人脸特征点定位研究及应用
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摘要
随着科技的进步和人们安全意识的提高,对快速有效的身份鉴别的实际需求日益迫切。人脸相比其他人体生物特征具有直接、友好、方便的特点,因此自动人脸识别成为身份鉴别的研究热点,有着广泛的应用前景。一个自动人脸识别系统一般包括:人脸检测、关键点特征定位、识别等步骤。影响自动人脸识别系统真正实用化的主要因素有:姿态问题、光照问题、表情问题等。特征点精确定位是解决这些问题的关键。
本论文对于人脸识别中的人脸特征点定位、人脸特征点跟踪以及相关应用进行了深入研究,并在此基础上对相关算法进行了创新性的改进。论文的主要工作如下:
1)系统的综述了人脸特征点定位的发展历史和研究现状。详细总结了基于灰度信息、基于先验规则、基于几何形状、基于统计、基于小波和小波包等人脸特征点定位方法,并分析和比较各种方法的优缺点。
2)对人脸特殊器官的定位进行了研究。首先介绍了霍夫变换法、变形模板法、边缘特征分析法、对称变换法和基于彩色图像色度经验公式法等定位眼睛的方法。然后研究了通过聚类定位嘴巴的方法。
3)介绍了基于模型的两种经典的特征点定位算法:主动形状模型(ASM)和主动外观模型(AAM)。并在本文的实验中构造了主动形状模型和主动表观模型,通过理论地分析和实验对ASM和AAM算法进行比较,提出改进方案。
4)具体地对主动形状模型(ASM)算法进行了创新性地改进,提出了一种在彩色图像中结合肤色概率信息的改进ASM算法,并进一步提出了基于人脸特征点Gabor小波特征分类的特征点搜索方法,对改进ASM的结果进行精确校正,达到鲁棒精确地定位特征点的目的。
5)对光流分析算法进行了研究,并将该算法应用于人脸图像序列的特征点的跟踪,并通过实验证明了其有效性。
6)本文对基于特征点跟踪的唇形识别进行了探索性研究,提出了一种特征点位置和运动特征提取的方法,并介绍了基于子空间学习的几种经典的特征降维方法:主元分析法(PCA)、线性判别分析(LDA)、基于流形学习的降维(LPP)、辨别共同向量(DCV),最后通过实验对这几种降维方法的识别效果进行比较,从而证明本文提出的基于特征点跟踪的唇形识别算法的可行性。
With the development of society and technology, the demand for effective automatic verification is increasingly urgent. Comparing with other human biological characteristics, face characteristics have direct, friendly and convenient characteristics, therefore automatic human face recognition becomes the research focus of identity verification and has extensive application foreground. General speaking, an automatic face recognition system contains: face detection, feature points extraction and recognition. There are some factors which affect instantiation of face recognition. They lie on pose, light and expression of face. Precise facial feature points extraction is the key to solve these problems.
In this dissertation, algorithms of facial feature points extraction, tracking and relevant application were profoundly researched. Based on the classical algorithms, novel and creative improvement was proposed. The main discussion of the dissertation is listed as follows:
1) The history and status of research on facial feature points extraction are systematically summarized. Detail kinds of feature points extraction approaches, including grey-level-based algorithm, knowledge-based algorithm, Geometry-base algorithm, statistic-based algorithm, wavelet-base algorithm, and then analysis the different algorithm.
2) The algorithms of locating rough position of special facial organs are researched. Introduce eyes locating methods including Hough Transform algorithm, Deformable Template algorithm, edge feature analysis algorithm and knowledge-based algorithm. And then introduce Clustering-based mouth locating method.
3) Introduce two classical algorithms of facial feature points extraction: Active Shape Model (ASM) and Active Appearance Model (AAM). In this dissertation, we construct ASM and AAM for experiments. With comparing the two algorithms not only in theory but also in experiments, we conclude the weakness and propose the improving solution.
4) A method of facial feature points extraction based on improved Active Shape Model and Gabor wavelet features is presented. And the experiments prove that facial feature points can be located robustly and precisely using the method proposed.
5) Research the optical flow algorithm and apply it to feature points tracking in serial images of face.
6) Explore the method of lip-movement recognition based on track of mouth feature points. An algorithm that extracts the features of lip points' track is proposed. Then introduce subspace learning based dimension reduction algorithm: PCA, LDA, LPP, DCV. And the experiments prove the feasibility of this algorithm proposed.
本论文对于人脸识别中的人脸特征点定位、人脸特征点跟踪以及相关应用进行了深入研究,并在此基础上对相关算法进行了创新性的改进。论文的主要工作如下:
1)系统的综述了人脸特征点定位的发展历史和研究现状。详细总结了基于灰度信息、基于先验规则、基于几何形状、基于统计、基于小波和小波包等人脸特征点定位方法,并分析和比较各种方法的优缺点。
2)对人脸特殊器官的定位进行了研究。首先介绍了霍夫变换法、变形模板法、边缘特征分析法、对称变换法和基于彩色图像色度经验公式法等定位眼睛的方法。然后研究了通过聚类定位嘴巴的方法。
3)介绍了基于模型的两种经典的特征点定位算法:主动形状模型(ASM)和主动外观模型(AAM)。并在本文的实验中构造了主动形状模型和主动表观模型,通过理论地分析和实验对ASM和AAM算法进行比较,提出改进方案。
4)具体地对主动形状模型(ASM)算法进行了创新性地改进,提出了一种在彩色图像中结合肤色概率信息的改进ASM算法,并进一步提出了基于人脸特征点Gabor小波特征分类的特征点搜索方法,对改进ASM的结果进行精确校正,达到鲁棒精确地定位特征点的目的。
5)对光流分析算法进行了研究,并将该算法应用于人脸图像序列的特征点的跟踪,并通过实验证明了其有效性。
6)本文对基于特征点跟踪的唇形识别进行了探索性研究,提出了一种特征点位置和运动特征提取的方法,并介绍了基于子空间学习的几种经典的特征降维方法:主元分析法(PCA)、线性判别分析(LDA)、基于流形学习的降维(LPP)、辨别共同向量(DCV),最后通过实验对这几种降维方法的识别效果进行比较,从而证明本文提出的基于特征点跟踪的唇形识别算法的可行性。
With the development of society and technology, the demand for effective automatic verification is increasingly urgent. Comparing with other human biological characteristics, face characteristics have direct, friendly and convenient characteristics, therefore automatic human face recognition becomes the research focus of identity verification and has extensive application foreground. General speaking, an automatic face recognition system contains: face detection, feature points extraction and recognition. There are some factors which affect instantiation of face recognition. They lie on pose, light and expression of face. Precise facial feature points extraction is the key to solve these problems.
In this dissertation, algorithms of facial feature points extraction, tracking and relevant application were profoundly researched. Based on the classical algorithms, novel and creative improvement was proposed. The main discussion of the dissertation is listed as follows:
1) The history and status of research on facial feature points extraction are systematically summarized. Detail kinds of feature points extraction approaches, including grey-level-based algorithm, knowledge-based algorithm, Geometry-base algorithm, statistic-based algorithm, wavelet-base algorithm, and then analysis the different algorithm.
2) The algorithms of locating rough position of special facial organs are researched. Introduce eyes locating methods including Hough Transform algorithm, Deformable Template algorithm, edge feature analysis algorithm and knowledge-based algorithm. And then introduce Clustering-based mouth locating method.
3) Introduce two classical algorithms of facial feature points extraction: Active Shape Model (ASM) and Active Appearance Model (AAM). In this dissertation, we construct ASM and AAM for experiments. With comparing the two algorithms not only in theory but also in experiments, we conclude the weakness and propose the improving solution.
4) A method of facial feature points extraction based on improved Active Shape Model and Gabor wavelet features is presented. And the experiments prove that facial feature points can be located robustly and precisely using the method proposed.
5) Research the optical flow algorithm and apply it to feature points tracking in serial images of face.
6) Explore the method of lip-movement recognition based on track of mouth feature points. An algorithm that extracts the features of lip points' track is proposed. Then introduce subspace learning based dimension reduction algorithm: PCA, LDA, LPP, DCV. And the experiments prove the feasibility of this algorithm proposed.
引文
[1]X.Xie, R.Sudhakar, and H.Zhuang, Corner Detection by a cost minimization approach,Patern Recognition, 26(8), 1993, 1235-1243.
[2]Jiann-shu Lee, Yung N. Sun, Chin-Hsing Chen, Ching-Tsomg Tsai, Wavelet Based Corner Detection Patern Recognition Vol.26,No.6, pp:853-865,1993
[3]Yuille, A.L., Hallinan, P.W.&Cohen, D.S. (1992). Feature extraction from faces using deformable templates.International Journal of Computer Vision,8,99-111
[4]A.R.Mirhosseini, H.Yan, and K.M.Lam, An adaptive defomable model for mouth boundary modeling, Opt.Eng.1998.3
[5]Craw,l. ,Tock,D .&Bennet,A ."Finding face features",In Proceedings 2nd European Conference on Computer Vision,pp 92-96,Italy,1992
[6]Huang, C.L.&Chen C.W. "Human facial feature-extraction for face interpretation and recognition".Pattern Recognition,25 (12),pp l435-1444,1992
[7]Chow, G.&Li, X.B. (1993). Towards a system for automatic facial feature detection. Pattern Recognition,26 (12),pp 1739-1755.19 93
[8]Xie,X ,Sudhakar,R .&Zhuang,H . Corner Detection By a Cost Minimization Approach Patern Recognition,26 (8),1993
[9]Xie, X., Sudhakar, R.&Zhuang, H. (1994). On improving eye feature-extraction using deformable templates. Pattern Recognition,27 (6),791-799
[10]Stephen J. Mckenna, Shaogang Gong, Tracking Facial Feature Points with Gabor Wavelets and shape Models. Pro.1st Con. On Audio-and Video-based Bio-metric Person Authentication, Le cture Notes in Computer Science, Springer Verlag,1997
[11]M.Kass,A .W itkin and D.Terzopdulos,, "Active Contour Models", 1st International Conference on Computer Vision, London, June, 1987,pp .25 9-268
[12]Lee W .S., KalraP .,and Magnenat-Thalmann N,"Model Based Face Reconstruction or Animation",In Proc.Multimedia Modeling( MMM'97), Singapore, pp .32 3-338.19 97.
[13]T.F. Cootes, C.J. Taylor, D.H. Cooper, and J. Haslam, Training Models of Shape from Sets of Examples, in Proc.British Machine Vision Conference.Sp ringer-Verlag,1992,pp .9-18.
[14]T.F.Cootes and C.J.Taylor,Active shape models "smart snakes", Proceeding of the British Vision Conference,Leeds, U K, 22-24 September 1992, London, 1992, Springer- Verlag,pp .26 6-275.
[15] T.F.Cootes, C.J.Taylor, and A.Lanitis, Multi-Resolution Search with Active Shape Model s. Proc .ICPR 1994.
[16] T.F.Cootes, C.J. Taylor,D.H.Cooper, and J.Graham, "Active Shape Models-Their Training and Application", Computer Vision and Image Understanding,6 1(I),19 95,pp .38 -5 9.
[17] T.F.Cootes,G .J.Edwards, and C .J.Taylor, "Active Appearance Models, "Proceeding of the 5th European Conference on Computer Vision, vol.2, 19 98,pp .48 4-498
[18]CaiJ. Goshtasby A.. Detecting face in color images.Imageand Vision Computing[J],1999,18(1): 63-75
[19] Canny J.A Computational Approach to Edge Detection [J]. IEEE Trans Pattern Anal&Mach
[20] REIN-LIEN HSU, ABDEL-MOTTALEB M, JAIN A K. Face detection in color images [J]. IEEE Trans on Pattern Analysis and Mechine Intelligence, 2002, 24 (5): 696-706.
[21]. Xinwen Hou, Stan Z.Li,Hongjiang Zhang,Qiansheng Cheng. Direct appearance models. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001: 828-833.
[22]. Li, S.Z, Yan Shuicheng, Zhang Hongjiang, Cheng Qiansheng, Multi-view face alignment using direct appearance models, 5th IEEE International Conference on Automatic Face and Gesture Recognition. 2002: 309-314.
[23]. Shuicheng Yan, Ce Liu, Stan Z.Li, Hongjiang Zhang, Heung-Yeung Shum, Face alignment using texture-constrained active shape models, Image and Vision Computing, 21(1) ,2003:69-75.
[24]. Yi Zhou, Lie Gu, Hongjiang Zhang, Bayesian tangent shape model: estimating shape and pose parameters via Bayesian inference, IEEE Conference on Computer Vision and Pattern Recognition, 2003:16-22.
[25]. Feng Jiao, Stan Li, Heung-Yeung Shum, Dale Schuurmans, Face alignment using statistical models and wavelet feature, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2003: 321-327.
[26]. Fei Zuo, Peter H.N.de With, Fast facial feature extraction using a deformable shape model with Haar-wavelet based local texture attributes, Proceedings of IEEE Conference on Image Processing. 2004: 1425-1428.
[27]. Laurenz Wiskott, Jean-Marc Fellous, Norbert Kruger, Christoph von der Malsburg, Face recognition by elastic bunch graph matching, IEEE Transactions on PAMI, 19(7), 1997: 775-779.
[28]. J.C.Gower, Generalized Procrustes analysis, Psychometrika 40, 1975: 33-51.
[29] T. F. Cootes, C. J. Taylor, D. H. Cooper and J. Graham, Active Shape Models - Their Training and Application., Computer Vision and Image Understanding, Vol. 61, No.1, January, pp. 38-59, 1995.
[30] Laurenz Wiskott, Jean-Marc Fellous,Norbert Kr uger, and Christoph von der Malsburg, Face Recognition by Elastic Bunch Graph Matching, In Intelligent Biometric Techniques in Fingerprint and Face Recognition,eds. L.C. Jain et al., publ. CRC Press, ISBN 0-8493-2055-0, Chapter 11, pp. 355-396, (1999).
[31] Viola P., Jones M. J., Robust Real-Time Face Detection[J].International Journal of Computer Vision 57(2), 2004 : 137-154
[32] REIN-LIEN HSU, ABDEL-MOTTALEB M, JAIN A K. Face detection in color images [J]. IEEE Trans on Pattern Analysis and Mechine Intelligence, 2002, 24 (5): 696-706.
[33] P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7) (1997) : 711-720.
[34] 李冯,姚莉秀,杨杰,戈新良. 复杂背景下的彩色图像人脸检测[J]. 上海交通大学学报 Vol.40 NO.5 May 2006: 778-782 LI Feng, YA0 Li-xiu, YANG Jie, GE Xin-liang. The Face Detection in Color Images with Complex Environments[J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY, Vol.40, NO.5, May, 2006 : 778-782 (in Chinese)
[35] Charles Poynton. A technical introduction to digital video[M]. John Wiley& Sons Publisher,1996.
[36] Chunhua Du, Jie Yang, Qiang Wu, Tianhao Zhang, Huahua Wang, Lu Chen, Zheng Wu: Extended Fitting Methods of Active Shape Model for the Location of Facial Feature Points[C]. Springer Berlin/Heidelberg Press, Lecture Notes in Computer Science. ICVGIP 2006: 610-618
[37] Lucas B, Kanade T. An iterative image registration technique with an application to stereo vision. In:Hayes PJ, ed. Proc. of the 7th Int'1 Joint Conf. on Artificial Intelligence.Vancouver: Morgan Kaufmann Publishers, 1981. 674～679.
[38] Jean-Yves Bouguet. Pyramidal Implementation of the Lucas Kanade Feature Tracker. The paper is included into OpenCV distribution
[39] 郑忠龙,人脸识别中新算法的实现与改进,上海交通大学博士论文,2005
[40] 戈新良,人脸姿势估计与各姿势下的人脸对齐,上海交通大学博士论文,2006
[41] 王华华,基于统计学习的人脸识别方法研究,上海交通大学硕士论文, 2007
[42] 石林英,基于改进的 ASM 方法的人脸特征点检测,中山大学硕士学位论文,2005
[43] 徐艳,陈孝威,人脸检测中的眼睛定位算法研究,计算机和信息技术,2007
[44] Pentland A, Moghaddam B, T Starner, View-based and modular eigenspaces for face recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994: 84-91.
[45] Okada K, Malsburg C V D, Pose-invariant face recognition with parametric linear subspaces, Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, 2002: 71-76.
[46] 王守觉,徐键,王宪保等,基于仿生模式识别的多镜头人脸身份确认系统研究,电子学报,31(1), 2003:1-3。
[47] Lanitis A, Taylor C J, Cootes T F, Automatic interpretation and coding of face images using flexible template, IEEE Transaction on Pattern Analysis and Machine Intelligence, 19(7), 1997: 743-756.
[48] Cootes T F, Edwards G J, Taylor C J, Active appearance model, IEEE Transaction on Pattern Analysis and Machine Intelligence, 23(6), 2001: 681-685.
[49] Li S Z, Cheng Yan Shui, Hongjiang Zhang, et al, Multi-view face alignment using direct appearance models, Proceedings of 5th IEEE International Conference on Automatic Face and Gesture Recognition, 2002:324-329.
[50] Yongmin Li, Shaogang Gong, Heather Liddell, Recognising trajectories of facial identities using kernel discriminant analysis, Image and Vision Computing, 21(13-14), 2003: 1077-1086.
[51] Gordon G. Face recognition based on depth maps and surface curvature, In Geometric methods in Computer Vision, The Proceedings of SPIE, 1570, 1991: 234-247.
[52] Ralph GrossIain, Iain Matthews, Simon Baker, Eigen light-fields and face recognition across pose, Proceedings 5th IEEE International Conference on Automatic Face and Gesture Recognition, 5, 2002: 1-7.
[53] Dibyendu Nandy, Jezekiel Ben-Arie, Shape from recognition: a novel approach for 3-D face shape recovery, IEEE Transaction on Pattern Analysis and Machine Intelligence, 10(2), 2001: 206-217.
[54] Sami Romdhani, Volker Blanz, Thomas Vetter, Face identification by fitting a 3D morphable model using linear shape and texture error functions, in 7th European Conference on Computer Vision, 2002: 3-19.
[55] Sherrah J, Gong S, Ong E J, Face distributions in similarity space under varying head pose, Image and Vision Computing, 19(12), 2001: 807-819.
[56] Tenenbaum J B, Silva V D, Langford J C, A global geometric framework for nonlinear dimensionality reduction, Science, 29, 2000: 2319-2323.
[57] V D Silva, J B Tenenbaum, Local versus global methods for nonlinear dimensionality reduction, Advances in Neural Information Processing System, 15, 2003: 705-712.
[58] Roweis S T, Saul L K, Nonlinear dimensionality reduction by locally linear embedding, Science, 29, 2000: 2323-2326.
[59] Lawrence K.Saul, Sam T. Roweis, Think globally, fit locally: unsupervised learning of low dimensional manifolds, Journal of Machine Learning Research, 4(2), 2004: 119-155.
[60] M Belkin, P Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, Advances in Neural Information Processing System 14 (NIPS 2001) MIT Press, Cambridge 2002: 585-591.
[61] Yang M H, Face recognition using extended isomap, In International Conference on ImageProcessing Ⅱ,2002: 117-120.
[62] Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, Hongjiang Zhang, Face recognition using Laplacianfaces, IEEE Transaction on Pattern Analysis and Machine Intelligence, 27(3), 2005: 328-340.
[63] 颜水成,人脸定位与识别若干问题的研究,北京大学博士研究生学位论文。
[64] C.Garcia, G.Zikos, G.Tziritas, Wavelet packet analysis for face recognition, Image and Vision Computing, 18(4), 2000: 289-297.
[65] TS Lee, Image representation using 2D Gabor wavelets, IEEE Transaction on PAMI, 18(10), 1996: 959-971.
[66] Chengjun Liu, Gabor-based kernel PCA with fractional power polynomial models for face recognition, IEEE Transaction on PAMI, 26(5), 2004: 572-581.
[67] Yang, Q., Ding, X., Chen, Z., Discriminant local feature analysis of facial images, ICIP, 2, 2003: 863-866.
[68] Laurenz Wiskott, Jean-Marc Fellous, Norbert Kruger, Christoph von der Malsburg, Face recognition by elastic bunch graph matching, IEEE Transactions on PAMI, 19(7), 1997: 775-779.
[69] T.F.Cootes, C.J.Taylor, D.H.Cooper, J.Graham, Active shape models-their training and application, Computer Vision and Image Understanding, 61(1),1995: 38-59.
[70] Timothy F.Cootes, Gareth J.Edwards, and Christopher J.Taylor, Active appearance model, IEEE Transactions on PAMI, 23(6) ,2001: 681-685.
[71] Xinwen Hou, Stan Z.Li,Hongjiang Zhang,Qiansheng Cheng. Direct appearance models. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001: 828-833.
[72] Li, S.Z, Yan Shuicheng, Zhang Hongjiang, Cheng Qiansheng, Multi-view face alignment using direct appearance models, 5th IEEE International Conference on Automatic Face and Gesture Recognition. 2002: 309-314.
[73] Shuicheng Yan, Ce Liu, Stan Z.Li, Hongjiang Zhang, Heung-Yeung Shum, Face alignment using texture-constrained active shape models, Image and Vision Computing, 21(1) ,2003:69-75.
[74] Yi Zhou, Lie Gu, Hongjiang Zhang, Bayesian tangent shape model: estimating shape and pose parameters via Bayesian inference, IEEE Conference on Computer Vision and Pattern Recognition, 2003:16-22.
[75] Feng Jiao, Stan Li, Heung-Yeung Shum, Dale Schuurmans, Face alignment using statistical models and wavelet feature, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2003: 321-327.
[76] Fei Zuo, Peter H.N.de With, Fast facial feature extraction using a deformable shape model with Haar-wavelet based local texture attributes, Proceedings of IEEE Conference on ImageProcessing. 2004: 1425-1428.
[77] J.C.Gower, Generalized Procrustes analysis, Psychometrika 40, 1975: 33-51.
[78] Tsuyoshi Kawaguchi, Mohamed Rizon, Iris detection using intensity and edge information, Pattern Recognition, 36(2),2003:549-562.
[2]Jiann-shu Lee, Yung N. Sun, Chin-Hsing Chen, Ching-Tsomg Tsai, Wavelet Based Corner Detection Patern Recognition Vol.26,No.6, pp:853-865,1993
[3]Yuille, A.L., Hallinan, P.W.&Cohen, D.S. (1992). Feature extraction from faces using deformable templates.International Journal of Computer Vision,8,99-111
[4]A.R.Mirhosseini, H.Yan, and K.M.Lam, An adaptive defomable model for mouth boundary modeling, Opt.Eng.1998.3
[5]Craw,l. ,Tock,D .&Bennet,A ."Finding face features",In Proceedings 2nd European Conference on Computer Vision,pp 92-96,Italy,1992
[6]Huang, C.L.&Chen C.W. "Human facial feature-extraction for face interpretation and recognition".Pattern Recognition,25 (12),pp l435-1444,1992
[7]Chow, G.&Li, X.B. (1993). Towards a system for automatic facial feature detection. Pattern Recognition,26 (12),pp 1739-1755.19 93
[8]Xie,X ,Sudhakar,R .&Zhuang,H . Corner Detection By a Cost Minimization Approach Patern Recognition,26 (8),1993
[9]Xie, X., Sudhakar, R.&Zhuang, H. (1994). On improving eye feature-extraction using deformable templates. Pattern Recognition,27 (6),791-799
[10]Stephen J. Mckenna, Shaogang Gong, Tracking Facial Feature Points with Gabor Wavelets and shape Models. Pro.1st Con. On Audio-and Video-based Bio-metric Person Authentication, Le cture Notes in Computer Science, Springer Verlag,1997
[11]M.Kass,A .W itkin and D.Terzopdulos,, "Active Contour Models", 1st International Conference on Computer Vision, London, June, 1987,pp .25 9-268
[12]Lee W .S., KalraP .,and Magnenat-Thalmann N,"Model Based Face Reconstruction or Animation",In Proc.Multimedia Modeling( MMM'97), Singapore, pp .32 3-338.19 97.
[13]T.F. Cootes, C.J. Taylor, D.H. Cooper, and J. Haslam, Training Models of Shape from Sets of Examples, in Proc.British Machine Vision Conference.Sp ringer-Verlag,1992,pp .9-18.
[14]T.F.Cootes and C.J.Taylor,Active shape models "smart snakes", Proceeding of the British Vision Conference,Leeds, U K, 22-24 September 1992, London, 1992, Springer- Verlag,pp .26 6-275.
[15] T.F.Cootes, C.J.Taylor, and A.Lanitis, Multi-Resolution Search with Active Shape Model s. Proc .ICPR 1994.
[16] T.F.Cootes, C.J. Taylor,D.H.Cooper, and J.Graham, "Active Shape Models-Their Training and Application", Computer Vision and Image Understanding,6 1(I),19 95,pp .38 -5 9.
[17] T.F.Cootes,G .J.Edwards, and C .J.Taylor, "Active Appearance Models, "Proceeding of the 5th European Conference on Computer Vision, vol.2, 19 98,pp .48 4-498
[18]CaiJ. Goshtasby A.. Detecting face in color images.Imageand Vision Computing[J],1999,18(1): 63-75
[19] Canny J.A Computational Approach to Edge Detection [J]. IEEE Trans Pattern Anal&Mach
[20] REIN-LIEN HSU, ABDEL-MOTTALEB M, JAIN A K. Face detection in color images [J]. IEEE Trans on Pattern Analysis and Mechine Intelligence, 2002, 24 (5): 696-706.
[21]. Xinwen Hou, Stan Z.Li,Hongjiang Zhang,Qiansheng Cheng. Direct appearance models. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001: 828-833.
[22]. Li, S.Z, Yan Shuicheng, Zhang Hongjiang, Cheng Qiansheng, Multi-view face alignment using direct appearance models, 5th IEEE International Conference on Automatic Face and Gesture Recognition. 2002: 309-314.
[23]. Shuicheng Yan, Ce Liu, Stan Z.Li, Hongjiang Zhang, Heung-Yeung Shum, Face alignment using texture-constrained active shape models, Image and Vision Computing, 21(1) ,2003:69-75.
[24]. Yi Zhou, Lie Gu, Hongjiang Zhang, Bayesian tangent shape model: estimating shape and pose parameters via Bayesian inference, IEEE Conference on Computer Vision and Pattern Recognition, 2003:16-22.
[25]. Feng Jiao, Stan Li, Heung-Yeung Shum, Dale Schuurmans, Face alignment using statistical models and wavelet feature, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2003: 321-327.
[26]. Fei Zuo, Peter H.N.de With, Fast facial feature extraction using a deformable shape model with Haar-wavelet based local texture attributes, Proceedings of IEEE Conference on Image Processing. 2004: 1425-1428.
[27]. Laurenz Wiskott, Jean-Marc Fellous, Norbert Kruger, Christoph von der Malsburg, Face recognition by elastic bunch graph matching, IEEE Transactions on PAMI, 19(7), 1997: 775-779.
[28]. J.C.Gower, Generalized Procrustes analysis, Psychometrika 40, 1975: 33-51.
[29] T. F. Cootes, C. J. Taylor, D. H. Cooper and J. Graham, Active Shape Models - Their Training and Application., Computer Vision and Image Understanding, Vol. 61, No.1, January, pp. 38-59, 1995.
[30] Laurenz Wiskott, Jean-Marc Fellous,Norbert Kr uger, and Christoph von der Malsburg, Face Recognition by Elastic Bunch Graph Matching, In Intelligent Biometric Techniques in Fingerprint and Face Recognition,eds. L.C. Jain et al., publ. CRC Press, ISBN 0-8493-2055-0, Chapter 11, pp. 355-396, (1999).
[31] Viola P., Jones M. J., Robust Real-Time Face Detection[J].International Journal of Computer Vision 57(2), 2004 : 137-154
[32] REIN-LIEN HSU, ABDEL-MOTTALEB M, JAIN A K. Face detection in color images [J]. IEEE Trans on Pattern Analysis and Mechine Intelligence, 2002, 24 (5): 696-706.
[33] P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7) (1997) : 711-720.
[34] 李冯,姚莉秀,杨杰,戈新良. 复杂背景下的彩色图像人脸检测[J]. 上海交通大学学报 Vol.40 NO.5 May 2006: 778-782 LI Feng, YA0 Li-xiu, YANG Jie, GE Xin-liang. The Face Detection in Color Images with Complex Environments[J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY, Vol.40, NO.5, May, 2006 : 778-782 (in Chinese)
[35] Charles Poynton. A technical introduction to digital video[M]. John Wiley& Sons Publisher,1996.
[36] Chunhua Du, Jie Yang, Qiang Wu, Tianhao Zhang, Huahua Wang, Lu Chen, Zheng Wu: Extended Fitting Methods of Active Shape Model for the Location of Facial Feature Points[C]. Springer Berlin/Heidelberg Press, Lecture Notes in Computer Science. ICVGIP 2006: 610-618
[37] Lucas B, Kanade T. An iterative image registration technique with an application to stereo vision. In:Hayes PJ, ed. Proc. of the 7th Int'1 Joint Conf. on Artificial Intelligence.Vancouver: Morgan Kaufmann Publishers, 1981. 674～679.
[38] Jean-Yves Bouguet. Pyramidal Implementation of the Lucas Kanade Feature Tracker. The paper is included into OpenCV distribution
[39] 郑忠龙,人脸识别中新算法的实现与改进,上海交通大学博士论文,2005
[40] 戈新良,人脸姿势估计与各姿势下的人脸对齐,上海交通大学博士论文,2006
[41] 王华华,基于统计学习的人脸识别方法研究,上海交通大学硕士论文, 2007
[42] 石林英,基于改进的 ASM 方法的人脸特征点检测,中山大学硕士学位论文,2005
[43] 徐艳,陈孝威,人脸检测中的眼睛定位算法研究,计算机和信息技术,2007
[44] Pentland A, Moghaddam B, T Starner, View-based and modular eigenspaces for face recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994: 84-91.
[45] Okada K, Malsburg C V D, Pose-invariant face recognition with parametric linear subspaces, Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, 2002: 71-76.
[46] 王守觉,徐键,王宪保等,基于仿生模式识别的多镜头人脸身份确认系统研究,电子学报,31(1), 2003:1-3。
[47] Lanitis A, Taylor C J, Cootes T F, Automatic interpretation and coding of face images using flexible template, IEEE Transaction on Pattern Analysis and Machine Intelligence, 19(7), 1997: 743-756.
[48] Cootes T F, Edwards G J, Taylor C J, Active appearance model, IEEE Transaction on Pattern Analysis and Machine Intelligence, 23(6), 2001: 681-685.
[49] Li S Z, Cheng Yan Shui, Hongjiang Zhang, et al, Multi-view face alignment using direct appearance models, Proceedings of 5th IEEE International Conference on Automatic Face and Gesture Recognition, 2002:324-329.
[50] Yongmin Li, Shaogang Gong, Heather Liddell, Recognising trajectories of facial identities using kernel discriminant analysis, Image and Vision Computing, 21(13-14), 2003: 1077-1086.
[51] Gordon G. Face recognition based on depth maps and surface curvature, In Geometric methods in Computer Vision, The Proceedings of SPIE, 1570, 1991: 234-247.
[52] Ralph GrossIain, Iain Matthews, Simon Baker, Eigen light-fields and face recognition across pose, Proceedings 5th IEEE International Conference on Automatic Face and Gesture Recognition, 5, 2002: 1-7.
[53] Dibyendu Nandy, Jezekiel Ben-Arie, Shape from recognition: a novel approach for 3-D face shape recovery, IEEE Transaction on Pattern Analysis and Machine Intelligence, 10(2), 2001: 206-217.
[54] Sami Romdhani, Volker Blanz, Thomas Vetter, Face identification by fitting a 3D morphable model using linear shape and texture error functions, in 7th European Conference on Computer Vision, 2002: 3-19.
[55] Sherrah J, Gong S, Ong E J, Face distributions in similarity space under varying head pose, Image and Vision Computing, 19(12), 2001: 807-819.
[56] Tenenbaum J B, Silva V D, Langford J C, A global geometric framework for nonlinear dimensionality reduction, Science, 29, 2000: 2319-2323.
[57] V D Silva, J B Tenenbaum, Local versus global methods for nonlinear dimensionality reduction, Advances in Neural Information Processing System, 15, 2003: 705-712.
[58] Roweis S T, Saul L K, Nonlinear dimensionality reduction by locally linear embedding, Science, 29, 2000: 2323-2326.
[59] Lawrence K.Saul, Sam T. Roweis, Think globally, fit locally: unsupervised learning of low dimensional manifolds, Journal of Machine Learning Research, 4(2), 2004: 119-155.
[60] M Belkin, P Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, Advances in Neural Information Processing System 14 (NIPS 2001) MIT Press, Cambridge 2002: 585-591.
[61] Yang M H, Face recognition using extended isomap, In International Conference on ImageProcessing Ⅱ,2002: 117-120.
[62] Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, Hongjiang Zhang, Face recognition using Laplacianfaces, IEEE Transaction on Pattern Analysis and Machine Intelligence, 27(3), 2005: 328-340.
[63] 颜水成,人脸定位与识别若干问题的研究,北京大学博士研究生学位论文。
[64] C.Garcia, G.Zikos, G.Tziritas, Wavelet packet analysis for face recognition, Image and Vision Computing, 18(4), 2000: 289-297.
[65] TS Lee, Image representation using 2D Gabor wavelets, IEEE Transaction on PAMI, 18(10), 1996: 959-971.
[66] Chengjun Liu, Gabor-based kernel PCA with fractional power polynomial models for face recognition, IEEE Transaction on PAMI, 26(5), 2004: 572-581.
[67] Yang, Q., Ding, X., Chen, Z., Discriminant local feature analysis of facial images, ICIP, 2, 2003: 863-866.
[68] Laurenz Wiskott, Jean-Marc Fellous, Norbert Kruger, Christoph von der Malsburg, Face recognition by elastic bunch graph matching, IEEE Transactions on PAMI, 19(7), 1997: 775-779.
[69] T.F.Cootes, C.J.Taylor, D.H.Cooper, J.Graham, Active shape models-their training and application, Computer Vision and Image Understanding, 61(1),1995: 38-59.
[70] Timothy F.Cootes, Gareth J.Edwards, and Christopher J.Taylor, Active appearance model, IEEE Transactions on PAMI, 23(6) ,2001: 681-685.
[71] Xinwen Hou, Stan Z.Li,Hongjiang Zhang,Qiansheng Cheng. Direct appearance models. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001: 828-833.
[72] Li, S.Z, Yan Shuicheng, Zhang Hongjiang, Cheng Qiansheng, Multi-view face alignment using direct appearance models, 5th IEEE International Conference on Automatic Face and Gesture Recognition. 2002: 309-314.
[73] Shuicheng Yan, Ce Liu, Stan Z.Li, Hongjiang Zhang, Heung-Yeung Shum, Face alignment using texture-constrained active shape models, Image and Vision Computing, 21(1) ,2003:69-75.
[74] Yi Zhou, Lie Gu, Hongjiang Zhang, Bayesian tangent shape model: estimating shape and pose parameters via Bayesian inference, IEEE Conference on Computer Vision and Pattern Recognition, 2003:16-22.
[75] Feng Jiao, Stan Li, Heung-Yeung Shum, Dale Schuurmans, Face alignment using statistical models and wavelet feature, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2003: 321-327.
[76] Fei Zuo, Peter H.N.de With, Fast facial feature extraction using a deformable shape model with Haar-wavelet based local texture attributes, Proceedings of IEEE Conference on ImageProcessing. 2004: 1425-1428.
[77] J.C.Gower, Generalized Procrustes analysis, Psychometrika 40, 1975: 33-51.
[78] Tsuyoshi Kawaguchi, Mohamed Rizon, Iris detection using intensity and edge information, Pattern Recognition, 36(2),2003:549-562.