基于序列统计特性的步态识别算法研究
|
摘要
步态是指人的行走方式,由于其远距离、非接触性、难于隐藏和伪装等特点,被认为是远距离情况下进行智能视觉监控的最具潜力的生物特征。步态识别是指根据人的行走方式来识别其身份,近年来引起了计算机视觉研究者们的浓厚兴趣。步态识别技术的研究可以促进计算机视觉和模式识别理论的发展,具有重要的理论意义；步态识别技术在安全敏感场所的视频监控、特殊场合的访问控制、辅助破案等方面也有着广泛的应用价值。
基于视频的步态识别技术的研究对象是包含人体运动的步态侧影图像。由于相机抖动、光线变化、遮挡等噪声的存在,使得检测到的步态侧影质量较低。因此,本文不考虑从单帧侧影中进行特征提取或匹配,而是对一定时间间隔内的所有步态图像进行统计分析和处理,将人体姿态的时空变化特性用某种统计值来代替,本质上属于统计步态识别方法。本文的研究目的在于提高步态识别算法的识别性能和校验性能,促进步态识别技术朝着实用化的方法发展。本课题的研究得到了国家自然科学基金(编号60675024)的支持。
本文的研究成果与创新点主要包括：
1)为了解决步态侧影图像质量低劣的问题,提出了一种基于概率模型的步态侧影修复算法,专门用于修复残缺的步态侧影图像。首先对所有步态侧影图像进行质量评估,自动检测出质量不高的图像；然后利用训练好的个体模型对需要修复的侧影进行序列内的模型修复；若依然存在质量较差的侧影,再利用群体模型对其进行修复。实验证明经修复后的侧影不仅质量更高且能提高算法的识别率。该算法的特点在于仅对需要修复的侧影进行更新,而且更大程度上进行的是一种自我修复,因增强了类内相似度而更利于分类。
2)以序列的均值形状为基础,本文提出了一种基于形状上下文的统计步态识别新方法。引入形状上下文描述子来表征均值形状上采样点的分布情况,得到的归一化直方图分布是一种信息量丰富的步态特征,称为形状上下文矩阵。为了减少点匹配带来的计算负担,本文通过设置统一的初始采样点及循环移位操作来实现采样点间的快速匹配。实验结果证明,引入这种新的形状描述方法可以带来算法识别性能上的收益。
3)以步态序列的均值形状为基础,本文还从形状在参考点处的局部走向和发展趋势着手,提出了一种基于切向角特征的统计步态识别新方法。计算均值形状上所有采样点的切向量在向量空间中所对应的角度值,即可得到切向角步态特征TAF。利用局部切向差距离来计算任意两个TAF之间的相似性,并用最简单的标准分类器实现步态识别。实验结果表明该方法能获得较好的识别准确率。
4)以步态序列的均值侧影为基础,提出了一种基于虚拟GEI的步态识别算法。对GEI进行位平面分解,并将所有位平面按某种权值进行加权组合可得到GEI的结构图像和细节图像,分别代表GEI的结构信息和细节信息。在复数空间中表示结构图像和细节图像,便得到虚拟步态能量图VGEI。结合复空间中的广义PCA变换对VGEI进行降维处理,得到低维的步态特征表达。经典的欧氏距离被用来计算不同步态特征之间的相似性,NN分类器被用来完成目标分类。实验结果验证了基于VGEI的步态识别算法的有效性。
5)以均值侧影为基础,提出了一种基于纹理分析的步态识别算法,从纹理分析的角度提取GEI的纹理特征来实现步态识别。提取的纹理特征包括局部变化幅度、局部标准差和局部熵等。采用相应的局部变化幅度图像、局部标准差图像和局部熵图像进行步态识别,在CASIA数据集上均表现出较好的识别性能和校验性能。实验结果说明纹理特征比原始的GEI特征具有更强的鉴别能力。
6)为了获得更优的识别性能和校验性能,本文还对基于特征融合的步态识别算法进行了探讨,提出了融合轮廓特征的步态识别算法和融合区域特征的步态识别算法。融合的轮廓特征包括序列的均值形状、形状上下文矩阵和切向角步态特征,融合的区域特征为GEI的三种纹理特征即局部变化幅度、局部标准差和局部熵。本文通过大量的实验验证了融合算法的识别性能和校验性能都优于任何特征单独作用时的效果。
Gait, which captures the manner of human walking, has been considered to be the most potential biometrics in the area of intelligent visual surveillance at a distance. It has the merit of being non-invasive, hard to conceal, being readily captured without physical contact, and executable from a distance. Gait recognition is the technique to identify humans by their walking pattern, and it has gained increasing interest from computer vision researchers recently. The study of gait recognition technique could promote the development of the theory of computer vision and pattern recognition. It also has extensive applications such as the video surveillance in security-sensitive places, access control for special occasions and assistance to catch criminals, etc.
Video-based gait recognition technology mainly concentrates on gait silhouette images composed of moving pixels. Because of various system noises such as camera shake, variation of illumination and occlusion, the quality of the detected gait silhouette is always very low. Rather than extracting or matching features from single silhouette image, this thesis deals with the entire images during a certain time interval through statistical techniques. This study can be concluded to be statistical gait recognition, because the spatial-temporal characteristics of gesture changing are represented by certain kind of statistical measurements in this research. The main purpose of this work is to improve both the identification performance and the verification performance of gait recognition algorithms, and also to furtherance the practical use of gait recognition techniques. This thesis is supported by the National Natural Science Foundation of China under grant No.60675024.
In detail, the main contributions of this thesis are as follows:
1) In order to provide a better shape presentation of gait silhouettes, we propose a probabilistic model-based silhouette refining algorithm to fill in holes and recover missing parts automatically. First, all the raw silhouettes are evaluated by a quality detection algorithm and those silhouettes with low quality can be automatically detected. Then, the distorted silhouettes in a particular sequence are refined by an individual model which is trained in advance by the current sequence. If the quality of some silhouettes is still low, then those silhouettes can be further refined by a population model. The experimental results show that the refined silhouettes not only have a better presentation but also help improve the recognition performance of the existing gait recognition algorithms. The characteristic of this refining algorithm is that it only refines the silhouette in bad quality, and the between-class similarity can be enhanced because most silhouettes are undergoing a self-updating procedure.
2) The spatial-temporal variations of silhouette contours capture the walking patterns of human being, and the mean shape of a gait image sequence can be obtained by applying the method of Procrustes shape analysis. A novel shape descriptor, shape context, is introduced to depict the distribution of the sample points on the mean shape. Shape context uses normalized histogram bins to describe the relative spatial relationship of the boundary points and offers us a powerful gait feature representation. The computation cost can be decreased by a fast point matching strategy. The experimental results indicate that the classification performance of the mean shape representation can be further promoted by introducing this shape descriptor.
3) On the basis of mean shape representation of gait sequences, this thesis proposes a novel statistical gait recognition algorithm based on tangent angle features. The tangent angle of a certain point on the mean shape is defined as the corresponding angle of the tangential vector at that point in vector space. The tangent angle is considered to reflect the local appearance and tendency at that particular point and is treated as a local discriminative feature called tangent angle features (TAF). The local tangent angle dissimilarity is used to measure the distance between two different TAFs, and the simplest standard classifiers are used to distinguish different patterns. The experimental results reveal that, the proposed algorithm outperforms other existing approaches in terms of recognition accuracy.
4) The mean silhouette of the gait sequence, which is also called gait energy image (GEI), can be decomposed into eight bit-planes. We consider that some bit-planes have more structural characteristics, while the others have more detailed features. Towards combining those bit-planes according to different weights respectively, we can get structural image and detailed image of the original GEI. They represent the structural information and detailed information respectively. A virtual gait energy image (VGEI) can be obtained by integrating the structural image and detailed image in complex space. The generalized PCA is applied to VGEI to reduce the dimensions. The classical Euclidean distance is used to measure the similarity of different gait features, and the nearest neighbor classifier is adopted to discriminate different patterns. The experiments on CASIA database testify the effectiveness of the proposed algorithm.
5) From the perspective of texture analysis, we try to extract texture features from GEI to achieve a gait recognition algorithm. The extracted texture features include local range, local standard deviation and local entropy which reflect the local variability of the intensity values of pixels in GEI. The corresponding local range images, local standard deviation images and local entropy images can be trained and applied to accomplish the gait recognition task. They show exciting identification performance and verification performance on CASIA database. It has proved that the texture features have stronger discriminative power than the original GEI.
6) In order to improve the identification performance and verification performance, we investigate the algorithm that utilizes the theory of information fusion. The gait features can be fused in two aspects:one is contour-based gait feature which includes the mean shape, the shape context matrix and the tangent angle features, and the other is area-based gait features which include the local range, local standard deviation and local entropy. Large amount of experiments have shown that, both the identification performance and verification performance can be promoted to a certain degree by fusing multiple features, and the fused features outperform any single feature when it is used individually.
基于视频的步态识别技术的研究对象是包含人体运动的步态侧影图像。由于相机抖动、光线变化、遮挡等噪声的存在,使得检测到的步态侧影质量较低。因此,本文不考虑从单帧侧影中进行特征提取或匹配,而是对一定时间间隔内的所有步态图像进行统计分析和处理,将人体姿态的时空变化特性用某种统计值来代替,本质上属于统计步态识别方法。本文的研究目的在于提高步态识别算法的识别性能和校验性能,促进步态识别技术朝着实用化的方法发展。本课题的研究得到了国家自然科学基金(编号60675024)的支持。
本文的研究成果与创新点主要包括：
1)为了解决步态侧影图像质量低劣的问题,提出了一种基于概率模型的步态侧影修复算法,专门用于修复残缺的步态侧影图像。首先对所有步态侧影图像进行质量评估,自动检测出质量不高的图像；然后利用训练好的个体模型对需要修复的侧影进行序列内的模型修复；若依然存在质量较差的侧影,再利用群体模型对其进行修复。实验证明经修复后的侧影不仅质量更高且能提高算法的识别率。该算法的特点在于仅对需要修复的侧影进行更新,而且更大程度上进行的是一种自我修复,因增强了类内相似度而更利于分类。
2)以序列的均值形状为基础,本文提出了一种基于形状上下文的统计步态识别新方法。引入形状上下文描述子来表征均值形状上采样点的分布情况,得到的归一化直方图分布是一种信息量丰富的步态特征,称为形状上下文矩阵。为了减少点匹配带来的计算负担,本文通过设置统一的初始采样点及循环移位操作来实现采样点间的快速匹配。实验结果证明,引入这种新的形状描述方法可以带来算法识别性能上的收益。
3)以步态序列的均值形状为基础,本文还从形状在参考点处的局部走向和发展趋势着手,提出了一种基于切向角特征的统计步态识别新方法。计算均值形状上所有采样点的切向量在向量空间中所对应的角度值,即可得到切向角步态特征TAF。利用局部切向差距离来计算任意两个TAF之间的相似性,并用最简单的标准分类器实现步态识别。实验结果表明该方法能获得较好的识别准确率。
4)以步态序列的均值侧影为基础,提出了一种基于虚拟GEI的步态识别算法。对GEI进行位平面分解,并将所有位平面按某种权值进行加权组合可得到GEI的结构图像和细节图像,分别代表GEI的结构信息和细节信息。在复数空间中表示结构图像和细节图像,便得到虚拟步态能量图VGEI。结合复空间中的广义PCA变换对VGEI进行降维处理,得到低维的步态特征表达。经典的欧氏距离被用来计算不同步态特征之间的相似性,NN分类器被用来完成目标分类。实验结果验证了基于VGEI的步态识别算法的有效性。
5)以均值侧影为基础,提出了一种基于纹理分析的步态识别算法,从纹理分析的角度提取GEI的纹理特征来实现步态识别。提取的纹理特征包括局部变化幅度、局部标准差和局部熵等。采用相应的局部变化幅度图像、局部标准差图像和局部熵图像进行步态识别,在CASIA数据集上均表现出较好的识别性能和校验性能。实验结果说明纹理特征比原始的GEI特征具有更强的鉴别能力。
6)为了获得更优的识别性能和校验性能,本文还对基于特征融合的步态识别算法进行了探讨,提出了融合轮廓特征的步态识别算法和融合区域特征的步态识别算法。融合的轮廓特征包括序列的均值形状、形状上下文矩阵和切向角步态特征,融合的区域特征为GEI的三种纹理特征即局部变化幅度、局部标准差和局部熵。本文通过大量的实验验证了融合算法的识别性能和校验性能都优于任何特征单独作用时的效果。
Gait, which captures the manner of human walking, has been considered to be the most potential biometrics in the area of intelligent visual surveillance at a distance. It has the merit of being non-invasive, hard to conceal, being readily captured without physical contact, and executable from a distance. Gait recognition is the technique to identify humans by their walking pattern, and it has gained increasing interest from computer vision researchers recently. The study of gait recognition technique could promote the development of the theory of computer vision and pattern recognition. It also has extensive applications such as the video surveillance in security-sensitive places, access control for special occasions and assistance to catch criminals, etc.
Video-based gait recognition technology mainly concentrates on gait silhouette images composed of moving pixels. Because of various system noises such as camera shake, variation of illumination and occlusion, the quality of the detected gait silhouette is always very low. Rather than extracting or matching features from single silhouette image, this thesis deals with the entire images during a certain time interval through statistical techniques. This study can be concluded to be statistical gait recognition, because the spatial-temporal characteristics of gesture changing are represented by certain kind of statistical measurements in this research. The main purpose of this work is to improve both the identification performance and the verification performance of gait recognition algorithms, and also to furtherance the practical use of gait recognition techniques. This thesis is supported by the National Natural Science Foundation of China under grant No.60675024.
In detail, the main contributions of this thesis are as follows:
1) In order to provide a better shape presentation of gait silhouettes, we propose a probabilistic model-based silhouette refining algorithm to fill in holes and recover missing parts automatically. First, all the raw silhouettes are evaluated by a quality detection algorithm and those silhouettes with low quality can be automatically detected. Then, the distorted silhouettes in a particular sequence are refined by an individual model which is trained in advance by the current sequence. If the quality of some silhouettes is still low, then those silhouettes can be further refined by a population model. The experimental results show that the refined silhouettes not only have a better presentation but also help improve the recognition performance of the existing gait recognition algorithms. The characteristic of this refining algorithm is that it only refines the silhouette in bad quality, and the between-class similarity can be enhanced because most silhouettes are undergoing a self-updating procedure.
2) The spatial-temporal variations of silhouette contours capture the walking patterns of human being, and the mean shape of a gait image sequence can be obtained by applying the method of Procrustes shape analysis. A novel shape descriptor, shape context, is introduced to depict the distribution of the sample points on the mean shape. Shape context uses normalized histogram bins to describe the relative spatial relationship of the boundary points and offers us a powerful gait feature representation. The computation cost can be decreased by a fast point matching strategy. The experimental results indicate that the classification performance of the mean shape representation can be further promoted by introducing this shape descriptor.
3) On the basis of mean shape representation of gait sequences, this thesis proposes a novel statistical gait recognition algorithm based on tangent angle features. The tangent angle of a certain point on the mean shape is defined as the corresponding angle of the tangential vector at that point in vector space. The tangent angle is considered to reflect the local appearance and tendency at that particular point and is treated as a local discriminative feature called tangent angle features (TAF). The local tangent angle dissimilarity is used to measure the distance between two different TAFs, and the simplest standard classifiers are used to distinguish different patterns. The experimental results reveal that, the proposed algorithm outperforms other existing approaches in terms of recognition accuracy.
4) The mean silhouette of the gait sequence, which is also called gait energy image (GEI), can be decomposed into eight bit-planes. We consider that some bit-planes have more structural characteristics, while the others have more detailed features. Towards combining those bit-planes according to different weights respectively, we can get structural image and detailed image of the original GEI. They represent the structural information and detailed information respectively. A virtual gait energy image (VGEI) can be obtained by integrating the structural image and detailed image in complex space. The generalized PCA is applied to VGEI to reduce the dimensions. The classical Euclidean distance is used to measure the similarity of different gait features, and the nearest neighbor classifier is adopted to discriminate different patterns. The experiments on CASIA database testify the effectiveness of the proposed algorithm.
5) From the perspective of texture analysis, we try to extract texture features from GEI to achieve a gait recognition algorithm. The extracted texture features include local range, local standard deviation and local entropy which reflect the local variability of the intensity values of pixels in GEI. The corresponding local range images, local standard deviation images and local entropy images can be trained and applied to accomplish the gait recognition task. They show exciting identification performance and verification performance on CASIA database. It has proved that the texture features have stronger discriminative power than the original GEI.
6) In order to improve the identification performance and verification performance, we investigate the algorithm that utilizes the theory of information fusion. The gait features can be fused in two aspects:one is contour-based gait feature which includes the mean shape, the shape context matrix and the tangent angle features, and the other is area-based gait features which include the local range, local standard deviation and local entropy. Large amount of experiments have shown that, both the identification performance and verification performance can be promoted to a certain degree by fusing multiple features, and the fused features outperform any single feature when it is used individually.
引文
[1]L. Lee, "Gait dynamics for recognition and classification," Technical Report AIM-2001-019, MIT AI Lab, Sep.2001.
[2]M. S. Nixon and J. N. Carter, "Advances in automatic gait recognition," Proc. of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp.139-144,2004.
[3]M. S. Nixon's homepage, http://www.ecs.soton.ac.uk/people/msn.
[4]Wikipedia, "Murder of James Bulger," http://en.wikipedia.org/wiki/Murder_of_James_Bulger.
[5]News from the The Times, "Aiming to catch criminals red-footed," http://business.timesonline.co.uk/tol/business/law/article685365.ece, Jul.10,2006.
[6]News from BBC, "Walk offers clues to identity," http://news.bbc.co.uk/2/hi/technology/2712995.stm, Feb.1,2003.
[7]News from ABC, "Can You Identify a Criminal By His Walk? " http://abcnews.go.com/GMA/story?id=2187105, Jul.13,2006.
[8]M. P. Murray, A. B. Drought, R. C. Kory, "Walking Patterns of Normal Men," Journal of Bone and Joint Surgery, vol.46, no.2, pp.335-360,1964.
[9]M. P. Murray, "Gait as a total pattern of movement," American Journal of Physical Medicine, vol. 46, pp.290-332, June 1967.
[10]H. J. Ralston, V. Inman, F. Todd, "Human Walking," Williams and Wilkins,1981.
[11]G. Johansson, "Visual Perception of Biological Motion and a Model for its Analysis," Perception and Psychophysics, vol.14, pp.201-211,1973.
[12]C. Barclay, J. E. Cutting, L. T. Kozlowski, "Temporal and spatial factors in gait perception that influence gender recognition," Perception and Psychophysics, vol.23, no.2, pp.145-152,1978.
[13]J. E. Cutting and L. T. Kozlowski, "Recognising friends by their walk:Gait perception without familiarity cues," Bulletin of the Psychonomic Society, vol.9, no.5, pp.353-356,1977.
[14]J. E. Cutting, D. R. Proffitt, L. T. Kozlowski, "A biochemical invariant for gait perception," Journal of Experimental Psychology:Human Perception and Performance, vol.4, pp.357-372, 1978.
[15]S. A. Niyogi and E. H. Adelson, "Analyzing and Recognizing Walking Figures in XYT," Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.469-474,1994.
[16]P. J. Phillips, "Progress in Human ID," Proc. IEEE Conference on Advanced Video and Signal Based Surveillance, pp.1-1,2003.
[17]Z. Liu, L. Malave, A. Osuntogun, P.Sudhakar, S. Sarkar, "Toward understanding the limits of gait recognition," Proc. SPIE International Symposium on Defense and Security Symposium: Biometric Technology for Human Identification, Apr.2004.
[18]于仕琪,“多视角步态分析与识别,”中国科学院自动化所：博士论文,2007.
[19]James J. Little and Jeffrey E. Boyd, "Recognizing people by their gait:the shape of motion," Videre:Journal of Computer Vision Research, vol.1, no.2 pp.2-32,1998.
[20]L. Lee and W. E. L. Grimson, "Gait analysis for recognition and classification," Proc.5th IEEE International Conference on Automatic Face and Gesture Recognition, Washington D.C., USA, pp.155-162. May 2002.
[21]Human Identification at a Distance at Gatech. http://www.cc.gatech.edu/cpl/projects/hid.
[22]Human ID at CMU. http://www.hid.ri.cmu.edu.
[23]R. Gross and J. Shi. "The CMU Motion of Body (MoBo) Database," Technical Report CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon University, Jun.2001.
[24]Automatic Gait Recognition for Human ID at a Distance at Soton. http://www.gait.ecs.soton.ac.uk.
[25]J. D. Shutler, M. G. Grant, M. S. Nixon, J. N. Carter, "On a large sequence-based human gait database," Proc. of 4th International Conference on Recent Advances in Soft Computing, Nottingham, UK, pp.66-71.2002.
[26]Human ID Challenge Problem at USE http://figment.csee.usf.edu/GaitBaseline.
[27]S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother, K. W. Bowyer, "The humanid gait challenge problem:Data sets, performance, and analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.2, pp:162-177, Feb.2005.
[28]Center for Biometrics and Chinese Academy of Scienses Security Research, Institute of Automation. http://www.cbsr.ia.ac.cn.
[29]Shiqi Yu, Daoliang Tan, Tieniu Tan, "A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition," Proc.18' th International Conference on Pattern Recognition, Hong Kong, China, pp.441-444, Aug.2006.
[30]王亮,胡卫明,谭铁牛,“人运动的视觉分析综述,”计算机学报,vol.25, no.3, pp.225-237,2002.
[31]I. Haritaoglu, D. Harwood, L. Davis, "W4S:Who? When? Where? What? A Real-time System for Detecting and Tracking People," Proc. International Conference on Automatic Face and Gesture Recognition, pp.222-227,1998.
[32]E. Stringa and C. S. Regazzoni, "Real-time Video-shot Detection for Scene Surveillance Applications," IEEE Transactions on Image Processing, vol.1, no.9, pp.69-79,2000.
[33]K. Toyama, J. Krumm, B. Brumitt, B. Meyers, "Wallflower:Principles and Practice of Background Maintenance," Proc. International Conference on Computer Vision, pp.255-261, 1999.
[34]A. Lipton, H. Fujiyoshi, R. Patil, "Moving Target Classification and Tracking from Realtime Video," Proc. IEEE Workshop on Applications of Compmer Vision, pp.129-136,1998.
[35]G. L. Foresti, "Object Recognition and Tracking for Remote Video Surveillance," IEEE Transactions on Circuits and Systems for Video Technology, vol.9, no.7, pp.1045-1062,1999.
[36]J. Shin, S. Kim, S. Kang, et al, "Optical flow-based real-time object tracking using non-prior training active feature model," Real-Time Imaging, vol.11, no.3,2005.
[37]D. Meyer, J. Denzler, H. Niemann, "Model based extraction of articulated objects in image sequences for gait analysis," Proc. International Conference on Image Processing, pp.78-81, 1997.
[38]J. Barron, D. Fleet, S. Beauchemin, "Performance of optical flow techniques," International Journal of Computer Vision, pp.12, no.1, pp.42-77,1994.
[39]R. Okada, Y. Shirai, J. Miura, "Object tracking based on optical flow and depth," Proc. IEEE/ SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp.565-571,1996.
[40]马勤勇,“基于步态的身份识别研究,”浙江大学：博士论文,2008.
[41]J. Han and B. Bhanu, "Human Activity Recognition in Thermal Infrared Imagery," Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.126-133,2005.
[42]G. V. Veres, L. Gordon, J. N. Carter, M. S. Nixon, "What image information is important in silhouette-based gait recognition," Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.776-782,2004.
[43]J. P. Foster, M. S. Nixon, A. Prugel-Bennett, "Automatic gait recognition using area-based metrics," Pattern Recognition Letters, vol.34, no.14, pp.2489-2497,2003.
[44]N. V. Boulgouris, D. Hatzinakos, K. N. Plataniotis, "Gait Recognition:A challenging signal processing technology for biometric identification," IEEE Signal Processing Magazine, vol.22, no.6, pp.78-90,2005.
[45]J. D. Shutler, M. S. Nixon, "Statistical Gait Recognition via Velocity Moments," Proc. IEE Colloquium on Visual Biometrics, London, UK, vol.10, pp.1-5,2000.
[46]H. Su, E. G. Huang, "Markerless Human Gait Recognition by Shape and Motion Analysis," Proc. International Conference on Intelligent Sensing and Information Processing, pp.161-165, 2005.
[47]C. S. Lee and A. Elgammal, "Gait style and gait content:bilinear models for gait recognition using gait re-sampling," Proc. International Conference on Automatic Face and Gesture Recognition, pp.147-152,2004.
[48]R. Sagawa, Y. Makihara, T. Echigo, Y. Yagi, "Matching Gait Image Sequences in the Frequency Domain for Tracking People at a Distance," Proc.7th Asian Conference on Compnter Vision, pp.141-150,2006.
[49]Z. Liu and S. Sarkar, "Simplest Representation yet for Gait Recognition:Averaged Silhouette," Proc. International Conference on Pattern Recognition, Cambridge, UK, pp.211-214,2004.
[50]A. Kale, A. Sundaresan, A. N. Rajagopalan, N. Cuntoor, A. Roychowdhury, V. Krueger, "Identification of Humans Using Gait," IEEE Transactions on Image Processing, vol.13, no.9, pp.1163-1173,2004
[51]Liang Wang, Huazhong Ning, Weiming Hu, Tieniu Tan, "Gait recognition based on Procrustes shape analysis," Proc.2002 International Conference on Image Processing, Rochester, USA, vol.3, pp.433-436,2002.
[52]Liang Wang, Tieniu Tan, Weiming Hu, Huazhong Ning, "Automatic gait recognition based on statistical shape analysis," IEEE Transactions on Image Processing, vol.12, no.9, pp. 1120-1131,2003.
[53]V. Perlibakas, "Distance measures for PCA-based face recognition," Pattern Recognition Letters, vol.25, no.6, pp.711-724, Apr.2004.
[54]N. Vasconcelos and A. Lippman, "A multiresolution manifold distance for invariant image similarity," IEEE Transactions on Multimedia,vol.7, no.1, pp.127-142,2005.
[55]陈实,黄万红,“基于hausdorff距离的人体步态识别,”浙江万里学院学报,vol.20, no.5, pp. 10-13, Sep.2007.
[56]陈实,马天峻,高有行,“用轮廓的点分布特征分析和识别步态,”计算机工程与应用,vol.44, no.2, pp.26-28,2008.
[57]王亮,胡卫明,谭铁牛,“基于步态的身份识别,”计算机学报,vol.26, no.3, pp.353-360,2003.
[58]Liang Wang, Tieniu Tan, Huazhong Ning, Weiming Hu, "Silhouette Analysis-Based Gait Recognition for Human Identification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, no.12, pp.1505-1518.2003.
[59]C. BenAbdelkader, R. Cutler, L. Davis, "Motion-based recognition of people in EigenGait space," Proc. Filth IEEE International Conference on Automatic Face and Gesture Recognition, pp.254-259, May.2002.
[60]T. H. W. Lam, R. S. T. Lee, D. Zhang, "Human gait recognition by the fusion of motion and static spatio-temporal templates," Pattern Recognition, vol.40, no.9, pp.2563-2573,2007.
[61]C. E. Thomaz, D. F. Gillies, R. Q. Feitosa, "A new covariance estimate for Bayesian classifiers in biometric recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol.14, no.2, pp.214-223,2004.
[62]A. N. M. Gomatam and S. Sasi, "Enhanced gait recognition using HMM and Vh techniques," Proc. International Workshop on Imaging Systems and Techniques, pp.144-147,2004.
[63]M. H. Cheng, M. F. Ho, C. L. Huang, "Gait analysis for human identification through manifold learning and HMM," Pattern Recognition, vol.41, no.8, pp.2541-2553, Aug.2008.
[64]F. Samaria and S. Young, "HMM-based architecture for face identification," Image and Vision Compution, vol.12, no.8, pp.537-543,1994.
[65]A. Nefian and M. H. Hayes Ⅲ, "An embedded HMM for face detection and recognition," Proc. IEEE International conference on Acoustics, Speech, and Signal Process, vol.6, pp.3553-3556,1999.
[66]S. Luhr, H. H. Bui, S. Venkatesh, G A. W. West, "Recognition of Human Activity through Hierarchical Stochastic Learning," Proc. IEEE International Conference on Pervasive Computing and Communications, Texas, USA, pp.416-422,2003.
[67]T. V. Duong, H. H. Bui, D. Q. Phung, S. Venkatesh, "Activity recognition and abnormality detection with the switching hidden semi-markov model," Proc. IEEE International Conference on Computer Vision and Pattern ecognition, San Diego, USA, pp.838-845,2005.
[68]R. K. Begg, M. Palaniswami, B. Owen, "Support Vector Machines for Automated Gait classification," IEEE Transactions on Biomedical Engineering, vol.52, no.5, pp.828-838, 2005.
[69]J. Lu and E. Zhang, "Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion," Pattern Recognition Letters, vol.28, no.16, pp.2401-2411, 2007.
[70]H. Lee, S. Hong, E. Kim, "Neural network ensemble with probabilistic fusion and its application to gait recognition," Neurocomputing, vol.72, no.7, pp.1557-1564,2009.
[71]M. S. Nixon, J. N. Carter, D. Cunado, P. S. Huang, S. V. Stevenage, "Automatic Gait Recognition," Biometrics:Personal Identification in Networked Society, pp.231-249,2002.
[72]J. H. Yoo, M. S. Nixon, C. J. Harris, "Model-Driven Statistical Analysis of Human Gait Motion," Proc. International Conference on Image Processing, pp.285-288,2002.
[73]D. K. Wagg, M. S. Nixon, "On Automated Model-Based Extraction and Analysis of Gait," Proc.6th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 11-16,2004.
[74]R. Zhang, C. Vogler, D. Metaxas, "Human Gait Recognition," Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.18-25, Jun.2004.
[75]杨军,吴晓娟,彭彰,陈文刚,“基于多区域分割的步态表示与识别算法研究,”计算机学报,vol.26, no.10, pp.1876-1881,2006.
[76]Liang Wang, Huazhong Ning, Tieniu Tan, Weiming Hu, "Fusion of Static and Dynamic Body Biometrics for Gait Recognition," Proc. Ninth IEEE International Conference on Computer Vision, vol.14, no.2, pp.149-158,2004.
[77]Liang Wang, Huazhong Ning, Tieniu Tan, Weiming Hu, "Fusion of Static and Dynamic Body Biometrics for Gait Recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol.14, no.2, pp.149-158, Feb.2004.
[78]Shiloh L. Dockstader and A. Murat Tekalp, "A Kinematic Model for Human Motion and Gait Analysis," Proc. Workshop on Statistical Methods in Video Processing (ECCV), Copenhagen, Denmark, pp.49-54, Jun.2002.
[79]Shiloh L. Dockstader, Michel J. Berg, A. Murat Tekalp, "Stochastic kinematic modeling and feature extraction for gait analysis," IEEE Transactions on Image Processing, vol.12, no.8, pp. 962-976,2003.
[80]R. Urtasun, P. Fua, "3D Tracking for Gait Characterization and Recognition," Proc. Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp.17-22,2004.
[81]R. C. Gonzalez, R. E. Woods, "Digital image processing," Second Edition, Prentice Hall, 2002.
[82]R. Zhang, A. Vashist, I. Muchnik, C. Kulikowski, D. Metaxas, "A New Combinatorial Approach to Supervised Learning:Application to Gait Recognition," Proc. International workshop on Analysis and modeling of faces and gestures, vol.3723, pp.55-69,2005.
[83]S. D. Mowbray and M. S. Nixon, "Automatic gait recognition via Fourier descriptors of deformable objects," Proc. International Conference on Audio-and Video-Based Biometric Person Authentication, Guildford, UK, vol.2688, pp.566-573,2003.
[84]S. D. Mowbray and M. S. Nixon, "Extraction and recognition of periodically deforming objects by continuous, spatio-temporal shape description," Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington DC, USA, pp.895-901, 2004.
[85]Shiqi Yu, Liang Wang, Weiming Hu, Tieniu Tan, "Gait analysis for human identification in frequency domain," Proc.3rd International Conference on Image and Graphics, Hong Kong, China, pp.282-285, Dec.2004.
[86]A. F. Bobick and J. W. Davis, "The recognition of human movement using temporal templates," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, no.3, pp. 257-267,2001.
[87]J. Han and B. Bhanu, "Statistical feature lusion for gait-based human recognition," Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington DC, USA, vol.2, pp.842-847, Jun.2004.
[88]J. Han and B. Bhanu, "Individual recognition using Gait Energy Image," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, no.2, pp.316-322,2006.
[89]X. Zou and B. Bhanu, "Human activity classification based on gait energy image and coevolutionary genetic programming," Proc.18th International Conference on Pattern Recognition, Hong Kong, China, vol.3 pp.556-559, Aug.2006.
[90]马勤勇,王申康,聂栋栋,邱剑锋,“基于瞬时步态能量图的远距离身份识别,”电子学报,vol.35,no.11,pp.2078-2082,2007.
[91]马勤勇,聂栋栋,王申康,“基于能量图分解与运动偏移特性的步态识别,”光电子·激光,vol.20,no.4,pp.545-549,2009.
[92]C. Chen, J. Liang, H. Zhao, H. Hu, J. Tian, "Frame difference energy image for gait recognition with incomplete silhouettes," Pattern Recognition Letters, vol.30, no.11, pp. 977-984,2009.
[93]C Liu, H Wechsler, "Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition," IEEE Transactions on Image Processing, vol.11, no.4, pp.467-476,2002.
[94]杨晓超,周越,张一,署光,王力,“基于增强步态能量图和Gabor特征的辨别共同向量身份识别,”上海交通大学学报,vol.42, no.12, pp.1988-1992,2008.
[95]X. Yang, Y. Zhou, T. Zhang, G. Shu, J. Yang, "Gait recognition based on dynamic region analysis,:Signal Processing, vol.88, no.9, pp.2350-2356,2008.
[96]D. Tao, X. Li, X. Wu, S. J. Maybank, "General Tensor Discriminant Analysis and Gabor Features for Gait Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, no.10, pp.1700-1715,2007.
[97]X Yang, Y Zhou, T Zhang, E Zheng, J Yang, "Gabor phase based gait recognition," Electronics Letters, vol.44, no.10, pp.620-621,2008.
[98]杨晓超,周越,署光,张田昊,“基于Gabor相位谱和流型学习的步态识别方法,”电子学报,vol.37, no.4, pp.753-757,2009.
[99]J. Liu, N. Zheng, "Gait history image:A novel temporal template for gait recognition," Proc. IEEE International Conference on Multimedia and Expo, Beijing, China, pp.663-666, Jul. 2007.
[100]H. Lee, S. Hong, I. F. Nizami, E. Kim, "A noise robust gait representation:Motion energy image," International Journal of Control, Automation and Systems, vol.7, no.4, pp.638-643, 2009.
[101]N. V. Boulgouris, and Z. X. Chi, "Gait recognition using radon transform and linear discriminant analysis," IEEE Transcation on Image Processing, vol.16, no.3, pp.731-740, 2007.
[102]王科俊,贲晛烨,刘丽丽,陈薇,“基于能量的信息融合步态识别,”华中科技大学学报(自然科学版),vol.37, no.5, pp.14-17, May.2009.
[103]E. Zhang, H. Ma, J. Lu, Y. Chen, "Gait recognition using dynamic gait energy and PCA+LPP method," Proc. International Conference on Machine Learning and Cybernetics, Baoding, China, vol.1, pp.50-53,2009.
[104]R. Collins, R. Gross, J. Shi, "Silhouette-based human identification from body shape and gait," Proc. IEEE International Conference on Automatic Face and Gesture Recognition, pp. 366-371,2002.
[105]A. Kale, N. Cuntoor, B. Yegnanarayana, A.N. Rajagopalan, R. Chellappa, "Gait Analysis for Human Identification," Proc. Third International Conference on Audio- and Video-Based Biometric Person Authentication, pp.706-714,2003.
[106]J. B. Hayfron-Acquah, M. S. Nixon, J. N. Carter, "Recognising Human and Animal Movement by Symmetry," Proc. International Conference on Image Processing, pp.290-293,2001.
[107]J. B. Hayfron-Acquah, M. S. Nixon, J. N. Carter, "Automatic gait recognition by synunetry analysis," Pattern Recognition Letters, vol.24, no.13, pp.2175-2183,2003.
[108]赵国英,向世明,李华,“基于反射对称的步态序列识别,”计算机辅助设计与图形学报,vol.17, no.10, pp.2239-2244,2005.
[109]A. K. Jain, R. M. Bolle, S. Pankanti, "Biometrics:Personal Identification in a Networked Society," Norwell, MA:Kluwer,1999.
[110]A. A. Ross, K. Nandakumar, A. K. Jain, "Handbook of Multibiometrics," Springer, Berlin, 2006.
[111]G. Shakhnarovich, L. Lee, T. Darrell, "Integrated face and gait recognition from multiple views," Proc. Computer Vision and Pattern Recognition, vol.1, pp.439-446,2001.
[112]G. Shakhnarovich and T. Darrell, "On probabilistic combination of face and gait cues for identification," Proc. International Conference on Automatic Face and Gesture Recognition, vol.5, pp.169-174,2002.
[113]A. Kale, A. Roy-chowdhury, R. Chellappa, "Fusion of gait and face for human identification," Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol.5, pp. 901-904,2004.
[114]X. Zhou, B. Bhanu, J Han, "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, pp.165-181,2005.
[115]X. Zhou and B. Bhanu, "Feature fusion ef face and gait for human recognition at a distance in video" Proc.18th International Conference on Pattern Recognition, pp.529-532,2006.
[116]X. Zhou and B. Bhanu, "Integrating face and gait for human recognition at a distance in video," IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, vol.37, no.5, pp.1119-1137,2007.
[117]X. Zhou and B. Bhanu, "Feature fusion of side face and gait for video-based human identification," Pattern Recognition, vol.41, no.3, pp.778-795,2008.
[118]Z. Liu, S. Sarkar, "Out door recognition at a distance by fusing gait and face," Image and Vision Computing, vol.25, pp.817-832,2007.
[119]Q. Li, Z. Lu, D. Zhang, "Integration of Gait and Side Face for Human Recognition in Video," Proc. Second International Symposium on Electronic Commerce and Security, pp.65-69, 2009.
[120]T. Savic and N. Pavesic, "Personal recognition based on an image of the palmar surface of the hand," Pattern Recognition, vol.40, no.11, pp.3152-3163,2007.
[121]P. J. Philips, H. Moon, S. A. Rizvi, P. J. Rauss, "The FERET evaluation methodology for face-recognition algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, no.10, pp.1090-1104, Oct.2000.
[122]Y. Kuno, T. Watanabe, Y. Shimosakoda, S. Nakagawa, "Automated detection of human for visual surveillance system," Proc. International Conference on Pattern Recognition, Vienna, Austria, pp.865-869,1996.
[123]T. W. Ridler, S. Calvard, "Picture thresholding using an iterative selection method," IEEE Transactions on Systems, Man, and Cybernetics, vol.8, pp.630-632,1978.
[124]L. Lee, G. Dalley, K. Tieu, "Learning Pedestrian Models for Silhouette Refinement," Proc. Ninth IEEE International Conference on Computer Vision, IEEE CS, pp.663-670, Oct.2003.
[125]Z. Liu, L. Malave, S. Sarkar, "Studies on Silhouette Quality and Gait Recognition," Proc. IEEE Conference on Computer Vision and PaRem Recognition, pp.704-711,2004.
[126]Z. Liu, S. Sarkar, "Effect of Silhouette Quality on Hard Problems in Gait Recognition," IEEE Transactions on Systems, Man, and Cybernetics, vol.35, no.2, pp.170-183,2005.
[127]J. Bernoulli, "The Art of Conjecturing," Johns Hopkins University Press, Baltimore,2006. English translation with notes by E. D. Sylla of the book Ars Conjectandi first published in 1713.
[128]M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, vol.3, no.1, pp.71-86,1991.
[129]M Kirby and L. Sirovich, "Application of the karhunen-loeve procedure for the characterization of human faces," IEEE Transactions on Pattern Analysis and Machinelntelligence, vol.12, no.1, pp.103-108,1990.
[130]R. Cutler and L. Davis, "Robust real-time periodic motion detection, analysis, and applications," IEEE Transactions on Pattern Analysis andMachine Intelligence, vol.22, no.8, pp.781-796,2000.
[131]C. BenAbdelkader, R. G. Cutler, L. S. Davis, "Stride and cadence as a biometric in automatic person identification and verification," Proc. Fifth IEEE International Conference on Automatic Face Gesture Recognition, Washington DC, USA, pp.372-377,2002.
[132]郭大钧,“大学数学手册,”第一版,山东科学技术出版社,1985.
[133]J. T. Kent, "New directions in shape analysis," Art of Statistical Science:A Tribute to G. S. Watson. NewYork, USA:Wiley, pp.115-127,1992.
[134]C. Zahn and R. Roskies, "Fourier Descriptors for Plane Closed Curves," IEEE Transactions on Computers, vol.21, no.3, pp.269-281, Mar.1972.
[135]L. M. J. Florack, B. M. T. Haar Romeny, J. J. Koendednk, M. A. Viergever, "General Intensity Transformations and Second Order Invadants," Proc.7th Scandinavian Conference on Image Analysis, Aalborg, Denmark, pp.338-445,1991.
[136]W. T. Freeman and E. H. Adelson, "The Design and Use of Steerable Filters,", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, no.9, pp.891-906,1991.
[137]K. Mikolajczyk and C. Schmid, "A Performance Evaluation of Local Descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.10, pp.1615-1630, 2005.
[138]D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol.60, no.2, pp.91-110,2004.
[139]S. Belongie, J. Malik, J. Puzicha, "Matching shapes," Proc. Eighth IEEE International Conference on Computer Vision, vol.1, pp.454-461, Jul.2001.
[140]S. Belongie, J. Malik, J. Puzicha, "Shape Matching and Object Recognition Using Shape Contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, no.4, pp. 509-522,2002.
[141]C. H. Papadimitriou, K. Steiglitz, "Combinatorial optimization:algorithms and complexity," Prentice-Hall, Englewood Cliffs,1982.
[142]陈实,马天骏,黄万红,高有行,“基于形状上下文描述子的步态识别,”模式识别与人工智能,vol.20, no.6, pp.794-799,2007.
[143]J. E. Boyd, "Synchronization of Oscillations for Machine Perception of Gaits," Computer Vision and Image Understanding, vol.96, no.1, pp.35-59,2004.
[144]A. Bazin, M. S. Nixon, "Gait verification using probabilistic methods," Proc. IEEE Workshop on Applications of Computer Vision, pp.60-65,2005.
[145]S. Chen, Y. Gao, "Stride history image:a new feature representation for pedestrian identification," Proc. IEEE Workshop on Signal Processing Systems, pp.543-547,2007.
[146]S. Chen, W. Huang, T. Ma, L. Dong, "Towards feature fusion for human identification by gait," Proc. Fourth International Conference on Image and Graphics, pp.678-682, Aug.2007.
[147]陈实,马天骏,黄万红,高有行,“用于步态识别的多层窗口图像矩,”电子与信息学报,vol.31, no.1, pp.116-119,2009.
[148]H. Wang, Y. Leng, Z. Wang, X. Wu, "Application of Image Correction and Bit-plane Fusion in Generalized PCA Based Face Recognition," Pattern Recognition Letters, vol.28, no.16, pp. 2352-2358, Dec.2007.
[149]J. Yang, J. Yang, "Generalized K-L transform based combined feature extraction," Pattern Recognition, vol.35, no.1, pp.295-297,2002.
[150]K. I. Kim, K. Jung, S. H. Park, H. J. Kim, "Support Vector Machines for Texture Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, no.11, pp.1542-1550,2002.
[151]H. Tamura, S. Moil, Y. Yamawaki, "Texture Feature Corresponding to Visual Perception," IEEE Transactions on Systems, Man, and Cybernetics, vol.8, pp.460-473,1978.
[152]C. Bovik, M. Clark, W. S. Geisler, "Mulfichannel Texture Analysis Using Localized Spatial Filers," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, no.1, pp. 55-73,1990.
[153]R. M. Haralick, K. Shanmugam, I. H. Dinstein, "Textural features for image classification," IEEE Transactions on Systems, Man, and Cybernetics,vol.3, no.6, pp.610-621.1973.
[154]C. E. Shannon and W. Weaver, "The Mathematical theory of Communication," University of Illinois Press, Urbana, IL,1949.
[155]孙即祥,“现代模式识别,”高等教育出版社,第二版,2008.
[156]A. Jain, K. Nandakumar, A. Ross, "Score normalization in multimodal biometric systems," Pattern recognition, vol.38, no.12, pp.2270-2285, Dec.2005.
[157]R. Snelick, U. Uludag, A. Mink, M. Indovina, A. Jain, "Large-Scale Evaluation of multimodal biometric authentication using state-of-the-art systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.3, pp.450-455, Mar.2005.
[158]A. Ross, A. K. Jain, "Information fusion in biometrics," Pattern Recognition Letters, vol.24, no,13, pp.2115-2125,2003.
[159]J. Kittler, M. Hatef, R. P. W. Duin, J. Matas, "On combining classifiers," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, no.3, pp.226-239,1998.
[2]M. S. Nixon and J. N. Carter, "Advances in automatic gait recognition," Proc. of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp.139-144,2004.
[3]M. S. Nixon's homepage, http://www.ecs.soton.ac.uk/people/msn.
[4]Wikipedia, "Murder of James Bulger," http://en.wikipedia.org/wiki/Murder_of_James_Bulger.
[5]News from the The Times, "Aiming to catch criminals red-footed," http://business.timesonline.co.uk/tol/business/law/article685365.ece, Jul.10,2006.
[6]News from BBC, "Walk offers clues to identity," http://news.bbc.co.uk/2/hi/technology/2712995.stm, Feb.1,2003.
[7]News from ABC, "Can You Identify a Criminal By His Walk? " http://abcnews.go.com/GMA/story?id=2187105, Jul.13,2006.
[8]M. P. Murray, A. B. Drought, R. C. Kory, "Walking Patterns of Normal Men," Journal of Bone and Joint Surgery, vol.46, no.2, pp.335-360,1964.
[9]M. P. Murray, "Gait as a total pattern of movement," American Journal of Physical Medicine, vol. 46, pp.290-332, June 1967.
[10]H. J. Ralston, V. Inman, F. Todd, "Human Walking," Williams and Wilkins,1981.
[11]G. Johansson, "Visual Perception of Biological Motion and a Model for its Analysis," Perception and Psychophysics, vol.14, pp.201-211,1973.
[12]C. Barclay, J. E. Cutting, L. T. Kozlowski, "Temporal and spatial factors in gait perception that influence gender recognition," Perception and Psychophysics, vol.23, no.2, pp.145-152,1978.
[13]J. E. Cutting and L. T. Kozlowski, "Recognising friends by their walk:Gait perception without familiarity cues," Bulletin of the Psychonomic Society, vol.9, no.5, pp.353-356,1977.
[14]J. E. Cutting, D. R. Proffitt, L. T. Kozlowski, "A biochemical invariant for gait perception," Journal of Experimental Psychology:Human Perception and Performance, vol.4, pp.357-372, 1978.
[15]S. A. Niyogi and E. H. Adelson, "Analyzing and Recognizing Walking Figures in XYT," Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.469-474,1994.
[16]P. J. Phillips, "Progress in Human ID," Proc. IEEE Conference on Advanced Video and Signal Based Surveillance, pp.1-1,2003.
[17]Z. Liu, L. Malave, A. Osuntogun, P.Sudhakar, S. Sarkar, "Toward understanding the limits of gait recognition," Proc. SPIE International Symposium on Defense and Security Symposium: Biometric Technology for Human Identification, Apr.2004.
[18]于仕琪,“多视角步态分析与识别,”中国科学院自动化所：博士论文,2007.
[19]James J. Little and Jeffrey E. Boyd, "Recognizing people by their gait:the shape of motion," Videre:Journal of Computer Vision Research, vol.1, no.2 pp.2-32,1998.
[20]L. Lee and W. E. L. Grimson, "Gait analysis for recognition and classification," Proc.5th IEEE International Conference on Automatic Face and Gesture Recognition, Washington D.C., USA, pp.155-162. May 2002.
[21]Human Identification at a Distance at Gatech. http://www.cc.gatech.edu/cpl/projects/hid.
[22]Human ID at CMU. http://www.hid.ri.cmu.edu.
[23]R. Gross and J. Shi. "The CMU Motion of Body (MoBo) Database," Technical Report CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon University, Jun.2001.
[24]Automatic Gait Recognition for Human ID at a Distance at Soton. http://www.gait.ecs.soton.ac.uk.
[25]J. D. Shutler, M. G. Grant, M. S. Nixon, J. N. Carter, "On a large sequence-based human gait database," Proc. of 4th International Conference on Recent Advances in Soft Computing, Nottingham, UK, pp.66-71.2002.
[26]Human ID Challenge Problem at USE http://figment.csee.usf.edu/GaitBaseline.
[27]S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother, K. W. Bowyer, "The humanid gait challenge problem:Data sets, performance, and analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.2, pp:162-177, Feb.2005.
[28]Center for Biometrics and Chinese Academy of Scienses Security Research, Institute of Automation. http://www.cbsr.ia.ac.cn.
[29]Shiqi Yu, Daoliang Tan, Tieniu Tan, "A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition," Proc.18' th International Conference on Pattern Recognition, Hong Kong, China, pp.441-444, Aug.2006.
[30]王亮,胡卫明,谭铁牛,“人运动的视觉分析综述,”计算机学报,vol.25, no.3, pp.225-237,2002.
[31]I. Haritaoglu, D. Harwood, L. Davis, "W4S:Who? When? Where? What? A Real-time System for Detecting and Tracking People," Proc. International Conference on Automatic Face and Gesture Recognition, pp.222-227,1998.
[32]E. Stringa and C. S. Regazzoni, "Real-time Video-shot Detection for Scene Surveillance Applications," IEEE Transactions on Image Processing, vol.1, no.9, pp.69-79,2000.
[33]K. Toyama, J. Krumm, B. Brumitt, B. Meyers, "Wallflower:Principles and Practice of Background Maintenance," Proc. International Conference on Computer Vision, pp.255-261, 1999.
[34]A. Lipton, H. Fujiyoshi, R. Patil, "Moving Target Classification and Tracking from Realtime Video," Proc. IEEE Workshop on Applications of Compmer Vision, pp.129-136,1998.
[35]G. L. Foresti, "Object Recognition and Tracking for Remote Video Surveillance," IEEE Transactions on Circuits and Systems for Video Technology, vol.9, no.7, pp.1045-1062,1999.
[36]J. Shin, S. Kim, S. Kang, et al, "Optical flow-based real-time object tracking using non-prior training active feature model," Real-Time Imaging, vol.11, no.3,2005.
[37]D. Meyer, J. Denzler, H. Niemann, "Model based extraction of articulated objects in image sequences for gait analysis," Proc. International Conference on Image Processing, pp.78-81, 1997.
[38]J. Barron, D. Fleet, S. Beauchemin, "Performance of optical flow techniques," International Journal of Computer Vision, pp.12, no.1, pp.42-77,1994.
[39]R. Okada, Y. Shirai, J. Miura, "Object tracking based on optical flow and depth," Proc. IEEE/ SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp.565-571,1996.
[40]马勤勇,“基于步态的身份识别研究,”浙江大学：博士论文,2008.
[41]J. Han and B. Bhanu, "Human Activity Recognition in Thermal Infrared Imagery," Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.126-133,2005.
[42]G. V. Veres, L. Gordon, J. N. Carter, M. S. Nixon, "What image information is important in silhouette-based gait recognition," Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.776-782,2004.
[43]J. P. Foster, M. S. Nixon, A. Prugel-Bennett, "Automatic gait recognition using area-based metrics," Pattern Recognition Letters, vol.34, no.14, pp.2489-2497,2003.
[44]N. V. Boulgouris, D. Hatzinakos, K. N. Plataniotis, "Gait Recognition:A challenging signal processing technology for biometric identification," IEEE Signal Processing Magazine, vol.22, no.6, pp.78-90,2005.
[45]J. D. Shutler, M. S. Nixon, "Statistical Gait Recognition via Velocity Moments," Proc. IEE Colloquium on Visual Biometrics, London, UK, vol.10, pp.1-5,2000.
[46]H. Su, E. G. Huang, "Markerless Human Gait Recognition by Shape and Motion Analysis," Proc. International Conference on Intelligent Sensing and Information Processing, pp.161-165, 2005.
[47]C. S. Lee and A. Elgammal, "Gait style and gait content:bilinear models for gait recognition using gait re-sampling," Proc. International Conference on Automatic Face and Gesture Recognition, pp.147-152,2004.
[48]R. Sagawa, Y. Makihara, T. Echigo, Y. Yagi, "Matching Gait Image Sequences in the Frequency Domain for Tracking People at a Distance," Proc.7th Asian Conference on Compnter Vision, pp.141-150,2006.
[49]Z. Liu and S. Sarkar, "Simplest Representation yet for Gait Recognition:Averaged Silhouette," Proc. International Conference on Pattern Recognition, Cambridge, UK, pp.211-214,2004.
[50]A. Kale, A. Sundaresan, A. N. Rajagopalan, N. Cuntoor, A. Roychowdhury, V. Krueger, "Identification of Humans Using Gait," IEEE Transactions on Image Processing, vol.13, no.9, pp.1163-1173,2004
[51]Liang Wang, Huazhong Ning, Weiming Hu, Tieniu Tan, "Gait recognition based on Procrustes shape analysis," Proc.2002 International Conference on Image Processing, Rochester, USA, vol.3, pp.433-436,2002.
[52]Liang Wang, Tieniu Tan, Weiming Hu, Huazhong Ning, "Automatic gait recognition based on statistical shape analysis," IEEE Transactions on Image Processing, vol.12, no.9, pp. 1120-1131,2003.
[53]V. Perlibakas, "Distance measures for PCA-based face recognition," Pattern Recognition Letters, vol.25, no.6, pp.711-724, Apr.2004.
[54]N. Vasconcelos and A. Lippman, "A multiresolution manifold distance for invariant image similarity," IEEE Transactions on Multimedia,vol.7, no.1, pp.127-142,2005.
[55]陈实,黄万红,“基于hausdorff距离的人体步态识别,”浙江万里学院学报,vol.20, no.5, pp. 10-13, Sep.2007.
[56]陈实,马天峻,高有行,“用轮廓的点分布特征分析和识别步态,”计算机工程与应用,vol.44, no.2, pp.26-28,2008.
[57]王亮,胡卫明,谭铁牛,“基于步态的身份识别,”计算机学报,vol.26, no.3, pp.353-360,2003.
[58]Liang Wang, Tieniu Tan, Huazhong Ning, Weiming Hu, "Silhouette Analysis-Based Gait Recognition for Human Identification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, no.12, pp.1505-1518.2003.
[59]C. BenAbdelkader, R. Cutler, L. Davis, "Motion-based recognition of people in EigenGait space," Proc. Filth IEEE International Conference on Automatic Face and Gesture Recognition, pp.254-259, May.2002.
[60]T. H. W. Lam, R. S. T. Lee, D. Zhang, "Human gait recognition by the fusion of motion and static spatio-temporal templates," Pattern Recognition, vol.40, no.9, pp.2563-2573,2007.
[61]C. E. Thomaz, D. F. Gillies, R. Q. Feitosa, "A new covariance estimate for Bayesian classifiers in biometric recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol.14, no.2, pp.214-223,2004.
[62]A. N. M. Gomatam and S. Sasi, "Enhanced gait recognition using HMM and Vh techniques," Proc. International Workshop on Imaging Systems and Techniques, pp.144-147,2004.
[63]M. H. Cheng, M. F. Ho, C. L. Huang, "Gait analysis for human identification through manifold learning and HMM," Pattern Recognition, vol.41, no.8, pp.2541-2553, Aug.2008.
[64]F. Samaria and S. Young, "HMM-based architecture for face identification," Image and Vision Compution, vol.12, no.8, pp.537-543,1994.
[65]A. Nefian and M. H. Hayes Ⅲ, "An embedded HMM for face detection and recognition," Proc. IEEE International conference on Acoustics, Speech, and Signal Process, vol.6, pp.3553-3556,1999.
[66]S. Luhr, H. H. Bui, S. Venkatesh, G A. W. West, "Recognition of Human Activity through Hierarchical Stochastic Learning," Proc. IEEE International Conference on Pervasive Computing and Communications, Texas, USA, pp.416-422,2003.
[67]T. V. Duong, H. H. Bui, D. Q. Phung, S. Venkatesh, "Activity recognition and abnormality detection with the switching hidden semi-markov model," Proc. IEEE International Conference on Computer Vision and Pattern ecognition, San Diego, USA, pp.838-845,2005.
[68]R. K. Begg, M. Palaniswami, B. Owen, "Support Vector Machines for Automated Gait classification," IEEE Transactions on Biomedical Engineering, vol.52, no.5, pp.828-838, 2005.
[69]J. Lu and E. Zhang, "Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion," Pattern Recognition Letters, vol.28, no.16, pp.2401-2411, 2007.
[70]H. Lee, S. Hong, E. Kim, "Neural network ensemble with probabilistic fusion and its application to gait recognition," Neurocomputing, vol.72, no.7, pp.1557-1564,2009.
[71]M. S. Nixon, J. N. Carter, D. Cunado, P. S. Huang, S. V. Stevenage, "Automatic Gait Recognition," Biometrics:Personal Identification in Networked Society, pp.231-249,2002.
[72]J. H. Yoo, M. S. Nixon, C. J. Harris, "Model-Driven Statistical Analysis of Human Gait Motion," Proc. International Conference on Image Processing, pp.285-288,2002.
[73]D. K. Wagg, M. S. Nixon, "On Automated Model-Based Extraction and Analysis of Gait," Proc.6th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 11-16,2004.
[74]R. Zhang, C. Vogler, D. Metaxas, "Human Gait Recognition," Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.18-25, Jun.2004.
[75]杨军,吴晓娟,彭彰,陈文刚,“基于多区域分割的步态表示与识别算法研究,”计算机学报,vol.26, no.10, pp.1876-1881,2006.
[76]Liang Wang, Huazhong Ning, Tieniu Tan, Weiming Hu, "Fusion of Static and Dynamic Body Biometrics for Gait Recognition," Proc. Ninth IEEE International Conference on Computer Vision, vol.14, no.2, pp.149-158,2004.
[77]Liang Wang, Huazhong Ning, Tieniu Tan, Weiming Hu, "Fusion of Static and Dynamic Body Biometrics for Gait Recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol.14, no.2, pp.149-158, Feb.2004.
[78]Shiloh L. Dockstader and A. Murat Tekalp, "A Kinematic Model for Human Motion and Gait Analysis," Proc. Workshop on Statistical Methods in Video Processing (ECCV), Copenhagen, Denmark, pp.49-54, Jun.2002.
[79]Shiloh L. Dockstader, Michel J. Berg, A. Murat Tekalp, "Stochastic kinematic modeling and feature extraction for gait analysis," IEEE Transactions on Image Processing, vol.12, no.8, pp. 962-976,2003.
[80]R. Urtasun, P. Fua, "3D Tracking for Gait Characterization and Recognition," Proc. Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp.17-22,2004.
[81]R. C. Gonzalez, R. E. Woods, "Digital image processing," Second Edition, Prentice Hall, 2002.
[82]R. Zhang, A. Vashist, I. Muchnik, C. Kulikowski, D. Metaxas, "A New Combinatorial Approach to Supervised Learning:Application to Gait Recognition," Proc. International workshop on Analysis and modeling of faces and gestures, vol.3723, pp.55-69,2005.
[83]S. D. Mowbray and M. S. Nixon, "Automatic gait recognition via Fourier descriptors of deformable objects," Proc. International Conference on Audio-and Video-Based Biometric Person Authentication, Guildford, UK, vol.2688, pp.566-573,2003.
[84]S. D. Mowbray and M. S. Nixon, "Extraction and recognition of periodically deforming objects by continuous, spatio-temporal shape description," Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington DC, USA, pp.895-901, 2004.
[85]Shiqi Yu, Liang Wang, Weiming Hu, Tieniu Tan, "Gait analysis for human identification in frequency domain," Proc.3rd International Conference on Image and Graphics, Hong Kong, China, pp.282-285, Dec.2004.
[86]A. F. Bobick and J. W. Davis, "The recognition of human movement using temporal templates," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, no.3, pp. 257-267,2001.
[87]J. Han and B. Bhanu, "Statistical feature lusion for gait-based human recognition," Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington DC, USA, vol.2, pp.842-847, Jun.2004.
[88]J. Han and B. Bhanu, "Individual recognition using Gait Energy Image," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, no.2, pp.316-322,2006.
[89]X. Zou and B. Bhanu, "Human activity classification based on gait energy image and coevolutionary genetic programming," Proc.18th International Conference on Pattern Recognition, Hong Kong, China, vol.3 pp.556-559, Aug.2006.
[90]马勤勇,王申康,聂栋栋,邱剑锋,“基于瞬时步态能量图的远距离身份识别,”电子学报,vol.35,no.11,pp.2078-2082,2007.
[91]马勤勇,聂栋栋,王申康,“基于能量图分解与运动偏移特性的步态识别,”光电子·激光,vol.20,no.4,pp.545-549,2009.
[92]C. Chen, J. Liang, H. Zhao, H. Hu, J. Tian, "Frame difference energy image for gait recognition with incomplete silhouettes," Pattern Recognition Letters, vol.30, no.11, pp. 977-984,2009.
[93]C Liu, H Wechsler, "Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition," IEEE Transactions on Image Processing, vol.11, no.4, pp.467-476,2002.
[94]杨晓超,周越,张一,署光,王力,“基于增强步态能量图和Gabor特征的辨别共同向量身份识别,”上海交通大学学报,vol.42, no.12, pp.1988-1992,2008.
[95]X. Yang, Y. Zhou, T. Zhang, G. Shu, J. Yang, "Gait recognition based on dynamic region analysis,:Signal Processing, vol.88, no.9, pp.2350-2356,2008.
[96]D. Tao, X. Li, X. Wu, S. J. Maybank, "General Tensor Discriminant Analysis and Gabor Features for Gait Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, no.10, pp.1700-1715,2007.
[97]X Yang, Y Zhou, T Zhang, E Zheng, J Yang, "Gabor phase based gait recognition," Electronics Letters, vol.44, no.10, pp.620-621,2008.
[98]杨晓超,周越,署光,张田昊,“基于Gabor相位谱和流型学习的步态识别方法,”电子学报,vol.37, no.4, pp.753-757,2009.
[99]J. Liu, N. Zheng, "Gait history image:A novel temporal template for gait recognition," Proc. IEEE International Conference on Multimedia and Expo, Beijing, China, pp.663-666, Jul. 2007.
[100]H. Lee, S. Hong, I. F. Nizami, E. Kim, "A noise robust gait representation:Motion energy image," International Journal of Control, Automation and Systems, vol.7, no.4, pp.638-643, 2009.
[101]N. V. Boulgouris, and Z. X. Chi, "Gait recognition using radon transform and linear discriminant analysis," IEEE Transcation on Image Processing, vol.16, no.3, pp.731-740, 2007.
[102]王科俊,贲晛烨,刘丽丽,陈薇,“基于能量的信息融合步态识别,”华中科技大学学报(自然科学版),vol.37, no.5, pp.14-17, May.2009.
[103]E. Zhang, H. Ma, J. Lu, Y. Chen, "Gait recognition using dynamic gait energy and PCA+LPP method," Proc. International Conference on Machine Learning and Cybernetics, Baoding, China, vol.1, pp.50-53,2009.
[104]R. Collins, R. Gross, J. Shi, "Silhouette-based human identification from body shape and gait," Proc. IEEE International Conference on Automatic Face and Gesture Recognition, pp. 366-371,2002.
[105]A. Kale, N. Cuntoor, B. Yegnanarayana, A.N. Rajagopalan, R. Chellappa, "Gait Analysis for Human Identification," Proc. Third International Conference on Audio- and Video-Based Biometric Person Authentication, pp.706-714,2003.
[106]J. B. Hayfron-Acquah, M. S. Nixon, J. N. Carter, "Recognising Human and Animal Movement by Symmetry," Proc. International Conference on Image Processing, pp.290-293,2001.
[107]J. B. Hayfron-Acquah, M. S. Nixon, J. N. Carter, "Automatic gait recognition by synunetry analysis," Pattern Recognition Letters, vol.24, no.13, pp.2175-2183,2003.
[108]赵国英,向世明,李华,“基于反射对称的步态序列识别,”计算机辅助设计与图形学报,vol.17, no.10, pp.2239-2244,2005.
[109]A. K. Jain, R. M. Bolle, S. Pankanti, "Biometrics:Personal Identification in a Networked Society," Norwell, MA:Kluwer,1999.
[110]A. A. Ross, K. Nandakumar, A. K. Jain, "Handbook of Multibiometrics," Springer, Berlin, 2006.
[111]G. Shakhnarovich, L. Lee, T. Darrell, "Integrated face and gait recognition from multiple views," Proc. Computer Vision and Pattern Recognition, vol.1, pp.439-446,2001.
[112]G. Shakhnarovich and T. Darrell, "On probabilistic combination of face and gait cues for identification," Proc. International Conference on Automatic Face and Gesture Recognition, vol.5, pp.169-174,2002.
[113]A. Kale, A. Roy-chowdhury, R. Chellappa, "Fusion of gait and face for human identification," Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol.5, pp. 901-904,2004.
[114]X. Zhou, B. Bhanu, J Han, "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, pp.165-181,2005.
[115]X. Zhou and B. Bhanu, "Feature fusion ef face and gait for human recognition at a distance in video" Proc.18th International Conference on Pattern Recognition, pp.529-532,2006.
[116]X. Zhou and B. Bhanu, "Integrating face and gait for human recognition at a distance in video," IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, vol.37, no.5, pp.1119-1137,2007.
[117]X. Zhou and B. Bhanu, "Feature fusion of side face and gait for video-based human identification," Pattern Recognition, vol.41, no.3, pp.778-795,2008.
[118]Z. Liu, S. Sarkar, "Out door recognition at a distance by fusing gait and face," Image and Vision Computing, vol.25, pp.817-832,2007.
[119]Q. Li, Z. Lu, D. Zhang, "Integration of Gait and Side Face for Human Recognition in Video," Proc. Second International Symposium on Electronic Commerce and Security, pp.65-69, 2009.
[120]T. Savic and N. Pavesic, "Personal recognition based on an image of the palmar surface of the hand," Pattern Recognition, vol.40, no.11, pp.3152-3163,2007.
[121]P. J. Philips, H. Moon, S. A. Rizvi, P. J. Rauss, "The FERET evaluation methodology for face-recognition algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, no.10, pp.1090-1104, Oct.2000.
[122]Y. Kuno, T. Watanabe, Y. Shimosakoda, S. Nakagawa, "Automated detection of human for visual surveillance system," Proc. International Conference on Pattern Recognition, Vienna, Austria, pp.865-869,1996.
[123]T. W. Ridler, S. Calvard, "Picture thresholding using an iterative selection method," IEEE Transactions on Systems, Man, and Cybernetics, vol.8, pp.630-632,1978.
[124]L. Lee, G. Dalley, K. Tieu, "Learning Pedestrian Models for Silhouette Refinement," Proc. Ninth IEEE International Conference on Computer Vision, IEEE CS, pp.663-670, Oct.2003.
[125]Z. Liu, L. Malave, S. Sarkar, "Studies on Silhouette Quality and Gait Recognition," Proc. IEEE Conference on Computer Vision and PaRem Recognition, pp.704-711,2004.
[126]Z. Liu, S. Sarkar, "Effect of Silhouette Quality on Hard Problems in Gait Recognition," IEEE Transactions on Systems, Man, and Cybernetics, vol.35, no.2, pp.170-183,2005.
[127]J. Bernoulli, "The Art of Conjecturing," Johns Hopkins University Press, Baltimore,2006. English translation with notes by E. D. Sylla of the book Ars Conjectandi first published in 1713.
[128]M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, vol.3, no.1, pp.71-86,1991.
[129]M Kirby and L. Sirovich, "Application of the karhunen-loeve procedure for the characterization of human faces," IEEE Transactions on Pattern Analysis and Machinelntelligence, vol.12, no.1, pp.103-108,1990.
[130]R. Cutler and L. Davis, "Robust real-time periodic motion detection, analysis, and applications," IEEE Transactions on Pattern Analysis andMachine Intelligence, vol.22, no.8, pp.781-796,2000.
[131]C. BenAbdelkader, R. G. Cutler, L. S. Davis, "Stride and cadence as a biometric in automatic person identification and verification," Proc. Fifth IEEE International Conference on Automatic Face Gesture Recognition, Washington DC, USA, pp.372-377,2002.
[132]郭大钧,“大学数学手册,”第一版,山东科学技术出版社,1985.
[133]J. T. Kent, "New directions in shape analysis," Art of Statistical Science:A Tribute to G. S. Watson. NewYork, USA:Wiley, pp.115-127,1992.
[134]C. Zahn and R. Roskies, "Fourier Descriptors for Plane Closed Curves," IEEE Transactions on Computers, vol.21, no.3, pp.269-281, Mar.1972.
[135]L. M. J. Florack, B. M. T. Haar Romeny, J. J. Koendednk, M. A. Viergever, "General Intensity Transformations and Second Order Invadants," Proc.7th Scandinavian Conference on Image Analysis, Aalborg, Denmark, pp.338-445,1991.
[136]W. T. Freeman and E. H. Adelson, "The Design and Use of Steerable Filters,", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, no.9, pp.891-906,1991.
[137]K. Mikolajczyk and C. Schmid, "A Performance Evaluation of Local Descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.10, pp.1615-1630, 2005.
[138]D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol.60, no.2, pp.91-110,2004.
[139]S. Belongie, J. Malik, J. Puzicha, "Matching shapes," Proc. Eighth IEEE International Conference on Computer Vision, vol.1, pp.454-461, Jul.2001.
[140]S. Belongie, J. Malik, J. Puzicha, "Shape Matching and Object Recognition Using Shape Contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, no.4, pp. 509-522,2002.
[141]C. H. Papadimitriou, K. Steiglitz, "Combinatorial optimization:algorithms and complexity," Prentice-Hall, Englewood Cliffs,1982.
[142]陈实,马天骏,黄万红,高有行,“基于形状上下文描述子的步态识别,”模式识别与人工智能,vol.20, no.6, pp.794-799,2007.
[143]J. E. Boyd, "Synchronization of Oscillations for Machine Perception of Gaits," Computer Vision and Image Understanding, vol.96, no.1, pp.35-59,2004.
[144]A. Bazin, M. S. Nixon, "Gait verification using probabilistic methods," Proc. IEEE Workshop on Applications of Computer Vision, pp.60-65,2005.
[145]S. Chen, Y. Gao, "Stride history image:a new feature representation for pedestrian identification," Proc. IEEE Workshop on Signal Processing Systems, pp.543-547,2007.
[146]S. Chen, W. Huang, T. Ma, L. Dong, "Towards feature fusion for human identification by gait," Proc. Fourth International Conference on Image and Graphics, pp.678-682, Aug.2007.
[147]陈实,马天骏,黄万红,高有行,“用于步态识别的多层窗口图像矩,”电子与信息学报,vol.31, no.1, pp.116-119,2009.
[148]H. Wang, Y. Leng, Z. Wang, X. Wu, "Application of Image Correction and Bit-plane Fusion in Generalized PCA Based Face Recognition," Pattern Recognition Letters, vol.28, no.16, pp. 2352-2358, Dec.2007.
[149]J. Yang, J. Yang, "Generalized K-L transform based combined feature extraction," Pattern Recognition, vol.35, no.1, pp.295-297,2002.
[150]K. I. Kim, K. Jung, S. H. Park, H. J. Kim, "Support Vector Machines for Texture Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, no.11, pp.1542-1550,2002.
[151]H. Tamura, S. Moil, Y. Yamawaki, "Texture Feature Corresponding to Visual Perception," IEEE Transactions on Systems, Man, and Cybernetics, vol.8, pp.460-473,1978.
[152]C. Bovik, M. Clark, W. S. Geisler, "Mulfichannel Texture Analysis Using Localized Spatial Filers," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, no.1, pp. 55-73,1990.
[153]R. M. Haralick, K. Shanmugam, I. H. Dinstein, "Textural features for image classification," IEEE Transactions on Systems, Man, and Cybernetics,vol.3, no.6, pp.610-621.1973.
[154]C. E. Shannon and W. Weaver, "The Mathematical theory of Communication," University of Illinois Press, Urbana, IL,1949.
[155]孙即祥,“现代模式识别,”高等教育出版社,第二版,2008.
[156]A. Jain, K. Nandakumar, A. Ross, "Score normalization in multimodal biometric systems," Pattern recognition, vol.38, no.12, pp.2270-2285, Dec.2005.
[157]R. Snelick, U. Uludag, A. Mink, M. Indovina, A. Jain, "Large-Scale Evaluation of multimodal biometric authentication using state-of-the-art systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, no.3, pp.450-455, Mar.2005.
[158]A. Ross, A. K. Jain, "Information fusion in biometrics," Pattern Recognition Letters, vol.24, no,13, pp.2115-2125,2003.
[159]J. Kittler, M. Hatef, R. P. W. Duin, J. Matas, "On combining classifiers," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, no.3, pp.226-239,1998.