007,37(5):1119-1137.
[28]Liu Z, Sarkar S. Outdoor recognition at a distance by fusing gait and face. Image and Vision Computing,2007,25(6):817-832.
[29]Nixon M S, Carter J N, Shutler J D, et al. Advances in automatic gait recogni-tion. International Conference on Automatic Face and Gesture Recognition, 2004:139-144.
[30]Niyogi S A, Adelson E H. Analyzing and Recognizing Walking Figures in XYT. IEEE Conference on Computer Vision and Pattern Recognition,1994: 469-474.
[31]Meyer D, Denzler J and Niemann H. Model based extraction of articulat-ed objects in image sequences for gait analysis. Proc IEEE International Conference on Image Processing, Santa Barbara, California 1997,78-81.
[32]Huang P S, Harris C J, Nixon M S. Recognition humans by gait via parametric canonical space. Arificial Intelligence in Engineering,1999,13(4):350-366.
[33]Shutler J D, Grant M G, Nixon M S, Carter J N. On a large sequence-based human gait database. International Conference on Recent Advances in Soft Computing,2004:339-346.
[34]Sarkar S, Phillips P J, Liu Z, et al. The human ID gait challenge problem: data sets, performance, and analysis. IEEE Trans on Pattern Analysis and Machine Intelligence,2003,27(2):162-177.
[35]Yu S, Tan D, Tan T, et al. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognitiong. International Conference on Pattern Recognition,2006:441-444.
[36]Little J and Boyd J. Recognizing people by their gait: the shape of motion. Videre:Journal of Computer Vision Research, The MIT Press,1998,1 (2): 2-32.
[37]Shutler J D, Grant M G, Nixon M S, and Carter J N. On a large sequence-based human gait database. Proc.4th International Conference on Recent Advances in Soft Computing, Nottingham (UK),2002:66-71.
[38]Phillips P J, Sarkar S, Robledo I, et.al. The gait identification challenge prob-lem:Data sets and baseline algorithm. International Conference on Pattern Recognition,2002:385-388.
[39]Gross Ralph, Shi Jianbo. The CMU Motion of Body (MoBo) database. Tech-nical Report CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon Uni-versity,2001.
[40]Wang L, Tan T, Hu W, Ning H. Automatic Gait Recognition Based on Statistical Shape Analysis. IEEE Transactions on Image Processing,2003: 1505-1518.
[41]Wang Yuan, Yu Shiqi, Wang Yunhong, et al. Gait recognition based on fusion of multi-view gait sequences. International Conference on Advances in Biometrics,2005:605-611.
[42]Tan D, Huang K, Yu S, Tan T. Efficient Night Gait Recognition Based on Template Matching. International Conference on Pattern Recognition,2006: 1000-1003.
[43]Jaeseok Yun. User Identification Using Gait Patterns on UbiFloorⅡ. Sensors, 2011,11:2611-2639.
[44]Haritaoglu I, Harwood D, Davis L. W4:real-time surveillance of people and their activities. IEEE Trans Pattern Analysis and Machine Intelligence,2000, 22(8):809-830.
[45]Stauffer C and Grimson W. Adaptive background mixture models for real-time tracking. Proc IEEE Conference on Computer Vision and Pattern Recogni-tion, Fort Collins, Colorado,1999,2:246-252.
McKenna S et al. Tracking groups of people. Computer Vision and Image Understanding,2000,80 (1):42-56.
[47]Kilger M. A shadow handler in a video-based real-time traffic monitoring system. Proc IEEE Workshop on Applications of Computer Vision, Palm Springs, CA,1992,1060-1066.
[48]Sun H, Feng T and Tan T. Robust extraction of moving objects from image sequences. Proc the Fourth Asian Conference on Computer Vision, Taiwan, 2000,961-964.
[49]Lipton A, Fujiyoshi H and Patil R. Moving target classification and tracking from real-time video. Proc IEEE Workshop on Applications of Computer Vision, Princeton, NJ,1998,8-14.
[50]Anderson C, Bert P and Vander Wal G. Change detection and tracking using pyramids transformation techniques. Proc SPIE Conference on Intelligent Robots and Computer Vision, Cambridge, MA,1985,579:72-78.
[51]Collins Ret al.. A system for video surveillance and monitoring:VSAM final report. Carnegie Mellon University, Technical Report:CMU-RI-TR-00-12, 2000.
[52]Verri A, Uras S and DeMicheli E. Motion Segmentation from optical flow. Proc the 5th Alvey Vision Conference, Brighton, UK,1989,209-214.
[53]Barron J, Fleet D and Beauchemin S. Performance of optical flow techniques. International Journal of Computer Vision,1994,12 (1):42-77.
[54]Friedman N and Russell S. Image segmentation in video sequences:a proba-bilistic approach. Proc the Thirteenth Conference on Uncertainty in Artificial Intelligence, Rhode Island, USA,1997.
[55]McLachlan G and Krishnan T. The EM Algorithm and Extensions. Wiley Interscience,1997.
[56]Stringa E. Morphological change detection algorithms for surveillance appli-cations. British Machine Vision Conference, Bristol, UK,2000,402-411.
[57]Schutze H, Hull D A, Pedersen J O. A comparison of classifiers and document representations for the routing problem. Proc of the 18th ACM Int. Conf on Research and Development in Information Retrieval. New York:ACM, 1995:229-237.
[58]刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法.自动化学报.2003,29(6)：900-911.
Vidal R, Ma Y, Sastry S. Generalized principal component analysis (GPCA). IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27: 1945-1959.
[60]曹杨,罗予频,杨士元.子空间搜索广义主成分分析.计算机学报,2007,30(12)：2151-2155.
[61]Weng J, Zhang Y, Hwang W S. Candid covariance-free incremental prin-cipal component analysis. IEEE Trans Pattern Analysis and Machine Intel-ligence,2003,25(8):1034-1040.
[62]Alleyne A, Hedrick J K. Nonlinear Control of a Quarter Car Active Sus-pension. Proceeding of the 1992 American Control Conference, Chicago IL, 1992,21-25.
[63]Fialho I, Balas G. Adaptive Vehicle Suspension Design Using LPV Methods. Proceeding of the 37th Conference on Decision & Control, Tampa, Florida USA,1998,469-474.
[64]王晓明,王士同.广义的监督局部保留投影算法.电子与信息学部,2009,31(8)：1840-1845.
[65]Wei S, Ning C, Gao Y. Manifold Learning Based Gait Feature Reduction and Recognition. Journal of Software.2011,6(7):1345-1352.
[66]Roweis S T, Saul L K. Nonlinear dimensionality reduction by Locally Linear embedding. Science.2002,290(5500):2323-2326.
[67]Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science,2000,290(5500):2319-2323.
[68]He X, Niyogi P. Locality Preserving Projections. Advance in Neural Infor-mation Processing Systems, Vancouver, Canada, December 2003, Vol.16.
[69]Kokiopoulou E and Saad Y. Orthogonal Neighborhood Preserving Projection-s. ICDM05.2005.
[70]Lu Haiping, Plataniotis K N and Venetsanopoulosy A N. Uncorrelated Mul-tilinear Discriminant Analysis with Regularization and Aggregation for Ten-sor Object Recognition. Biometrics Symposium 2007, Baltimore, Maryland, September 11-13,2007.
[71]M A O Vasileseu, D Terzopoulos. Multilinear analysis of images ensembles. Proc of European Conference on Computer Vision. Berlin:Springer-Verlag, 2002.447-460.
[72]He Xiaofei, Cai Deng, Niyogi Partha. Tensor Subspace analysis. http://book.nips.cc/nipsl8.html,2007-10-20.
[73]李勇周,罗大庸,刘少强.张量局部判别投影的人脸识别.电子学报,2008,36(10)：2070-2075.
[74]Yan S, Xu D, Zhang B, et al. Graph embedding: ageneral framework for dimensionality reduction. Proc IEEE Computer Vision and Pattern Recog-nition, San Diego, CA, USA,2005,2:830-837.
[75]Xu D, Yan S C, Tao D C, et al. Marginal Fisher Analysis and Its Vari-ants for Human Gait Recognition and Content-Based Image Retrieval. IEEE Transactions on image Processing,2007,16(11):2811-2821.
[76]李子荣,杜明辉.正交边际Fishei分析在人脸识别中的应用.科学技术与工程,2008,8(20)：5589-5592.
[77]He X. Locality Preserving Projections. Chicago, Illinos:University of Chica-go of USA,2005,12.
[78]Sun T K, Chen S C. Locality preserving CCA with applications to data visu-alization and pose estimation. Image and Vision Computing.2007,25(5): 531-543.
[79]马勤勇,聂栋栋,王申康.基于主运动轮廓线的步态表示与识别.自动化学报,2009,35(5)：519-525.
[80]陈实,马天骏,高有行.用行人轮廓的分布直方图分类和识别步态.计算机研究与发展,2009,46(2)：295-301.
[81]BenAbdelkader Chiraz, Cutler Ross, Davis Larry. View-invariant Estimation of Height and Stride for Gait Recognition. Biometric Authentication 2002, LNCS 2359, Springer-Verlag Berlin Heidelberg 2002,155-167.
[82]张浩,刘志镜.加权DTW距离的自动步态识别.中国图象图形学报,2010,15(5)：830-836.
[83]Su Han, Huang Fenggang. Gait recognition using principal Curves and Neural Networks. ISNN 2006, LNCS 3972, Springer-Verlag Berlin Heidelberg 2006: 238-243.
[84]薛召军,李佳,明东等.基于支持向量机的步态识别新方法.津大学学报,2007,40(1)：78-82.
[85]陈昌红,赵恒,胡海虹等.基于改进动态纹理模型的人体运动分析,模式识别与人工智能,2010,23(2)：267-272.
[86]Yoo J, Nixon M S. Automated Markerless Analysis of Human Gait Motion for Recognition and Classification. ETRI Journal,2011,33(2):259-266.
[87]Myers C, Rabinier L and Rosenberg A. Performance tradeoffs in dynamic time warping algorithms isolated word recognition. IEEE Trans Acoustics, Speech, and Signal Processing,1980,28 (6):623-635.
[88]Ashok Veeraraghavan, Amit K. Roy-Chowdhury and Rama Chellappa. Matching shape sequences in video with applications in human movement analysis. IEEE Trans on Pattern Analysis and Maching Intelligence,2005, 27(12):1896-1909.
[89]Bobick A and Wilson A. A state-based technique for the summarization and recognition of gesture. In:Proc International Conference on Computer Vision, Cambridge,1995,382-388.
[90]Takahashi K, and Seki S et al. Recognition of dexterous manipulations from time varying images. In:Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin,1994,23-28.
[91]Poritz A. Hidden Markov Models:a guided tour. In:Proc IEEE International Conference on Acoustics, Speech and Signal Processing, New York City, NY, 1988,7-13.
[92]Rabinier L. A tutorial on hidden Markov models and selected applications in speech recognition. In:Proc IEEE 1989,77 (2):257-285.
[93]Starner T and Pentland A. Real-time American Sign Language recognition from video using hidden Markov models. In:Proc International Symposium on Computer Vision, Coral Gables, Florida,1995,265-270.
[94]Yamato J, Ohya J and Ishii K. Recognizing human action in time-sequential images using hidden Markov model. In:Proc IEEE Conference on Computer Vision and Pattern Recognition, Champaign, Illinois,1992,379-385.
[95]Brand M, Oliver N and Pentland A. Coupled hidden Markov models for complex action recognition. In:Proc IEEE Conference Computer Vision and Pattern Recognition, Puerto Rico,1997,994-999.
[96]Guo Y, Xu G and Tsuji S. Understanding human motion patterns. In:Proc International Conference on Pattern Recognition, Jerusalem, Israel,1994, 325-329.
[97]Rosenblum M, Yacoob Y and Davis L. Human emotion recognition from motion using a radial basis function network architecture. In:Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin,1994, 43-49.
[98]Chai Y, Ren J, Zhao R, Jia J. Automatic Gait Recognition using Dynamic Variance Features. International Conference on Automatic Face and Gesture Recognition,2006:475-480.
[99]Lam T H W, Lee R S T. Human Identification by Using the Motion and Static Characteristic. International Conference on Pattern Recognition,2006:996-999.
[100]Lam T H W, Lee R S T, Zhang D. Human gait recognition by the fusion of motion and static spatio-temporal templates. Pattern Recognition,2007, 40(9):2563-2573.
[101]Begg R K, Palaniswami M, Owen B. Support Vector Machines for Auto-mated Gait Classification. IEEE Transactions on Biomedical Engineering, 2005,52(5):828-238.
[102]Wu J, Fang U, Wang J. A New Approach for Gait Classification using Sup-port Vector Machines. WSEAS Transactions on Computers,2006,2006(5): 943-948.
[103]Zhang E, Lu J, Duan G. Gait Recognition via Independent Component Anal-ysis Based on Support Vector Machine and Neural Network. International Conference on Advances in Natural Computation,2005:640-649.
[104]Lu J, Zhang E. Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion. Pattern Recognition Letters,2007, 28(16):2401-2411.
[105]Polana R and Nelson R. Low level recognition of human motion. In:Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin, TX,1994,77-82.
[106]Bobick A, Davis J. Real-time recognition of activity using temporal tem-plates. In:Proc IEEE Workshop on Applications of Computer Vision, Sara-sota, Florida,1996,39-42.
[107]Davis J and Bobick A. The representation and recognition of action using temporal templates. MIT Media Lab, Perceptual Computing Group, Techni-cal report:402,1997.
[108]Cui Y and Weng J. Hand segmentation using learning-based prediction and verification for hand sign recognition. In:Proc IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico,1997,88-93.
[109]Aaron F Bobick, James W. Davis, the Recognition for Human Movement Using Temporal Templates. IEEE Trans on PAMI,2001,23(3):257-267.
[110]Bregler C. Learning and recognizing human dynamics in video sequences. In:Proc IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico,1997,568-574.
[111]Campbell L and Bobick A. Recognition of human body motion using phase space constraints. In:Proc International Conference on Computer Vision, Cambridge,1995,624-630.
[112]Han J, Bhanu B. Statistical feature fusion for gait-based human recogni-tion. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, USA,2004:842-847.
[113]Na Q, Wang S, Nie D, et al. Recognition Human based on gait moment image.8th International Conference on SNPD,2007:606-610.
[114]Chen C, Liang J, Zhao H, et al. Factorial HMM and parallel HMM for gait recognition. IEEE Trans, on Systems, Man and Cybernetics, Part C,2009, 39(1):114-123.
[115]Ekinci M. Human identification using gait. Turkish Journal of Electrical Engineering and Computer Sciences,2006,14(2):267-291.
[116]Wang L, Ning H, Tan T, et al. Fusion of static and dynamic body bio-metrics for gait recognition. IEEE Trans, on Circuit and Systems for Video Technology,2004,15(2):149-158.
[117]Wang L, Tan T, Hu M, et al. Automatic recognition based on statistical shap analysis. IEEE Trans. Image Processing,2003,12(9):1120-1131.
[118]Lan T, Lee R. A new recognition for human gait recognition:motion silhou-ettes image(MSI). International conference on Biometrics,2006:612-618.
[119]赵永伟,张二虎,鲁继文等.多特征和多视角信息融合的步态识别.中国图象图形学报,2009,14(3)：388-393.
[120]Kale A, Roy-chowdhury A, Chellappa R. Fusion of gait and face human identification. Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing,2004,5:901-904.
[121]Zhou X, Bhanu B, Han J. Human recognition at a distance in video by integrating face profile and gait. Proc.5th International Conference on Audio-and Video-Based Biometric Person Authentication,2005:165-181.
[122]Li Q, Lu Z, Zhang D. Integration of gait and side face foe human recognition in video. Proc.2nd International Symposium on Electronic Commerce and Security,2009:65-69.
[123]Cuntoor N, Kale A, Challappa R. Combining multiple evidences for gati recogntion. Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, Hong Kong China,2003,3:33-36.
[124]Zheng Z H, Wu X Y, Srihari R. Feature selection for text categorization on imbalanced data. ACM SIGKDD Explorations Newsletter,2004,6(1): 80-89.
[125]Phillips P Joathon, Moon Hyeonjoon, Rizvi Syed A, et al. the FERET eva-lutation methodology for face-recognition algorithms. IEEE Trans on PAMI, 2000,22(10):1090-1104.
[126]Dash M, Liu H, Motoda H. Consistency based feature selection. Proc of the 4th Pacific-Asia Conf on Knowledge Discovery and Data Mining, Current Issues and New Applications. London:Springer-Verlag.2000:98-109.
[127]Almullim H, Dietterich T G. Learning Boolean concepts in the presence of many irrelevant features. Artificial Intelligence,1994,69(1-2):279-305.
[128]Kim H, Kweon I S. Appearance-cloning: photoconsistent scene recovery from multi-view images. International Journal of Computer Vision,2006, 66(2):163-192.
[129]Pons J P, Keriven R, Faugeras O. Multi-view stereo reconstruction and scene flow estimation with a global image-based matching score. International Journal of Computer Vision,2007,72(2):179-193
[130]Yu L, Liu H. Feature selection for high-dimensional data:a fast correlation-based filter solution. Proc of the 20th Int. Conf on Machine Learning, ICML2003. Washington DC:[s.n.],2003.
[131]Keller D, Sahami M. Towards optimal feature selection. Proc of the 13th Int. Conference on Machine Learning.1996:284-292.
[132]Mitra P, Murthy C A, Pal S K. Unsupervised feature selection using feature similarity. IEEE Trans on Pattern Recognition and Machine Intelligence, 2002,24(3):301-312.
[133]Haykin S. Neural networks: a comprehensive foundation. 2nd ed. [S.I.]: Prentice Hall,1998.
[134]Battiti R. Using mutual information for selecting features in supervised neutral net learning. IEEE Trans on Neural Networks,1994,5:537-550.
[135]Cang S, Yu H N. A new approach for detecting the best feature set. Proc of Networking, Sensing and Control. [S.I.]:IEEE CNF,2005:74-79.
[136]Kwak N, Choi C H. Input feature, selection for classification problems. IEEE Trans on Neural Networks,2002,13(1):143-159.
[137]Davis J V, Kulis B, Jain P, et al. Information-theoretic metric learning. Proc of the 24th Int. Conf on Machine Learning. New York:ACM,2007: 209-216.
[138]贾立好,邹建华,车凯.基于头顶三维运动轨迹的身份识别新方法.自动化学报,2011,37(1)：28-36.
[139]BenAbdelkader C, Cutler R, Davis L. Stride and cadence as a biometric in automatic person identification and verification. Automatic Face and Gesture Recognition, Proceedings. Fifth IEEE International Conference,2002,357-362.
[140]Bashir Khalid, Xiang Tao, Gong Shaogang. Feature selection on gait energy image for human identification. ICASSP 2008,985-988.
[141]Wang Liang, Tan T. Silhouette analysis-based gait recognition for human identification. IEEE Trans on PAMI,2003,25(12):1505-1518.
[142]王亮,胡卫明,谭铁牛.基于步态的身份识别.计算机学报,2003,26(3)：353-360.
[143]Liu Z Y. Effect of Silhouette Quality on Hard Problem in Gait Recognition. IEEE Trans on System, Man, and Cybernetics,2005,35(2):170-183.
[144]陈昌由,张军平.基于迭代切距离原型学习算法的步态识别.计算机研究与发展,2008,45(7)：1177-118.
[145]徐佩霞.小波分析与应用实例.合肥：中国科学技术大学出版社,1996.
[146]Nastar C, Ayache N. Frequency-based Non-rigid Motion Analysis. IEEE Trans. on PAMI,1996,18(11):1067-1079.
[147]Shutler J D, Nixon M, Harris C. Statistical gait description via velocity moments. Proc IEEE Southwest Symposium on Image Analysis and Inter-pretation, Austin, Texas,2000,291-295.
[148]Foster J, Nixon M, Prugel-Bennett A. New area based metrics for gait recognition. In:Proceedings of International Conference on Audio2 and Video2based Biometric Person Authentication, Halmstad, Sweden,2001. 312-317.
[149]Hayfron Acquah J, Nixon M, Carter J. Automatic gait recognition by sym-metry analysis. In:Proceedings of International Conference on Audio- and Video-based Biometric Person Authentication, Halmstad, Sweden,2001. 272-277.
[150]Suk H, Sin B. HMM-Based Gait Recognition with Human Profiles. SSPR-SPR 2006, LNCS 4109, pp.596-603,2006.
[151]Chen Changhong, Liang Jinmin, Zhao Heng, et al. Gait recognition using Hidden Markov Model. In:L Jiao et al. ICNC 2006, Part I, LNCS 4221. Berlin Heidelberg:Springer-Verleg,2006:399-407.
[2]Jain A K, Ross A, Prabhaker S. An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology,2004, 14(1):4-20.
[3]Delac K, Grgic M. A Survey of Biometric Recognition Methods. International Symposium Electronics in Marine,2004:184-193.
[4]张敏贵,周德龙生物特征识别及研究现状.生物物理学报,2002,18(2)：156-162.
[5]Solayappan N, Latifi S. A Survey of Unimodal Biometric Methods. Interna-tional Conference on Security and Management,2006:57-63.
[6]Wayman J L. Digital signal processing in biometric identification: a review. International Conference on Image Processing 2002:37-40.
[7]Shakhnarovich G, Lee L, Darrell T. Integrated face and gait recognition from multiple views. IEEE Conference on Computer Vision and Pattern Recognition,2001:439-446.
[8]Zhao G, Liu G, Li H, Pietikainen M.3D gait recognition using Multiple Cam-eras. International Conference on Automatic Face and Gesture Recognition, 2006:529-534.
[9]Urtasun R, Fua P.3D tracking for gait characterization and recognition. IEEE International Conference on Automatic Face and Gesture Recognition, 2004:17-22.
[10]Johnson A Y, Bobick A F. A multi-view method for gait recognition using static body parameters. International Conference on Auto-and Video-Based Biometric Person Authentication,2001:301-311.
[11]Bobick A F, Johnson A Y. Gait recognition using static, activity-specific parameters. IEEE Conference on Computer Vision and Pattern Recognition, 2001:423-430.
[12]Han J, Bhanu B, Roy-Chowdhury A K. A study on view-insensitive gait recognition. International Conference on Image Processing,2005:3561-3564.
[13]Gomatain A N M, Sasi S. Gait recognition based on isoluminance line and 3D template matching. International Conference on Intelligent Sensing and Information Processing,2005:156-160.
[14]Kale A, Chowdhury A K R, Chellappa R. Towards a view invariant gait recognition algorithm. IEEE Conference on Advanced Video and Signal Based Surveillance,2003:143-150.
[15]Lee C S, Elgammal A. Towards scalable view-invariant gait recognition: multilinear analysis for gait. International Conference on Audio-and Video-Based Biometric Person Authentication,2005:395-405.
[16]Zhao G, Chen R, Liu G, Li H. Amplitude spectrum-based gait recognition. International Conference on Automatic Face and Gesture Recognition,2004: 23-28.
[17]Tolliver D, Collins R T. Gait shape estimation for identification. Internation-al Conference on Audio-and Video-Based Biometric Person Authentication, 2003:734-742.
[18]Tanawongsuwan R, Bobick A. Performance analysis of time-distance gait parameters under different speeds. International Conference on Audio-and Video-Based Biometric Person Authentication,2003:715-724.
[19]Collins R,T, Gross R, Shi J. Silhouette-based human identification from body shape and gait. International Conference on Automatic Face and Gesture Recognition,2002:351-356.
[20]Boulgouris N V, Hatzinakos D, Plataniotis K N. Gait recognition:a chal-lenging signal Processing technology for biometricidentification. IEEE Signal Proccessing Magazine,2005,22(6):78-90.
[21]陈昌红,梁继民,赵恒等.步态表征和步态融合方法新进展.计算机科学,2010,37(8)：15-20.
[22]Huang P S, Harris C J. Canonical space representation for recognizing hu-mans by gait and face. IEEE Southwest Symposium on Image Analysis and Interpretation,1998:180-185.
[23]Zhou X, Bhanu B. Feature fusion of face and gait for human recognition at a distance in video. International Conference on Pattern Recognition,2006: 529-532.
[24]Kale A, Roychowdhury A K, Chellappa R. Fusion of gait and face for human identification. IEEE International Conference on Acoustics, Speech, and Signal Processing,2004, V.901-4 vol.5:147-152.
[25]Zhou X, Bhanu B. Integrating face and gait for human recognition. Confer-ence on Computer Vision and Pattern Recognition Workshop,2006:55-55.
[26]Zhou X, Bhanu B. Feature fusion of side face and gait for video-based human identification. Pattern Recognition,2008,41(3):778-795.
[27]Zhou X, Bhanu B. Integrating face and gait for human recognition at a distance in video. IEEE Transaction on Systems, Man, and Cybernetics, 2007,37(5):1119-1137.
[28]Liu Z, Sarkar S. Outdoor recognition at a distance by fusing gait and face. Image and Vision Computing,2007,25(6):817-832.
[29]Nixon M S, Carter J N, Shutler J D, et al. Advances in automatic gait recogni-tion. International Conference on Automatic Face and Gesture Recognition, 2004:139-144.
[30]Niyogi S A, Adelson E H. Analyzing and Recognizing Walking Figures in XYT. IEEE Conference on Computer Vision and Pattern Recognition,1994: 469-474.
[31]Meyer D, Denzler J and Niemann H. Model based extraction of articulat-ed objects in image sequences for gait analysis. Proc IEEE International Conference on Image Processing, Santa Barbara, California 1997,78-81.
[32]Huang P S, Harris C J, Nixon M S. Recognition humans by gait via parametric canonical space. Arificial Intelligence in Engineering,1999,13(4):350-366.
[33]Shutler J D, Grant M G, Nixon M S, Carter J N. On a large sequence-based human gait database. International Conference on Recent Advances in Soft Computing,2004:339-346.
[34]Sarkar S, Phillips P J, Liu Z, et al. The human ID gait challenge problem: data sets, performance, and analysis. IEEE Trans on Pattern Analysis and Machine Intelligence,2003,27(2):162-177.
[35]Yu S, Tan D, Tan T, et al. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognitiong. International Conference on Pattern Recognition,2006:441-444.
[36]Little J and Boyd J. Recognizing people by their gait: the shape of motion. Videre:Journal of Computer Vision Research, The MIT Press,1998,1 (2): 2-32.
[37]Shutler J D, Grant M G, Nixon M S, and Carter J N. On a large sequence-based human gait database. Proc.4th International Conference on Recent Advances in Soft Computing, Nottingham (UK),2002:66-71.
[38]Phillips P J, Sarkar S, Robledo I, et.al. The gait identification challenge prob-lem:Data sets and baseline algorithm. International Conference on Pattern Recognition,2002:385-388.
[39]Gross Ralph, Shi Jianbo. The CMU Motion of Body (MoBo) database. Tech-nical Report CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon Uni-versity,2001.
[40]Wang L, Tan T, Hu W, Ning H. Automatic Gait Recognition Based on Statistical Shape Analysis. IEEE Transactions on Image Processing,2003: 1505-1518.
[41]Wang Yuan, Yu Shiqi, Wang Yunhong, et al. Gait recognition based on fusion of multi-view gait sequences. International Conference on Advances in Biometrics,2005:605-611.
[42]Tan D, Huang K, Yu S, Tan T. Efficient Night Gait Recognition Based on Template Matching. International Conference on Pattern Recognition,2006: 1000-1003.
[43]Jaeseok Yun. User Identification Using Gait Patterns on UbiFloorⅡ. Sensors, 2011,11:2611-2639.
[44]Haritaoglu I, Harwood D, Davis L. W4:real-time surveillance of people and their activities. IEEE Trans Pattern Analysis and Machine Intelligence,2000, 22(8):809-830.
[45]Stauffer C and Grimson W. Adaptive background mixture models for real-time tracking. Proc IEEE Conference on Computer Vision and Pattern Recogni-tion, Fort Collins, Colorado,1999,2:246-252.
McKenna S et al. Tracking groups of people. Computer Vision and Image Understanding,2000,80 (1):42-56.
[47]Kilger M. A shadow handler in a video-based real-time traffic monitoring system. Proc IEEE Workshop on Applications of Computer Vision, Palm Springs, CA,1992,1060-1066.
[48]Sun H, Feng T and Tan T. Robust extraction of moving objects from image sequences. Proc the Fourth Asian Conference on Computer Vision, Taiwan, 2000,961-964.
[49]Lipton A, Fujiyoshi H and Patil R. Moving target classification and tracking from real-time video. Proc IEEE Workshop on Applications of Computer Vision, Princeton, NJ,1998,8-14.
[50]Anderson C, Bert P and Vander Wal G. Change detection and tracking using pyramids transformation techniques. Proc SPIE Conference on Intelligent Robots and Computer Vision, Cambridge, MA,1985,579:72-78.
[51]Collins Ret al.. A system for video surveillance and monitoring:VSAM final report. Carnegie Mellon University, Technical Report:CMU-RI-TR-00-12, 2000.
[52]Verri A, Uras S and DeMicheli E. Motion Segmentation from optical flow. Proc the 5th Alvey Vision Conference, Brighton, UK,1989,209-214.
[53]Barron J, Fleet D and Beauchemin S. Performance of optical flow techniques. International Journal of Computer Vision,1994,12 (1):42-77.
[54]Friedman N and Russell S. Image segmentation in video sequences:a proba-bilistic approach. Proc the Thirteenth Conference on Uncertainty in Artificial Intelligence, Rhode Island, USA,1997.
[55]McLachlan G and Krishnan T. The EM Algorithm and Extensions. Wiley Interscience,1997.
[56]Stringa E. Morphological change detection algorithms for surveillance appli-cations. British Machine Vision Conference, Bristol, UK,2000,402-411.
[57]Schutze H, Hull D A, Pedersen J O. A comparison of classifiers and document representations for the routing problem. Proc of the 18th ACM Int. Conf on Research and Development in Information Retrieval. New York:ACM, 1995:229-237.
[58]刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法.自动化学报.2003,29(6)：900-911.
Vidal R, Ma Y, Sastry S. Generalized principal component analysis (GPCA). IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27: 1945-1959.
[60]曹杨,罗予频,杨士元.子空间搜索广义主成分分析.计算机学报,2007,30(12)：2151-2155.
[61]Weng J, Zhang Y, Hwang W S. Candid covariance-free incremental prin-cipal component analysis. IEEE Trans Pattern Analysis and Machine Intel-ligence,2003,25(8):1034-1040.
[62]Alleyne A, Hedrick J K. Nonlinear Control of a Quarter Car Active Sus-pension. Proceeding of the 1992 American Control Conference, Chicago IL, 1992,21-25.
[63]Fialho I, Balas G. Adaptive Vehicle Suspension Design Using LPV Methods. Proceeding of the 37th Conference on Decision & Control, Tampa, Florida USA,1998,469-474.
[64]王晓明,王士同.广义的监督局部保留投影算法.电子与信息学部,2009,31(8)：1840-1845.
[65]Wei S, Ning C, Gao Y. Manifold Learning Based Gait Feature Reduction and Recognition. Journal of Software.2011,6(7):1345-1352.
[66]Roweis S T, Saul L K. Nonlinear dimensionality reduction by Locally Linear embedding. Science.2002,290(5500):2323-2326.
[67]Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science,2000,290(5500):2319-2323.
[68]He X, Niyogi P. Locality Preserving Projections. Advance in Neural Infor-mation Processing Systems, Vancouver, Canada, December 2003, Vol.16.
[69]Kokiopoulou E and Saad Y. Orthogonal Neighborhood Preserving Projection-s. ICDM05.2005.
[70]Lu Haiping, Plataniotis K N and Venetsanopoulosy A N. Uncorrelated Mul-tilinear Discriminant Analysis with Regularization and Aggregation for Ten-sor Object Recognition. Biometrics Symposium 2007, Baltimore, Maryland, September 11-13,2007.
[71]M A O Vasileseu, D Terzopoulos. Multilinear analysis of images ensembles. Proc of European Conference on Computer Vision. Berlin:Springer-Verlag, 2002.447-460.
[72]He Xiaofei, Cai Deng, Niyogi Partha. Tensor Subspace analysis. http://book.nips.cc/nipsl8.html,2007-10-20.
[73]李勇周,罗大庸,刘少强.张量局部判别投影的人脸识别.电子学报,2008,36(10)：2070-2075.
[74]Yan S, Xu D, Zhang B, et al. Graph embedding: ageneral framework for dimensionality reduction. Proc IEEE Computer Vision and Pattern Recog-nition, San Diego, CA, USA,2005,2:830-837.
[75]Xu D, Yan S C, Tao D C, et al. Marginal Fisher Analysis and Its Vari-ants for Human Gait Recognition and Content-Based Image Retrieval. IEEE Transactions on image Processing,2007,16(11):2811-2821.
[76]李子荣,杜明辉.正交边际Fishei分析在人脸识别中的应用.科学技术与工程,2008,8(20)：5589-5592.
[77]He X. Locality Preserving Projections. Chicago, Illinos:University of Chica-go of USA,2005,12.
[78]Sun T K, Chen S C. Locality preserving CCA with applications to data visu-alization and pose estimation. Image and Vision Computing.2007,25(5): 531-543.
[79]马勤勇,聂栋栋,王申康.基于主运动轮廓线的步态表示与识别.自动化学报,2009,35(5)：519-525.
[80]陈实,马天骏,高有行.用行人轮廓的分布直方图分类和识别步态.计算机研究与发展,2009,46(2)：295-301.
[81]BenAbdelkader Chiraz, Cutler Ross, Davis Larry. View-invariant Estimation of Height and Stride for Gait Recognition. Biometric Authentication 2002, LNCS 2359, Springer-Verlag Berlin Heidelberg 2002,155-167.
[82]张浩,刘志镜.加权DTW距离的自动步态识别.中国图象图形学报,2010,15(5)：830-836.
[83]Su Han, Huang Fenggang. Gait recognition using principal Curves and Neural Networks. ISNN 2006, LNCS 3972, Springer-Verlag Berlin Heidelberg 2006: 238-243.
[84]薛召军,李佳,明东等.基于支持向量机的步态识别新方法.津大学学报,2007,40(1)：78-82.
[85]陈昌红,赵恒,胡海虹等.基于改进动态纹理模型的人体运动分析,模式识别与人工智能,2010,23(2)：267-272.
[86]Yoo J, Nixon M S. Automated Markerless Analysis of Human Gait Motion for Recognition and Classification. ETRI Journal,2011,33(2):259-266.
[87]Myers C, Rabinier L and Rosenberg A. Performance tradeoffs in dynamic time warping algorithms isolated word recognition. IEEE Trans Acoustics, Speech, and Signal Processing,1980,28 (6):623-635.
[88]Ashok Veeraraghavan, Amit K. Roy-Chowdhury and Rama Chellappa. Matching shape sequences in video with applications in human movement analysis. IEEE Trans on Pattern Analysis and Maching Intelligence,2005, 27(12):1896-1909.
[89]Bobick A and Wilson A. A state-based technique for the summarization and recognition of gesture. In:Proc International Conference on Computer Vision, Cambridge,1995,382-388.
[90]Takahashi K, and Seki S et al. Recognition of dexterous manipulations from time varying images. In:Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin,1994,23-28.
[91]Poritz A. Hidden Markov Models:a guided tour. In:Proc IEEE International Conference on Acoustics, Speech and Signal Processing, New York City, NY, 1988,7-13.
[92]Rabinier L. A tutorial on hidden Markov models and selected applications in speech recognition. In:Proc IEEE 1989,77 (2):257-285.
[93]Starner T and Pentland A. Real-time American Sign Language recognition from video using hidden Markov models. In:Proc International Symposium on Computer Vision, Coral Gables, Florida,1995,265-270.
[94]Yamato J, Ohya J and Ishii K. Recognizing human action in time-sequential images using hidden Markov model. In:Proc IEEE Conference on Computer Vision and Pattern Recognition, Champaign, Illinois,1992,379-385.
[95]Brand M, Oliver N and Pentland A. Coupled hidden Markov models for complex action recognition. In:Proc IEEE Conference Computer Vision and Pattern Recognition, Puerto Rico,1997,994-999.
[96]Guo Y, Xu G and Tsuji S. Understanding human motion patterns. In:Proc International Conference on Pattern Recognition, Jerusalem, Israel,1994, 325-329.
[97]Rosenblum M, Yacoob Y and Davis L. Human emotion recognition from motion using a radial basis function network architecture. In:Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin,1994, 43-49.
[98]Chai Y, Ren J, Zhao R, Jia J. Automatic Gait Recognition using Dynamic Variance Features. International Conference on Automatic Face and Gesture Recognition,2006:475-480.
[99]Lam T H W, Lee R S T. Human Identification by Using the Motion and Static Characteristic. International Conference on Pattern Recognition,2006:996-999.
[100]Lam T H W, Lee R S T, Zhang D. Human gait recognition by the fusion of motion and static spatio-temporal templates. Pattern Recognition,2007, 40(9):2563-2573.
[101]Begg R K, Palaniswami M, Owen B. Support Vector Machines for Auto-mated Gait Classification. IEEE Transactions on Biomedical Engineering, 2005,52(5):828-238.
[102]Wu J, Fang U, Wang J. A New Approach for Gait Classification using Sup-port Vector Machines. WSEAS Transactions on Computers,2006,2006(5): 943-948.
[103]Zhang E, Lu J, Duan G. Gait Recognition via Independent Component Anal-ysis Based on Support Vector Machine and Neural Network. International Conference on Advances in Natural Computation,2005:640-649.
[104]Lu J, Zhang E. Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion. Pattern Recognition Letters,2007, 28(16):2401-2411.
[105]Polana R and Nelson R. Low level recognition of human motion. In:Proc IEEE Workshop on Motion of Non-Rigid and Articulated Objects, Austin, TX,1994,77-82.
[106]Bobick A, Davis J. Real-time recognition of activity using temporal tem-plates. In:Proc IEEE Workshop on Applications of Computer Vision, Sara-sota, Florida,1996,39-42.
[107]Davis J and Bobick A. The representation and recognition of action using temporal templates. MIT Media Lab, Perceptual Computing Group, Techni-cal report:402,1997.
[108]Cui Y and Weng J. Hand segmentation using learning-based prediction and verification for hand sign recognition. In:Proc IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico,1997,88-93.
[109]Aaron F Bobick, James W. Davis, the Recognition for Human Movement Using Temporal Templates. IEEE Trans on PAMI,2001,23(3):257-267.
[110]Bregler C. Learning and recognizing human dynamics in video sequences. In:Proc IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico,1997,568-574.
[111]Campbell L and Bobick A. Recognition of human body motion using phase space constraints. In:Proc International Conference on Computer Vision, Cambridge,1995,624-630.
[112]Han J, Bhanu B. Statistical feature fusion for gait-based human recogni-tion. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, USA,2004:842-847.
[113]Na Q, Wang S, Nie D, et al. Recognition Human based on gait moment image.8th International Conference on SNPD,2007:606-610.
[114]Chen C, Liang J, Zhao H, et al. Factorial HMM and parallel HMM for gait recognition. IEEE Trans, on Systems, Man and Cybernetics, Part C,2009, 39(1):114-123.
[115]Ekinci M. Human identification using gait. Turkish Journal of Electrical Engineering and Computer Sciences,2006,14(2):267-291.
[116]Wang L, Ning H, Tan T, et al. Fusion of static and dynamic body bio-metrics for gait recognition. IEEE Trans, on Circuit and Systems for Video Technology,2004,15(2):149-158.
[117]Wang L, Tan T, Hu M, et al. Automatic recognition based on statistical shap analysis. IEEE Trans. Image Processing,2003,12(9):1120-1131.
[118]Lan T, Lee R. A new recognition for human gait recognition:motion silhou-ettes image(MSI). International conference on Biometrics,2006:612-618.
[119]赵永伟,张二虎,鲁继文等.多特征和多视角信息融合的步态识别.中国图象图形学报,2009,14(3)：388-393.
[120]Kale A, Roy-chowdhury A, Chellappa R. Fusion of gait and face human identification. Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing,2004,5:901-904.
[121]Zhou X, Bhanu B, Han J. Human recognition at a distance in video by integrating face profile and gait. Proc.5th International Conference on Audio-and Video-Based Biometric Person Authentication,2005:165-181.
[122]Li Q, Lu Z, Zhang D. Integration of gait and side face foe human recognition in video. Proc.2nd International Symposium on Electronic Commerce and Security,2009:65-69.
[123]Cuntoor N, Kale A, Challappa R. Combining multiple evidences for gati recogntion. Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, Hong Kong China,2003,3:33-36.
[124]Zheng Z H, Wu X Y, Srihari R. Feature selection for text categorization on imbalanced data. ACM SIGKDD Explorations Newsletter,2004,6(1): 80-89.
[125]Phillips P Joathon, Moon Hyeonjoon, Rizvi Syed A, et al. the FERET eva-lutation methodology for face-recognition algorithms. IEEE Trans on PAMI, 2000,22(10):1090-1104.
[126]Dash M, Liu H, Motoda H. Consistency based feature selection. Proc of the 4th Pacific-Asia Conf on Knowledge Discovery and Data Mining, Current Issues and New Applications. London:Springer-Verlag.2000:98-109.
[127]Almullim H, Dietterich T G. Learning Boolean concepts in the presence of many irrelevant features. Artificial Intelligence,1994,69(1-2):279-305.
[128]Kim H, Kweon I S. Appearance-cloning: photoconsistent scene recovery from multi-view images. International Journal of Computer Vision,2006, 66(2):163-192.
[129]Pons J P, Keriven R, Faugeras O. Multi-view stereo reconstruction and scene flow estimation with a global image-based matching score. International Journal of Computer Vision,2007,72(2):179-193
[130]Yu L, Liu H. Feature selection for high-dimensional data:a fast correlation-based filter solution. Proc of the 20th Int. Conf on Machine Learning, ICML2003. Washington DC:[s.n.],2003.
[131]Keller D, Sahami M. Towards optimal feature selection. Proc of the 13th Int. Conference on Machine Learning.1996:284-292.
[132]Mitra P, Murthy C A, Pal S K. Unsupervised feature selection using feature similarity. IEEE Trans on Pattern Recognition and Machine Intelligence, 2002,24(3):301-312.
[133]Haykin S. Neural networks: a comprehensive foundation. 2nd ed. [S.I.]: Prentice Hall,1998.
[134]Battiti R. Using mutual information for selecting features in supervised neutral net learning. IEEE Trans on Neural Networks,1994,5:537-550.
[135]Cang S, Yu H N. A new approach for detecting the best feature set. Proc of Networking, Sensing and Control. [S.I.]:IEEE CNF,2005:74-79.
[136]Kwak N, Choi C H. Input feature, selection for classification problems. IEEE Trans on Neural Networks,2002,13(1):143-159.
[137]Davis J V, Kulis B, Jain P, et al. Information-theoretic metric learning. Proc of the 24th Int. Conf on Machine Learning. New York:ACM,2007: 209-216.
[138]贾立好,邹建华,车凯.基于头顶三维运动轨迹的身份识别新方法.自动化学报,2011,37(1)：28-36.
[139]BenAbdelkader C, Cutler R, Davis L. Stride and cadence as a biometric in automatic person identification and verification. Automatic Face and Gesture Recognition, Proceedings. Fifth IEEE International Conference,2002,357-362.
[140]Bashir Khalid, Xiang Tao, Gong Shaogang. Feature selection on gait energy image for human identification. ICASSP 2008,985-988.
[141]Wang Liang, Tan T. Silhouette analysis-based gait recognition for human identification. IEEE Trans on PAMI,2003,25(12):1505-1518.
[142]王亮,胡卫明,谭铁牛.基于步态的身份识别.计算机学报,2003,26(3)：353-360.
[143]Liu Z Y. Effect of Silhouette Quality on Hard Problem in Gait Recognition. IEEE Trans on System, Man, and Cybernetics,2005,35(2):170-183.
[144]陈昌由,张军平.基于迭代切距离原型学习算法的步态识别.计算机研究与发展,2008,45(7)：1177-118.
[145]徐佩霞.小波分析与应用实例.合肥：中国科学技术大学出版社,1996.
[146]Nastar C, Ayache N. Frequency-based Non-rigid Motion Analysis. IEEE Trans. on PAMI,1996,18(11):1067-1079.
[147]Shutler J D, Nixon M, Harris C. Statistical gait description via velocity moments. Proc IEEE Southwest Symposium on Image Analysis and Inter-pretation, Austin, Texas,2000,291-295.
[148]Foster J, Nixon M, Prugel-Bennett A. New area based metrics for gait recognition. In:Proceedings of International Conference on Audio2 and Video2based Biometric Person Authentication, Halmstad, Sweden,2001. 312-317.
[149]Hayfron Acquah J, Nixon M, Carter J. Automatic gait recognition by sym-metry analysis. In:Proceedings of International Conference on Audio- and Video-based Biometric Person Authentication, Halmstad, Sweden,2001. 272-277.
[150]Suk H, Sin B. HMM-Based Gait Recognition with Human Profiles. SSPR-SPR 2006, LNCS 4109, pp.596-603,2006.
[151]Chen Changhong, Liang Jinmin, Zhao Heng, et al. Gait recognition using Hidden Markov Model. In:L Jiao et al. ICNC 2006, Part I, LNCS 4221. Berlin Heidelberg:Springer-Verleg,2006:399-407.