复杂光照条件下人脸识别关键算法研究
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摘要
人脸识别是图像处理和模式识别领域的一个研究热点，它涉及到模式识别、图像处理、计算机视觉、机器学习等多个学科领域。人脸识别和认证技术在国家安全、公安系统和城市的公共安全等方面有着十分重要的应用前景。虽然经过了近40年的发展，但是，由于人脸是非刚性物体，并容易受到光照、姿态、表情等因素的影响，要实现一个高识别率、高鲁棒性的全自动人脸识别系统仍然是一个极具挑战性的课题。
本论文的研究围绕着光照问题展开，以提高人脸识别系统对光照的鲁棒性和识别率为主要目标，对人脸图像预处理、特征点定位、特征提取等关键环节展开研究，探讨光照问题的解决方法。本文的主要工作和创新点包括如下几个方面：
1、研究了光照归一化问题，提出了基于全变分模型的光照预处理算法
光照是影响人脸识别的主要因素之一，人脸图像因为光照条件的变化而产生的改变甚至超过由于不同个体之间差异而产生的变化。本文在对图像成像过程进行分析的基础上，根据辐照度光照模型，提出了基于全变分模型的光照预处理算法。实验表明，该算法可以有效地减少光照对人脸识别算法的影响，提高人脸识别系统的识别率和光照鲁棒性。
2、研究了人脸的特征点自动定位问题，提出了复杂光照条件下鲁棒的特征点定位算法
人脸特征点定位是人脸识别系统的一个关键环节，特征点定位的精度与鲁棒性直接影响着人脸识别系统的识别率和鲁棒性。主动形状模型(ASM)是一个重要特征点人脸对齐方法，但是其定位结果比较粗糙。而弹性图匹配算法则能够实现特征点的精确定位，但是其计算复杂度高，定位速度慢。本文在分析两者优缺点的基础上，提出了一种利用ASM和弹性图匹配算法实现人脸特征点的自动精确定位的方法。同时，为了克服ASM算法对光照和模型初始位置敏感的缺点，本文还提出了一种改进型的ASM算法。该算法引入了两种特征，即相位一致性特征和反射系数特征。在搜索过程中，首先利用相位一致性特征实现特征点粗定位，然后在
Face recognition is an active research topic in image processing and computer vision, which refers to pattern recognition, machine learning and so on. Face recognition has promising applications in public security, intelligent surveillance etc. Though face recognition has achieved great progress in the last 40 years, it is still a great challenge to build an automatic, high performance, high robust system for face recognition, due to the influence of illumination, pose and expression etc.
One important purpose of this dissertation is to improve the recognition accuracy and robustness of face recognition system under various lighting conditions. To achieve this purpose, the focus of our work is on the image preprocessing, feature point location and feature extraction. The main work and innovation of this dissertation includes:
1. The illumination normalization problem is studied, and an illumination preprocess algorithm based on total variation model is proposed.
Illumination is one of the factors that reduce the recognition accuracy of face recognition system. In most cases, the difference between two images caused by illumination change is greater than that caused by individual difference. According to the irradiance lighting model, we propose a preprocess algorithm of illumination based on total variation model. Experimental results showed that our method could reduce the effect of illumination, and improve the recognition accuracy and robustness of face recognition system.
2. The facial feature location problem is studied, and a robust facial feature point location algorithm under variable illumination is proposed.
Facial feature location algorithms are important for an automatic face recognition system. It affects distinctly the recognition accuracy and robustness of the face recognition system. Active Shape Model (ASM) is a population method for face alignment, but its location accuracy is rough. Elastic bunch graph matching (EBGM) is another import location algorithm. It can achieve a fine accuracy when locating facial feature points, while its computational complexity is high and convergence is slow. By analyzing the merit and demerit of ASM and EBGM, we proposed a
本论文的研究围绕着光照问题展开，以提高人脸识别系统对光照的鲁棒性和识别率为主要目标，对人脸图像预处理、特征点定位、特征提取等关键环节展开研究，探讨光照问题的解决方法。本文的主要工作和创新点包括如下几个方面：
1、研究了光照归一化问题，提出了基于全变分模型的光照预处理算法
光照是影响人脸识别的主要因素之一，人脸图像因为光照条件的变化而产生的改变甚至超过由于不同个体之间差异而产生的变化。本文在对图像成像过程进行分析的基础上，根据辐照度光照模型，提出了基于全变分模型的光照预处理算法。实验表明，该算法可以有效地减少光照对人脸识别算法的影响，提高人脸识别系统的识别率和光照鲁棒性。
2、研究了人脸的特征点自动定位问题，提出了复杂光照条件下鲁棒的特征点定位算法
人脸特征点定位是人脸识别系统的一个关键环节，特征点定位的精度与鲁棒性直接影响着人脸识别系统的识别率和鲁棒性。主动形状模型(ASM)是一个重要特征点人脸对齐方法，但是其定位结果比较粗糙。而弹性图匹配算法则能够实现特征点的精确定位，但是其计算复杂度高，定位速度慢。本文在分析两者优缺点的基础上，提出了一种利用ASM和弹性图匹配算法实现人脸特征点的自动精确定位的方法。同时，为了克服ASM算法对光照和模型初始位置敏感的缺点，本文还提出了一种改进型的ASM算法。该算法引入了两种特征，即相位一致性特征和反射系数特征。在搜索过程中，首先利用相位一致性特征实现特征点粗定位，然后在
Face recognition is an active research topic in image processing and computer vision, which refers to pattern recognition, machine learning and so on. Face recognition has promising applications in public security, intelligent surveillance etc. Though face recognition has achieved great progress in the last 40 years, it is still a great challenge to build an automatic, high performance, high robust system for face recognition, due to the influence of illumination, pose and expression etc.
One important purpose of this dissertation is to improve the recognition accuracy and robustness of face recognition system under various lighting conditions. To achieve this purpose, the focus of our work is on the image preprocessing, feature point location and feature extraction. The main work and innovation of this dissertation includes:
1. The illumination normalization problem is studied, and an illumination preprocess algorithm based on total variation model is proposed.
Illumination is one of the factors that reduce the recognition accuracy of face recognition system. In most cases, the difference between two images caused by illumination change is greater than that caused by individual difference. According to the irradiance lighting model, we propose a preprocess algorithm of illumination based on total variation model. Experimental results showed that our method could reduce the effect of illumination, and improve the recognition accuracy and robustness of face recognition system.
2. The facial feature location problem is studied, and a robust facial feature point location algorithm under variable illumination is proposed.
Facial feature location algorithms are important for an automatic face recognition system. It affects distinctly the recognition accuracy and robustness of the face recognition system. Active Shape Model (ASM) is a population method for face alignment, but its location accuracy is rough. Elastic bunch graph matching (EBGM) is another import location algorithm. It can achieve a fine accuracy when locating facial feature points, while its computational complexity is high and convergence is slow. By analyzing the merit and demerit of ASM and EBGM, we proposed a
引文
[1] Samal & Iyengar, "Automatic Recognition and Analysis of Human Faces and Facial Expressions," Pattern Recognition, vol. 25, 1992;
[2] Valentin, Abdi, O' Toole & Cottrell, "Connectionist Models of Face Processing: A Survey," Pattern Recognition, Vol. 27, 1994;
[3] Chellappa, Wilson & Sirohey, "Human and Machine Recognition of Faces: A Survey," Proc. IEEE, Vol. 83, 1995;
[4] Grudin, "On Internal Representations in Face Recognition Systems," Pattern Recognition, vol. 33, 2000;
[5] Zhao, Chellappa, Rosenfeld & Phillips, "Face Recognition: A Literature Survey," UMD CS-TR-4167, 2000;
[6] 周杰，陆春雨，张长水，李衍达，“人脸自动识别方法综述，”电子学报，No．4，2000：
[7] 张翠平，苏光大，“人脸识别技术综述，”中国图形图像学报，vol．5，2000：
[8] 山世光，“人脸识别中若干关键问题的研究”，中国科学院计算所博士学位论文，2004年5月。
[9] M. Turk, A. Pentland," Eigenfaces for Recognition," Journal of Cognitive Neurosicence, Vol. 3, No. 1, 1991, pp. 71-86;
[10] A. Pentland, B. Moghaddam, T. Starner," View-Based and Modular Eigenspaces for Face Recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 21-23 June 1994, Seattle, Washington, USA, pp. 84-91;
[11] H. Moon, P. J. Phillips," Computational and Performance aspects of PCA-based Face Recognition Algorithms", Perception, Vol. 30, 2001, pp. 303-321;
[12] M. S. Bartlett, J. R. Movellan, T. J. Sejnowski, "Face Recognition by Independent Component Analysis", IEEE Trans. On Neural Networks, Vol. 13, No. 6, November 2002, pp. 1450-1464;
[13] C. Liu, H. Wechsler, "Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition", Proc. Of the Second International Conference on Audio- and Video-based Biometric Person Authentication, AVBPA' 99, 22-24 March 1999, Washington D. C., USA, pp. 211-216;
[14] C. Liu, H. Wechsler, "A Unified Bayesian Framework for Face Recognition", Proc. Of the 1998 IEEE International Conference on Image Processing, ICIP98, 4-7 October 1998, Chicago, Illinois, USA, pp. 151-155;
[15] B. Moghaddam, C. Nastar, A. Pentland, "A Bayesian Similarity Measure for Deformable Image Matching," Image and Vision Computing, Vol. 19, Issue 5, May 2001, pp.235-244;
[16] M.H. Yang, " Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods, " Proc. Of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 20-21 May 2002, Washington D. C., USA, pp. 215-220;
[17] F.R. Bach, M.I. Jordan, "Kernel Independent Component Analysis, " Journal of Machine Learning Research, Vol.3, 2002, pp. 1-48;
[18] B. Scholkopf, A. Smola, K. R. Muller, "Nolinear Component Analysis as a Kernel Eigenvalue Problem," Technical Report No.44, December 1996, 18 pages;
[19] S. Zhou, R. Chellappa, B. Moghaddam, "Intra-personal kernel space for face recognition, " Proc. Of the 6~(th) International Conference on Automatic Face and Gesture Recognition, FGR2004, 17-19 May 2004, Seoul, Korea, pp.235-240;
[20] S. Zhou, R. Chellappa, "Multiple-exemplar discriminant analysis for face recognition, " Proc. Of the 17" International Conference on Pattern Recognition, ICPR04, 23-26 August 2004, Cambridge, UK, pp. 191-194;
[21] J. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, "Face Recognition Using Kernel Direct Discriminant Analysis Algorithms," IEEE Trans. On Neural Networks, Vol.14, No. 1, January 2003, pp. 117-126;
[22] J. Huang, B. Heisele, V. Blanz, "Component-based Face Recognition with 3D Morphable Models, " Proc. Of the 4" International Conference on Audio- and Video-Based Biometric Person Authentication, AVBPA 2003, 09-11 June 2003, Guildford, UK, pp. 27-34;
[23] V. Blanz, T. Vetter, "A Morphable Model for the Synthesis of 3D Faces, " Proc. Of the SIGGRAPH99, 08-13 August 1999, Los Angeles, USA, pp. 187-194;
[24] V. Blanz, T. Vetter, "Face Recognition Based on Fitting a 3D Morphable Model, " IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.25, NO. 9, September 2003, pp. 1063-1074;
[25] B. Moghaddam, J. H. Lee, H. Pf ister, R. Machiraju, "Model-Based 3D Face Capture with Shape-from-Silhouettes, " Proc. Of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, AMFG, 17 October 2003, Nice, France, pp. 20-27;
[26] A. Bronstein, M. Bronstein, R. Kimmel, and A. Spira, "3D Face recognition without facial surface reconstruction" , in Proceedings of ECCV 2004, Prague, Czech Republic, May 11-14, 2004;
[27] A. Bronstein, M. Bronstein, and R. Kimmel, "Expression-invariant 3D face recognition, " Proc. Audio & Video-Based Biometirc Person Authentication (AVBPA), Lecture Notes in Comp. Sicence 2688, Springer, 2003, pp. 62-69;
[28] G. Guo, S. Z. Li, K. Chan, "Face Recognition by Support Vector Machines," Proc. Of the IEEE International Confernece on Automatic Face and Gesture Recognition, 26-30 March 2000, Grenobel, France, pp. 196-201;
[29] B. Heisele, P. Ho, T. Poggio, "Face Recognition with Support Vector Machines: Globlal versus Component-based Approach, " Proc. Of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, Vol.2, 09-12 July 2001, Vancouver, Canada, pp.688-694;
[30] K. Jonsson, J. Matas, J. Kittler, Y. P. Li, "Learning Support Vectors for Face Verification and Recognition," Proc. Of the IEEE International Conference on Automatic Face and Gesture Recognition, 26-30 March 2000, Grenoble, France, pp. 208-213;
[31] Y. Freund, R. E. Schapire, "A Decision-Theoretic Generalization of 0n-Line Learning and an Application to Boosting, " Journal of Computer and System Sciences, Vol.55, No. 1, 1997, pp. 119-139;
[32] R. Meir, G. Raetsch, "An Introduction to Boosting and Leveraging" , in S. Mendelson and A. Smola, Editors, Advanced Lectures on Machine Learning, LNAI 2600, pp. 118-183, Springer, 2003;
[33] P. Viola, M. J. Jones, "Robust Real-Time Face Detection," International Journal of Computer Vision, Vol.57, No. 2, May 2004, pp. 137-154;
[34] J. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, S. Z. Li, "Ensemble-based Discriminant Learning with Boosting for Face Recognition, " IEEE Transactions on Neural Networks, Vol.17, No. 1, pp. 166-178, January 2006;
[35] P. Yang, S. Shan, W. Gao, Stan Li, D. Zhang, "Face Recognition Using Ada-Boosted Gabor Features, " Proceeding of the 6~(th) International Conference on Face and Gesture Recognition, Korea, May, 2004;
[36] Xiangsheng Huang, Stan Z. Li, Wang Yangsheng, "Jensen-Shannon Boosting for Object Recognition, " International Conference on Computer Vision and Pattern Recognition 2005;
[37] T. F. Cootes, K. Walker, C. J. Taylor, "View-Based Active Appearance Models, " Proc. Of the IEEE International Conference on Automatic Face and Gesture Recognition, 26-30 March 2000, Grenoble, France, pp. 227-232;
[38] T. F. Cootes, G. J. Edwards, C. J. Taylor, "Active Appearance Models," Proc. European Conf. Computer Vision, Vol.2, pp.484-498,1998;
[39] G.Edwards, T. Cootes, and C. Taylor, "Advances in Active Appearance Models, " Proc. International Conf. Computer Vision, pp.137-142, 1999;
[40] M. B. Stegmann, "Active Appearance Models, Theory, Extension and Cases," Master Thesis, Technical University of Denmark, 2000;
[41] X. W. Hou, S.Z. Li, H. J. Zhang, Q. S. Cheng, "Direct Appearance Models," In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Hawaii, December, 2001;
[42] S.Z. Li, S. C. Yan, H. J. Zhang, Q. S. Cheng, "Multi-View Face Alignment Using Direct Appearance Models," Proceedings of The 5~(th) International Conference on Automatic Face and Gesture Recognition, pp324-329, Washingtion D. C. , USA, 20-21 May 2002;
[43] S.C. Yan, C. Liu, S. Z. Li, H. J. Zhang, H. Shum, Q. S. Cheng, "Face Alignment Using Texture-Constrained Active Shape Models," Image and Vision Computing, pp. 69-75, Vol.21, No.1, 10 January 2003;
[44] Tong Wang, Haizhou Ai, Gaofeng Huang, "A Two-Stage Approach to Automatic Face Alignment," Proceedings of SPIE - The International Society for Optical Engineering, v5286, n2, 2003, p558-563;
[45] Yi Zhou, Lie Gu, Hongjiang Zhang, "Bayesian Tangent Shape Model: Estimating Shape and Pose Parameters via Bayesian Inference," Proceeding of the IEEE Conference on Computer Vision and pattern Recognition (CVPR 2003), Wisconsin, USA, June 16-22, 2003;
[46] K. Etemad, R. Chellappa, "Discriminant Analysis for Recognition of Human Faces Images," Journal of the Optical Society of America A, Vol.14, No. 8, August 1997, pp.1724-1733;
[47] P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection," Proc. Of the 4th European Conference on Computer Vision, ECCV96, 15-18 April 1996, Cambridge, UK, pp. 45-58;
[48] W. Zhao, R. Chellappa, A. Krishnaswamy, "Discriminant Analysis of Principal Components for Face Recognition," Proc. Of the 3rd IEEE International Conference on Face and Gesture Recognition, FG98, 14-16 April 1998, Nara, Japan, p. 336 ;
[49] A.M. Martinez, A.C. Kak, "PCA versus LDA," IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol.23, No. 2, 2001, pp.228-233;
[50] W. Zhao, A. Krishnaswamy, R. Chellappa, D. L. Swets, J. Weng, "Discriminant Analysis of Principal Components for Face Recognition, Face Recognition: From Theory to Applications," H. Wechsler, P. J. Phillips, V. Bruce, F. F. Soulie, and T.S. Huang, eds., Springer-Verlag, Berlin, 1998, pp. 73085;
[51] J. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, "Face Recognition Using LDA-Based Algorithms," IEEE Trans. On Neural Networks, Vol. 14, No. 1, January 2003, pp. 195-200;
[52] S. Zhou, V. Krueger, R. Chellappa, "Probabilistic recognition of human faces from video," Computer Vision and Image Understanding, Vol.91, 2003, pp. 214-245;
[53] S. Zhou, R. Chellappa, B. Moghaddam, "Visual tracking and recognition using appearance-adaptive models in particle filters," IEEE Trans. On Image Proceesing, Vol.13, No. 11, pp. 1491-1506;
[54] S. Zhou, R. Chellappa, "Probabilistic identity characterization for face recognition," Proc. Of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, 27 June-02 July 2004, Washington D. C., USA;
[55] Z. Biuk, S. Loncaric, "Face Recognition Rrom Multi-pose Image Sequence," Proc. Of the 2nd IEEE R8-EURASIP Symposium on Image and Signal Processing and Analysis, ISPA01, 19-21 June 2001, Pula, Croatia, pp. 319-324;
[56] K. C. Lee, J. Ho, M.H. Yang, D. Kriegman, "Video-Based Face Recognition Using Probabilistic Appearance Manifolds, " Proc. Of the IEEE Computer Society Conference on Computer Vision an d Pattern Recognition, CVPR 2003, Vol. 1, 16-22 June 2003, Madison, Wisconsin, USA, pp. 313-320;
[57] X. Liu, T. Chen, "Video-Based Face Recognition Using Adaptive Hidden Markov Models," Proc. Of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR2003, Vol.1, 16-22 June 2003, Madison, Wisconsin, USA, pp.340-345;
[58] G. Aggarwal, A. K. Roy-Chowdhury, R. Chellappa, "A System Identification Approach for Video-based Face Recognition," Proc. Of the International Conference on Pattern Recognition, 23-26 August 2004, Cambridge, UK;
[59] A.V. Nefian, M. H. Hayes III, "Hidden Markov Models for Face Recognition," Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP98, 12-15 May 1998, Seattle, Washington, USA, pp. 2721-2724;
[60] A.V. Nefian, M.H. Hayes, "Maximum likelihood training of the embedded HMM for face detection and recognition," Proc. Of the IEEE International Conference on Image Processing, ICIP 2000, Vol. 1, 10-13 September 2000, Vancouver, BC, Cannada, pp. 33-36;
[61] A. V. Nefian, "Embedded Bayesian networks for face recognition, " Proc. Of the IEEE International Conference on Multimedia and Expo. Vol. 2, 26—29 August 2002, Lusanne, Switzerland, pp.133-136;
[62] D.M. Blackburn, M. Bone, P. J. Phillips, "Facial Recognition Vendor Test 2002: Evaluation Report, " Feb. 2001;
[63] P. J. Phillips, P. Grother, R. J. Micheals, et al. "Face Recognition Vendor Test 2002: Overview and Summary, " March 2003;
[64] P. J. Phillips, P. Grother, R. J. Micheals, et al. "Face Recognition Vendor Test 2002: Evaluation Report," NISTIR 6965, March 2003;
[65] D.M. Blackburn, M. Bone, P. J. Phillips, "Facial Recognition Vendor Test 2000: Executive Overview," FRVT2000 Evaluation Report;
[66] D.M. Blackburn, M. Bone, P. J. Phillips, "Facia Recognition Vendor Test 2000: Evaluation Report, " Feb. 2001;
[67] P. J. Phillips, H. Moon, S. A. Rizvi, "The FERET evaluation methodology for face recognition algorithms," IEEE Trans. On PAMI, 2000, 22(10):1090-1104;
[68] P. J. Phillips, H. Wechsler, J. Huang, et al. "The FERET database and evaluation procedure for face recognition algorithms, " Image and Vision Computing Journal, 1998, 16(5):295-306;
[69] S.A. Rizvi, P.J. Phillips, H. Moon, "The FERET verification testing protocol for face recognition algorithms," Image and Vision Computing, 1998, 48-53;
[70] P. J. Phillips, P. J. Rauss, S. Z. Der, "FERET recognition algorithm development and test results, " ARL-TR-995, Oct. 1996;
[71] FERET. The facial recognition technology database. 1996;
[72] R. L. Hsu, M. Abdel-Mottaleb, A. K. Jain, "Face Detection in Color Images", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No. 5, pp. 696-706;
[73] R. Brunelli and T. Poggio, "HyperBF Networks for Real Object Recognition", Proc. IJCAI, pp. 1278-1284, Sydney, Australia, 1991.
[74] B. Manjunath, C. Shekhar, R. Chellappa, and C. vonder Malsburg, "A Robust Method for Detecting Image Features with Application to Face Recognition and Motion Correspondence," ICPR, B, pp. 208-212, 1992.
[75] S. Edelman, D. Reisfeld, and Y. Yeshurun," A System for Face Recognition that Learns from Examples," Proc. European Conf. Computer Vision, S. Sandini, ed., pp.787-791. SpringerVerlag, 1992;
[76] S.G. Shan, W. Gao, B. Cao, D. B. Zhao, "Illumination Normalization for Robust Face Recognition against Varying Lighting Conditions, " Proc. IEEE Int. Workshop on Analysis Modeling of Faces and Gestures (AMFG03), pp. 157-164, 2003;
[77] P. N. Belhumeur and D. W. Jacobs, "Comparing images under variable illumination," Proc. 1998 IEEE Conf. on Computer Vision and Patter Recognition (CVPR98), pp. 610-617, 1998;
[78] M. Savvides, B. V. Kumar and P. K. Khosla, "Eigenphase vs. Eigenfaces, " Proc. 17~(th) Int. Conf. Pattern Recognition (ICPR04), pp. 810-813, 2004;
[79] H. T. Wang, S. Z. Li, Y. S. Wang, "Face Recognition under Varying Lighting Conditions Using Self Quotient Image, " Proc. 6~(th) IEEE Int. Conf. on Automatic Face and Gesture Recognition (FGR04), pp. 819-824, 2004;
[80] H.T. Wang, S. Z. Li, Y.S. Wang, "Generalized Quotient Image, " Proc. 2004 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR04), Vol.2, pp. 498-505, 2004;
[81] Hallinan, "A Low-Dimensional Representation of Human Faces for Arbitrary Lighting Conditions" , Proc. Computer Vision and Pattern Recognition Conf. pp. 995-999, 1994;
[82] Ravi Ramamoorthi, Pat Hanrahan, "On the relationship between radiance and irradiance: determining the illumination for images of a convex Lambertian object" , J. Opt. Soc. Am., Vol.18, No.10, 2001;
[83] Ravi Ramamoorthi, "Analytic PCA Construction for Theoretical Analysis of Lighting Variability in Images of a Lambertian Object", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No.10, 2002-10-21;
[84] [84] Ronen Brasri, David Jacobs, "Lambertian Reflectance and Linear Subspaces" , NEC Research Institute Technical Report 2002-172R;
[85] F. X. Sillion, J. Arvo, S. H. Westin, and D. Greenberg. A global illumination solution for general re ectance distributions. In SIGGRAPH 91, ppl87-196, 1991;
[86] S. K. Nayar and H. Murase, "Dimensionality of Illumination Manifolds in Eigenspace" , Technical Report CUCS-021-94, August 1994;
[87] B. Cabral, N. Max, R. Springmeyer. "Bidirectional Reflection Functions from Surface Bump Maps" , Comp. Graph., 21(4), 1987;
[88] Amnon Shashua, and Tammy Riklin-Raviv, "The quotient image: Class-based re-rendering and recognition with varying illuminations", Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, 2001;
[89] T. Riklin-Raviv and A. Shashua, "The Quotient image: Class-based recognition and sysnthesis under varying illumination" . In Proceedings of the 1999 Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, 1999;
[90] Amnon Shashua, "Geometry and Photometry in 3D Visual Recognition," Ph.D. Thesis, MIT, Nov,1992;
[91] P. N. Belhumeur, David J. Kriegman," What is the set of Images of an Object Under All possible Lighting Conditions?", IEEE conf. On Computer Vision and Pattern Recognition, 1996;
[92] Athinodoros S. Georghiades and Peter N. Belhumeur, "Illumination cone models for recognition under variable lighting: Faces" , CVPR, 1998;
[93] T. Sim, T. Kanade, "Combining Models and Exemplars for Face Recognition: An Illumination Example," Proc. Workshop on Models versus Exemplars in Computer Vision with CVPR01, 2001;
[94] W. Y. Zhao and R. Chellappa, "Illumination-insensitive Face Recognition using Symmetric Shape-from-Shading," Proc. 2000 IEEE Conf. on Computer Vision and Pattern Recognition (CVPROO), Vol.1, pp.286-293, 2000;
[95] W. Y. Zhao, R. Chellappa, "SFS Based View Synthesis for Robust Face Recognition," Proc. 4~(th) Int. Conf. on Face and Gesture Recognition (FGROO), pp.285-292, 2000;
[96] Yael Adnin, Yael Moses and Shimon Ullman, "Face recognition: The problem of compensating fro changes in illumination direction", IEEE Transactions on Pattern Analysis and Machine intelligence, Vol. 19, No.7, 1997, pp 712-732.
[97] W. Zhao, R. Chellappa, "Robust face recognition using symmetric shape-from-shading" Technical report, Center for Automation Research, University of Maryland, 1999.
[98] Face Recognition Vendor Test (FRVT) 2002, Jonathon Phillips etc.
[99] T. Riklin-Raviv, A. Shashua: The Quotient image:class-based re-rendering and recognition with varying illumination conditions. In: IEEE PAMI. (2001) 10.
[100]Georghiades, A., Kriegman, D., Belhumeur, P.: From few to many: Generative models for recognition under variable pose and illumination. IEEE PAMI (2001)10, 14.
[101]Ravi Ramamoorthi, Pat Hanrahan, "On the relationship between radiance and irradiance: determining the illumination from images of a convex Labertianobject", J. Opt. Soc. Am. Vol. 18, No. 10, 2001.
[102][102]Land, E., McCann, J., "Lightness and retinex theory," Journal of the Optical Society of America 61 (1971) ;
[103]Jobson, D., Rahman, Z., Woodell, G.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. On Image Processing 6 (1997);
[104]H. Wang, S. Z. Li, and Y. Wang, "Generalized Quotient Image", IEEE International Conference on Computer Vision and Pattern Recognition, 2004
[105]B. K, P. Horn, "Robot Vision", MIT Press, 1986;
[106]Ralph Gross, Vladimir Brajovic, "An Image Preprocessing Algorithm for Illumination Invariant Face Recognition", 4th International Conference on Audio-and Video Based Biometric Person Authentication(AVBPA), 2003
[107]J. Tumblin, G. Turk, "LCIS: A boundary hierarchy for detail-preserving contrast reduction," In: ACM SIGGRAPH. (1999);
[108]L. Rudin, S. Osher, and E. Fatemi, "Nolinear total variation based noise removal algorithms" Physica D, vol. 60, pp. 259-268, 1992;
[109] T. F. Chan, and S. Esedoglu, "Aspects of Total Variation Regularized L1 Function Approximation" UCLA CAM Report 04-07, Feb, 2004;
[110]T. Sim, S. Baker, and M. Bsat, " The CMU Pose, Illumination, and Expression (PIE) Database", Processing of the IEEE International Conference on Automatic Face and Gesture Recognition. May 2002;
[111]B. Moghaddam and A. Pentland, "Probabilistic Visual Learning for Object representation," IEEE Trans. On PAMI, vol.20, no. 7, pp696-710, July, 1997;
[112]B. Moghaddam, T. Jebara, A. Pentland, "Bayesian Face Recognition," Pattern Recognition Vol.33 (2000), ppl771-1782, 2000;
[113]E. Bailly-Bailliere, S. Bengio, F. Bimbot, etc. "The BANCA database and evaluation protocol," In Audio- and Video-Based Biometric Person Authentication (AVBPA), pages 625-638, 2003;
[114]Wen Gao, Bo Cao, Shiguang Shan, etc. "The CAS-PEAL Large-Scale Chinese Face Database and Evaluation Protocols," Technical Report No. JDL_TR_04_FR_001, Joint Research U Development Laboratory, CAS, 2004;
[115] A. R. Martinez and R. Benavente, "The AR face database," Technical Report 24, Computer Vision Center (CVC) Technical Report, Barcelona, Spain, June 1998;
[116] F. S. Samaria, and A. C. Harter, "Parameterisation of a Stochastic Model for Human Face Identification, " Proceedings of the 2~(nd) IEEE Workshop on Applications of Computer Vision, December 1994.
[117] K. Messer, J. Matas, J. Kittler, etc. "XM2VTSDB: The extended M2VTS database," In Second International Conference on Audio- and Video-Based Biometric PersonAuthentication, March 1999;
[118]M. Turk, A. Pentland, "Eigen-faces for Recognition," Jounal of cognitive neuroscience, 3(1), pp 71-86, 1991;
[119]S, Z. Li, A. K. Jain, "Handbook of Face Recognition," Springer Verlag, New York. ISBN# 0-387-40595-x, 2004;
[120]Hyoja-Dong, and Nam-Gu, "Asian Face Image Database PF01," Pohang University of Science and Technology;
[121]B. —W. Hwang, H. Byun, M.-C. Roh, and S.-W. Lee, "Performance evaluation of face recognition algorithms on the asian face database, KFDB," In Audio- and Video-Based Biometric Person Authentication (AVBPA), pages 557-565, 2003;
[122]K. Messer, K., J. Kittler, M. Sadeghi, et al. Face Authentication Test on the BANCA Database. Proc. the 17th International Conference on Pattern Recognition (ICPR04), pp. 523-532, 2004.
[123]C.Liu. "Statistical and evolutionary approaches for face recognition," George Mason University, Ph.d Dissertation, 1999.
[124]L. F. Chen, H. M. Liao, J. C. Lin, M. T. Ko, and G. J. Yu. "A New LDA-based Face Recognition System Which Can Solve the Small Sample Size Problem," Pattern Recognition, vol. 33, no. 10, pp. 1713-1726, 2000.
[125]C. Liu and H. Wechsler, "A Shape and Texture Based Enhanced Fisher classifier for Face Recognition," IEEE Trans. on Image Processing, vol. 10, no. 4, pp. 598-608, 2001.
[126]C. Liu, H. Wechsler, "Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition," IEEE Trans. Image Processing, vol. 11, no. 4 pp. 467-476, 2002;
[127]H. Yu and J. Yang, "A Direct LDA Algorithm for High-dimensional Data - with Application to Face Recognition," Pattern Recognition, Vol. 34, pp 2067-2070, 2001.
[128]M. H. Yang, N. Ahuja, D. Kriegman Face Recognition Using Kernel Eigenfaces, Int. Conf. on Image Processing, 2000, vol.1, pp. 37-40;
[129]Q. S. Liu, R.Huang, H. Q. Lu and S. D. Ma, "Face Recognition Using Kernel Based Fisher Discriminant Analysis" , Proc. Int'l Conf. Automatic Face and Gesture Recognition, pp. 197-201, 2002.
[130] M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, etc., "Distortion Invariant Object Recognition in the Dynamic Link Architecture," IEEE Trans. On Computers, 42(3), pp300-311, 1993;
[131]L. Wiskott, J. M. Fellous, N. Kruger, C. v. d. Malsburg, "Face Recognition by Elastic Bunch Graph Matching," IEEE Trans. On PAMI, Vol. 19, No. 7, pp775-779, 1997;
[132] C. L. Rotropoulos, A. Tefas, I. Pitas, "Frontal Face Authentication Using Discriminating Grids with Morphological Feature Vectors," IEEE Trans. On Multimedia, Vol. 2, No. 1, pp14-26 March, 2000;
[133] A. Tefas, C. Kotropoulos, and I. Pitas, Using Support Vector Machines to Enhance the Performance of Elastic Graph Matching for Frontal Face Authentication, IEEE Transactions On Pattern Analysis And Machine Intelligence, VOL. 23, NO. 7, pp735-746, JULY 2001.
[134] 丁嵘，苏光大，林行刚，“使用关键点信息改进弹性匹配人脸识别算法，”电子学报，Vol．30，No．9：1292-1294，2002：
[135] B. Moghaddam, W. Wahid, and A. Pentland, "Beyond eigenface: probabilistic matching for face recognition," Proc. 3rd IEEE Int. Conf. on Automatic Face and Gesture Recognition (FGR9B), pp. 30-35, April, 1998.
[136] B. Moghaddam. "Principal Manifolds and Probabilistic Subspaces for Visual Recognition," IEEE Trans. on PAMI, Vol. 24, No. 6, pp. 780-788, June 2002.
[137] Athinodoros S. Georghiades, Peter N. Belhumeur, David J. Kriegman, "From Few to Many: Illumination Cone Models for Face Recognition Under Variable Lighting and Pose," IEEE Trans. on PAMI, 23(6) pp. 643660-139, 2001. 6
[138] P. N. Bellhumer, J. Hespanha, and D. Kriegman. "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Face Recognition, 17(7): 711—720, 1997
[139] L. Denes, P. Metes, and Y. Liu, "Hyperspectral face database," Technical report, Robotics Institute, Carnegie Mellon University, 2002;
[140] D. Socolinsky, L. Wolff, J. Neuheisel, and C. Eveland, "Illumination invariant face recognition using thermal infrared imagery," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2001;
[141] P. Flynn, K. Bowyer, and P. J. Phillips, "Assesment of time dependency in face recognition: an initial study," In Audio- and Video-Based Biometric Person Authentication (AVBPA), pp. 44-51, 2003;
[142] A. O' Vooole, J. Harms, S. Snow, D. R. nurst, M. Pappas, and H. Abdi, "A video database of moving faces and people," submitted, 2003;
[143] D. Graham and N, Allison, "Characterizing virtual eigensignatures for general purpose face recognition," In H. Wechsler, P. J. Phillips, V. Bruce, F. Fogelman-Soulie, and T. Huang, editors, Face Recognition: From Theory to Applications," NATO ASI Series F. pages 446-456, 1998;
[144] NIST databse, http://www.nistgov/srd/nistsd18.htm;
[145] R. Brunelli, T. Poggio, Face recognition: features vs. templates. IEEE Trans. on PAMI, 15(10): 1042-1052, 1993;
[146] FRGC, http://www.frvt.org/FRGC/;
[147] T. Sim and Y. Kanade, "Illuminating the Face," Technology report CMU-RI-TR-01-31, Robotics Institute, Carnegie Mellon University, September, 2001;
[148] G. D. Finlayson, S. D. Hordley, M. S. Drew, "Removing Shadows from Images Using Retinex[A]," Proceedings of IS&T/ SID Tenth Color Imaging Conference: Color Science, Systems andApplications [C]. Scottsdale, Arizona: IS&T/SID, 2002. 73-79.
[149] Edwin H Land, "Recent advances in retinex theory[J], " Vision Research, 1986, 26(1): 7-21;
[150] D. J. Jobson, Z. Rahman, G. A. Woodell, "Properties and performance of a center/surround retinex[J], " IEEE Trans. on image processing, 1997, 6(3): 451-462.
[151] 唐亮，谢维信，黄建军，肖志级，“城市航空影像中基于Retinex的阴影消除”，电子学报，Vol．33，No．3，Mar．2005：
[152] 熊志勇等，基于本征特征的弹性模板匹配法，计算机研究与发展，1999．7第7期：
[153] 丁嵘等，使用部件信息改进弹性匹配人脸识别，光电子·激光，2002．8 Vol．13；
[154] N. Kruger, "An algorithm for the learning of weights in discrimination functions usingapriori constraints [J]," IEEE Trans. on PAMI, 1997, 19: 764-768;
[155] Wurtz R P. Object recognition robust under translations, deformation, and changes in background[J]. IEEE Trans on PAMI, 1997, 19: 769-774;
[156] M. Kass, A. Witkin, andD. Terzopoulos. "Snakes: Active contour models," Int. Journal of Computer Vision, pp. 321-331,1988;
[157] A. L. Yuille, "Deformable templates for face detection," J. Cogn. neurosci. 3, 59-70, 1991.
[158] A. L. Yuille, P. W. Hallinan, D. S. Cohen, "Feature extraction from faces using deformable templates," International Journal of Computer Vision, 8, 99-111. 1992;
[159] T. F. Cootes, C. J. Taylor, et al. Active Shape Models——Their Training and Application. Computer Vision and Image Understanding, January 1995, Vol. 61, No. 1, PP. 38-59;
[160] T. F. Cootes, C. J. Taylor, et al. Training Models of Shape from Sets of Examples. In Prec. British Machine Vision Conference. Springer-Verlag, 1992, pp550-557;
[161] T. F. Cootes and C. J. Taylor. Statistical Models of Appearance for Computer Vision. Technical Report, Imaging Science and Biomedical Engineeing, University of Manchester, 2000;
[162] L. Wiskott andC. v. d. Malsburg, "Face Recognition by Dynamic Link Matching". Internal Report IROINI 96-05, Institut for Neuroinformatik, Ruhr-University Bochum, 1996.
[163] L. Wiskott, J. M. Fellous, N. Kruger and C. v. d. Malsburg, "Face Recognition and Gender Determination", Prec. Int' 1 Workshop on Automatic Face and Gesture Recognition, pp. 92-97, 1995;
[164] 陶亮，庄镇泉。复杂背景下自动人眼定位。计算机辅助设计与图形学学报，2003．1 Vol．15；
[165] C Y. Xu, and J. L. Prince, "Snakes, Shapes, and Gradient Vector Flow", IEEE Transactions on Image Processing, Vol. 7, No. 3, March 1998;
[166] G. Hamarneh, T. Gustavsson, "Statistically Constrained Snake Deformations", Image Analysis Group, Chalmers University of Technology, Sweden, 2000, IEEE;
[167] Peter Kovesi, "Image Features From Phase Congruency," Videre. Volume 1, Number 3, summer 1999;
[168] Peter Kovesi, "Image Features from Phase Congruency[R]," Technical Report 95/4, University of Western Australia, Robotics and Vision Group, 1995.
[169] D. J. Field, "Relation between the Statistics of Natural Images and the Response Properties of Cortical Ceils," JOSA A, 4(12): 2379-2394, December 1987.
[170] T. Stockam, "Image processing in the context of a visual model," Proceedings of the IEEE 60 (1972) 828-848.
[171] Jianke Zhu, Mang I Yai and Peng Un Mak, "Face Recognition Using 2D DCT with PCA", in The 4nd Chinese Conference on Biometric Recognition (Sinobiometrics' 03) at Beijing, P. R. China, Dec. 7-8, 2003;
[172] R. Brunelli and T. Poggio, Face Recognition through Geometrical Features, Proceedings of ECCV '92, pp792-800;
[173] B. S. Manjunath, R. Chellappa, C.v.d. Malsburg, "A Feature based approach to face recognition," in Pro. IEEE Computer Soc. Con. On Computer Vision and Pattern Recognition, 1992, pp373-378
[174] Z. Hong, "Algebraic feature extraction of image for recognition," Pattern Recognition, 24(3), pp211-219, 1991;
[175] 洪子泉，杨静宇．用于图像识别的图像代数特征抽取．自动化学报，1992，18(2)： 233～237．
[176] 洪子泉，杨静宇．基于奇异值特征和统计模型的人像识别算法．计算机研究与发展，1994，31(3)：60～65．
[177] 黄修武，基于代数方法的人脸图像特征提取与识别，南京理工大学博士论文，1998；
[178] P. Penev and J. Atick, "Local Feature Analysis: A General Statistical Theory for Object Representation," Network: Computation in Neural Systems, vol. 7, pp. 477-500, 1996;
[178] Jianhuang Lai, P. C. Yuen, G. C. Feng, "Spectroface: A Fourier-based Approach for Human Face Recognition," ICMI, Vol. 2, 115-120, 1999;
[179] 赖剑煌，阮帮志，冯国灿，“频谱脸：一种基于小波变换和Fourier变换的人像识别新方法”，中国图像图形学报，(A版)，4(10)，811-817，1999：
[180] Jianhuang Lai, P.C. Yuen, G. C. Feng, "Face recognition using Holistic Fourier Invariant Features", Pattern Recognition, 34(1), 95-109, 2001;
[181] Jen-Tzung Chien, Chia-Chen Wu, "Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition," IEEE Transactions On Pattern Analysis And Machine Intelligence, VOL. 24, NO. 12, pp1644-1649, DECEMBER 2002;
[182] T. S. Lee. "Image Representation Using 2d Gabor Wavelets." IEEE Trans. Pattern Analysis and Machine Intelligence, 18(10): 959—971, 1996;
[183] J. G. Daugman, "Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters," J. Optical Soc. Amer., vol. 2, no. 7, pp. 1, 160-1,169, 1985.
[184] V. Krueger, "Gabor wavelet networks for object representation," DAGM Symposium, Kiel, Germany, 9, 13-15, 2000.
[185] Y. Freund and R. Schapire, "A short introduction to boosting," Journal of Japanese Society for Artificial Intelligence, vol. 14, no. 5, pp. 771-780, 1999.
[186] Paul Viola, M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple," CVPR-2001;
[187] Paul Viola, M. Jones, "Fast and Robust Classification using Asymmetric," NIPS-2001;
[2] Valentin, Abdi, O' Toole & Cottrell, "Connectionist Models of Face Processing: A Survey," Pattern Recognition, Vol. 27, 1994;
[3] Chellappa, Wilson & Sirohey, "Human and Machine Recognition of Faces: A Survey," Proc. IEEE, Vol. 83, 1995;
[4] Grudin, "On Internal Representations in Face Recognition Systems," Pattern Recognition, vol. 33, 2000;
[5] Zhao, Chellappa, Rosenfeld & Phillips, "Face Recognition: A Literature Survey," UMD CS-TR-4167, 2000;
[6] 周杰，陆春雨，张长水，李衍达，“人脸自动识别方法综述，”电子学报，No．4，2000：
[7] 张翠平，苏光大，“人脸识别技术综述，”中国图形图像学报，vol．5，2000：
[8] 山世光，“人脸识别中若干关键问题的研究”，中国科学院计算所博士学位论文，2004年5月。
[9] M. Turk, A. Pentland," Eigenfaces for Recognition," Journal of Cognitive Neurosicence, Vol. 3, No. 1, 1991, pp. 71-86;
[10] A. Pentland, B. Moghaddam, T. Starner," View-Based and Modular Eigenspaces for Face Recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 21-23 June 1994, Seattle, Washington, USA, pp. 84-91;
[11] H. Moon, P. J. Phillips," Computational and Performance aspects of PCA-based Face Recognition Algorithms", Perception, Vol. 30, 2001, pp. 303-321;
[12] M. S. Bartlett, J. R. Movellan, T. J. Sejnowski, "Face Recognition by Independent Component Analysis", IEEE Trans. On Neural Networks, Vol. 13, No. 6, November 2002, pp. 1450-1464;
[13] C. Liu, H. Wechsler, "Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition", Proc. Of the Second International Conference on Audio- and Video-based Biometric Person Authentication, AVBPA' 99, 22-24 March 1999, Washington D. C., USA, pp. 211-216;
[14] C. Liu, H. Wechsler, "A Unified Bayesian Framework for Face Recognition", Proc. Of the 1998 IEEE International Conference on Image Processing, ICIP98, 4-7 October 1998, Chicago, Illinois, USA, pp. 151-155;
[15] B. Moghaddam, C. Nastar, A. Pentland, "A Bayesian Similarity Measure for Deformable Image Matching," Image and Vision Computing, Vol. 19, Issue 5, May 2001, pp.235-244;
[16] M.H. Yang, " Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods, " Proc. Of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 20-21 May 2002, Washington D. C., USA, pp. 215-220;
[17] F.R. Bach, M.I. Jordan, "Kernel Independent Component Analysis, " Journal of Machine Learning Research, Vol.3, 2002, pp. 1-48;
[18] B. Scholkopf, A. Smola, K. R. Muller, "Nolinear Component Analysis as a Kernel Eigenvalue Problem," Technical Report No.44, December 1996, 18 pages;
[19] S. Zhou, R. Chellappa, B. Moghaddam, "Intra-personal kernel space for face recognition, " Proc. Of the 6~(th) International Conference on Automatic Face and Gesture Recognition, FGR2004, 17-19 May 2004, Seoul, Korea, pp.235-240;
[20] S. Zhou, R. Chellappa, "Multiple-exemplar discriminant analysis for face recognition, " Proc. Of the 17" International Conference on Pattern Recognition, ICPR04, 23-26 August 2004, Cambridge, UK, pp. 191-194;
[21] J. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, "Face Recognition Using Kernel Direct Discriminant Analysis Algorithms," IEEE Trans. On Neural Networks, Vol.14, No. 1, January 2003, pp. 117-126;
[22] J. Huang, B. Heisele, V. Blanz, "Component-based Face Recognition with 3D Morphable Models, " Proc. Of the 4" International Conference on Audio- and Video-Based Biometric Person Authentication, AVBPA 2003, 09-11 June 2003, Guildford, UK, pp. 27-34;
[23] V. Blanz, T. Vetter, "A Morphable Model for the Synthesis of 3D Faces, " Proc. Of the SIGGRAPH99, 08-13 August 1999, Los Angeles, USA, pp. 187-194;
[24] V. Blanz, T. Vetter, "Face Recognition Based on Fitting a 3D Morphable Model, " IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.25, NO. 9, September 2003, pp. 1063-1074;
[25] B. Moghaddam, J. H. Lee, H. Pf ister, R. Machiraju, "Model-Based 3D Face Capture with Shape-from-Silhouettes, " Proc. Of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, AMFG, 17 October 2003, Nice, France, pp. 20-27;
[26] A. Bronstein, M. Bronstein, R. Kimmel, and A. Spira, "3D Face recognition without facial surface reconstruction" , in Proceedings of ECCV 2004, Prague, Czech Republic, May 11-14, 2004;
[27] A. Bronstein, M. Bronstein, and R. Kimmel, "Expression-invariant 3D face recognition, " Proc. Audio & Video-Based Biometirc Person Authentication (AVBPA), Lecture Notes in Comp. Sicence 2688, Springer, 2003, pp. 62-69;
[28] G. Guo, S. Z. Li, K. Chan, "Face Recognition by Support Vector Machines," Proc. Of the IEEE International Confernece on Automatic Face and Gesture Recognition, 26-30 March 2000, Grenobel, France, pp. 196-201;
[29] B. Heisele, P. Ho, T. Poggio, "Face Recognition with Support Vector Machines: Globlal versus Component-based Approach, " Proc. Of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, Vol.2, 09-12 July 2001, Vancouver, Canada, pp.688-694;
[30] K. Jonsson, J. Matas, J. Kittler, Y. P. Li, "Learning Support Vectors for Face Verification and Recognition," Proc. Of the IEEE International Conference on Automatic Face and Gesture Recognition, 26-30 March 2000, Grenoble, France, pp. 208-213;
[31] Y. Freund, R. E. Schapire, "A Decision-Theoretic Generalization of 0n-Line Learning and an Application to Boosting, " Journal of Computer and System Sciences, Vol.55, No. 1, 1997, pp. 119-139;
[32] R. Meir, G. Raetsch, "An Introduction to Boosting and Leveraging" , in S. Mendelson and A. Smola, Editors, Advanced Lectures on Machine Learning, LNAI 2600, pp. 118-183, Springer, 2003;
[33] P. Viola, M. J. Jones, "Robust Real-Time Face Detection," International Journal of Computer Vision, Vol.57, No. 2, May 2004, pp. 137-154;
[34] J. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, S. Z. Li, "Ensemble-based Discriminant Learning with Boosting for Face Recognition, " IEEE Transactions on Neural Networks, Vol.17, No. 1, pp. 166-178, January 2006;
[35] P. Yang, S. Shan, W. Gao, Stan Li, D. Zhang, "Face Recognition Using Ada-Boosted Gabor Features, " Proceeding of the 6~(th) International Conference on Face and Gesture Recognition, Korea, May, 2004;
[36] Xiangsheng Huang, Stan Z. Li, Wang Yangsheng, "Jensen-Shannon Boosting for Object Recognition, " International Conference on Computer Vision and Pattern Recognition 2005;
[37] T. F. Cootes, K. Walker, C. J. Taylor, "View-Based Active Appearance Models, " Proc. Of the IEEE International Conference on Automatic Face and Gesture Recognition, 26-30 March 2000, Grenoble, France, pp. 227-232;
[38] T. F. Cootes, G. J. Edwards, C. J. Taylor, "Active Appearance Models," Proc. European Conf. Computer Vision, Vol.2, pp.484-498,1998;
[39] G.Edwards, T. Cootes, and C. Taylor, "Advances in Active Appearance Models, " Proc. International Conf. Computer Vision, pp.137-142, 1999;
[40] M. B. Stegmann, "Active Appearance Models, Theory, Extension and Cases," Master Thesis, Technical University of Denmark, 2000;
[41] X. W. Hou, S.Z. Li, H. J. Zhang, Q. S. Cheng, "Direct Appearance Models," In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Hawaii, December, 2001;
[42] S.Z. Li, S. C. Yan, H. J. Zhang, Q. S. Cheng, "Multi-View Face Alignment Using Direct Appearance Models," Proceedings of The 5~(th) International Conference on Automatic Face and Gesture Recognition, pp324-329, Washingtion D. C. , USA, 20-21 May 2002;
[43] S.C. Yan, C. Liu, S. Z. Li, H. J. Zhang, H. Shum, Q. S. Cheng, "Face Alignment Using Texture-Constrained Active Shape Models," Image and Vision Computing, pp. 69-75, Vol.21, No.1, 10 January 2003;
[44] Tong Wang, Haizhou Ai, Gaofeng Huang, "A Two-Stage Approach to Automatic Face Alignment," Proceedings of SPIE - The International Society for Optical Engineering, v5286, n2, 2003, p558-563;
[45] Yi Zhou, Lie Gu, Hongjiang Zhang, "Bayesian Tangent Shape Model: Estimating Shape and Pose Parameters via Bayesian Inference," Proceeding of the IEEE Conference on Computer Vision and pattern Recognition (CVPR 2003), Wisconsin, USA, June 16-22, 2003;
[46] K. Etemad, R. Chellappa, "Discriminant Analysis for Recognition of Human Faces Images," Journal of the Optical Society of America A, Vol.14, No. 8, August 1997, pp.1724-1733;
[47] P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection," Proc. Of the 4th European Conference on Computer Vision, ECCV96, 15-18 April 1996, Cambridge, UK, pp. 45-58;
[48] W. Zhao, R. Chellappa, A. Krishnaswamy, "Discriminant Analysis of Principal Components for Face Recognition," Proc. Of the 3rd IEEE International Conference on Face and Gesture Recognition, FG98, 14-16 April 1998, Nara, Japan, p. 336 ;
[49] A.M. Martinez, A.C. Kak, "PCA versus LDA," IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol.23, No. 2, 2001, pp.228-233;
[50] W. Zhao, A. Krishnaswamy, R. Chellappa, D. L. Swets, J. Weng, "Discriminant Analysis of Principal Components for Face Recognition, Face Recognition: From Theory to Applications," H. Wechsler, P. J. Phillips, V. Bruce, F. F. Soulie, and T.S. Huang, eds., Springer-Verlag, Berlin, 1998, pp. 73085;
[51] J. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, "Face Recognition Using LDA-Based Algorithms," IEEE Trans. On Neural Networks, Vol. 14, No. 1, January 2003, pp. 195-200;
[52] S. Zhou, V. Krueger, R. Chellappa, "Probabilistic recognition of human faces from video," Computer Vision and Image Understanding, Vol.91, 2003, pp. 214-245;
[53] S. Zhou, R. Chellappa, B. Moghaddam, "Visual tracking and recognition using appearance-adaptive models in particle filters," IEEE Trans. On Image Proceesing, Vol.13, No. 11, pp. 1491-1506;
[54] S. Zhou, R. Chellappa, "Probabilistic identity characterization for face recognition," Proc. Of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, 27 June-02 July 2004, Washington D. C., USA;
[55] Z. Biuk, S. Loncaric, "Face Recognition Rrom Multi-pose Image Sequence," Proc. Of the 2nd IEEE R8-EURASIP Symposium on Image and Signal Processing and Analysis, ISPA01, 19-21 June 2001, Pula, Croatia, pp. 319-324;
[56] K. C. Lee, J. Ho, M.H. Yang, D. Kriegman, "Video-Based Face Recognition Using Probabilistic Appearance Manifolds, " Proc. Of the IEEE Computer Society Conference on Computer Vision an d Pattern Recognition, CVPR 2003, Vol. 1, 16-22 June 2003, Madison, Wisconsin, USA, pp. 313-320;
[57] X. Liu, T. Chen, "Video-Based Face Recognition Using Adaptive Hidden Markov Models," Proc. Of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR2003, Vol.1, 16-22 June 2003, Madison, Wisconsin, USA, pp.340-345;
[58] G. Aggarwal, A. K. Roy-Chowdhury, R. Chellappa, "A System Identification Approach for Video-based Face Recognition," Proc. Of the International Conference on Pattern Recognition, 23-26 August 2004, Cambridge, UK;
[59] A.V. Nefian, M. H. Hayes III, "Hidden Markov Models for Face Recognition," Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP98, 12-15 May 1998, Seattle, Washington, USA, pp. 2721-2724;
[60] A.V. Nefian, M.H. Hayes, "Maximum likelihood training of the embedded HMM for face detection and recognition," Proc. Of the IEEE International Conference on Image Processing, ICIP 2000, Vol. 1, 10-13 September 2000, Vancouver, BC, Cannada, pp. 33-36;
[61] A. V. Nefian, "Embedded Bayesian networks for face recognition, " Proc. Of the IEEE International Conference on Multimedia and Expo. Vol. 2, 26—29 August 2002, Lusanne, Switzerland, pp.133-136;
[62] D.M. Blackburn, M. Bone, P. J. Phillips, "Facial Recognition Vendor Test 2002: Evaluation Report, " Feb. 2001;
[63] P. J. Phillips, P. Grother, R. J. Micheals, et al. "Face Recognition Vendor Test 2002: Overview and Summary, " March 2003;
[64] P. J. Phillips, P. Grother, R. J. Micheals, et al. "Face Recognition Vendor Test 2002: Evaluation Report," NISTIR 6965, March 2003;
[65] D.M. Blackburn, M. Bone, P. J. Phillips, "Facial Recognition Vendor Test 2000: Executive Overview," FRVT2000 Evaluation Report;
[66] D.M. Blackburn, M. Bone, P. J. Phillips, "Facia Recognition Vendor Test 2000: Evaluation Report, " Feb. 2001;
[67] P. J. Phillips, H. Moon, S. A. Rizvi, "The FERET evaluation methodology for face recognition algorithms," IEEE Trans. On PAMI, 2000, 22(10):1090-1104;
[68] P. J. Phillips, H. Wechsler, J. Huang, et al. "The FERET database and evaluation procedure for face recognition algorithms, " Image and Vision Computing Journal, 1998, 16(5):295-306;
[69] S.A. Rizvi, P.J. Phillips, H. Moon, "The FERET verification testing protocol for face recognition algorithms," Image and Vision Computing, 1998, 48-53;
[70] P. J. Phillips, P. J. Rauss, S. Z. Der, "FERET recognition algorithm development and test results, " ARL-TR-995, Oct. 1996;
[71] FERET. The facial recognition technology database. 1996;
[72] R. L. Hsu, M. Abdel-Mottaleb, A. K. Jain, "Face Detection in Color Images", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No. 5, pp. 696-706;
[73] R. Brunelli and T. Poggio, "HyperBF Networks for Real Object Recognition", Proc. IJCAI, pp. 1278-1284, Sydney, Australia, 1991.
[74] B. Manjunath, C. Shekhar, R. Chellappa, and C. vonder Malsburg, "A Robust Method for Detecting Image Features with Application to Face Recognition and Motion Correspondence," ICPR, B, pp. 208-212, 1992.
[75] S. Edelman, D. Reisfeld, and Y. Yeshurun," A System for Face Recognition that Learns from Examples," Proc. European Conf. Computer Vision, S. Sandini, ed., pp.787-791. SpringerVerlag, 1992;
[76] S.G. Shan, W. Gao, B. Cao, D. B. Zhao, "Illumination Normalization for Robust Face Recognition against Varying Lighting Conditions, " Proc. IEEE Int. Workshop on Analysis Modeling of Faces and Gestures (AMFG03), pp. 157-164, 2003;
[77] P. N. Belhumeur and D. W. Jacobs, "Comparing images under variable illumination," Proc. 1998 IEEE Conf. on Computer Vision and Patter Recognition (CVPR98), pp. 610-617, 1998;
[78] M. Savvides, B. V. Kumar and P. K. Khosla, "Eigenphase vs. Eigenfaces, " Proc. 17~(th) Int. Conf. Pattern Recognition (ICPR04), pp. 810-813, 2004;
[79] H. T. Wang, S. Z. Li, Y. S. Wang, "Face Recognition under Varying Lighting Conditions Using Self Quotient Image, " Proc. 6~(th) IEEE Int. Conf. on Automatic Face and Gesture Recognition (FGR04), pp. 819-824, 2004;
[80] H.T. Wang, S. Z. Li, Y.S. Wang, "Generalized Quotient Image, " Proc. 2004 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR04), Vol.2, pp. 498-505, 2004;
[81] Hallinan, "A Low-Dimensional Representation of Human Faces for Arbitrary Lighting Conditions" , Proc. Computer Vision and Pattern Recognition Conf. pp. 995-999, 1994;
[82] Ravi Ramamoorthi, Pat Hanrahan, "On the relationship between radiance and irradiance: determining the illumination for images of a convex Lambertian object" , J. Opt. Soc. Am., Vol.18, No.10, 2001;
[83] Ravi Ramamoorthi, "Analytic PCA Construction for Theoretical Analysis of Lighting Variability in Images of a Lambertian Object", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No.10, 2002-10-21;
[84] [84] Ronen Brasri, David Jacobs, "Lambertian Reflectance and Linear Subspaces" , NEC Research Institute Technical Report 2002-172R;
[85] F. X. Sillion, J. Arvo, S. H. Westin, and D. Greenberg. A global illumination solution for general re ectance distributions. In SIGGRAPH 91, ppl87-196, 1991;
[86] S. K. Nayar and H. Murase, "Dimensionality of Illumination Manifolds in Eigenspace" , Technical Report CUCS-021-94, August 1994;
[87] B. Cabral, N. Max, R. Springmeyer. "Bidirectional Reflection Functions from Surface Bump Maps" , Comp. Graph., 21(4), 1987;
[88] Amnon Shashua, and Tammy Riklin-Raviv, "The quotient image: Class-based re-rendering and recognition with varying illuminations", Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, 2001;
[89] T. Riklin-Raviv and A. Shashua, "The Quotient image: Class-based recognition and sysnthesis under varying illumination" . In Proceedings of the 1999 Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, 1999;
[90] Amnon Shashua, "Geometry and Photometry in 3D Visual Recognition," Ph.D. Thesis, MIT, Nov,1992;
[91] P. N. Belhumeur, David J. Kriegman," What is the set of Images of an Object Under All possible Lighting Conditions?", IEEE conf. On Computer Vision and Pattern Recognition, 1996;
[92] Athinodoros S. Georghiades and Peter N. Belhumeur, "Illumination cone models for recognition under variable lighting: Faces" , CVPR, 1998;
[93] T. Sim, T. Kanade, "Combining Models and Exemplars for Face Recognition: An Illumination Example," Proc. Workshop on Models versus Exemplars in Computer Vision with CVPR01, 2001;
[94] W. Y. Zhao and R. Chellappa, "Illumination-insensitive Face Recognition using Symmetric Shape-from-Shading," Proc. 2000 IEEE Conf. on Computer Vision and Pattern Recognition (CVPROO), Vol.1, pp.286-293, 2000;
[95] W. Y. Zhao, R. Chellappa, "SFS Based View Synthesis for Robust Face Recognition," Proc. 4~(th) Int. Conf. on Face and Gesture Recognition (FGROO), pp.285-292, 2000;
[96] Yael Adnin, Yael Moses and Shimon Ullman, "Face recognition: The problem of compensating fro changes in illumination direction", IEEE Transactions on Pattern Analysis and Machine intelligence, Vol. 19, No.7, 1997, pp 712-732.
[97] W. Zhao, R. Chellappa, "Robust face recognition using symmetric shape-from-shading" Technical report, Center for Automation Research, University of Maryland, 1999.
[98] Face Recognition Vendor Test (FRVT) 2002, Jonathon Phillips etc.
[99] T. Riklin-Raviv, A. Shashua: The Quotient image:class-based re-rendering and recognition with varying illumination conditions. In: IEEE PAMI. (2001) 10.
[100]Georghiades, A., Kriegman, D., Belhumeur, P.: From few to many: Generative models for recognition under variable pose and illumination. IEEE PAMI (2001)10, 14.
[101]Ravi Ramamoorthi, Pat Hanrahan, "On the relationship between radiance and irradiance: determining the illumination from images of a convex Labertianobject", J. Opt. Soc. Am. Vol. 18, No. 10, 2001.
[102][102]Land, E., McCann, J., "Lightness and retinex theory," Journal of the Optical Society of America 61 (1971) ;
[103]Jobson, D., Rahman, Z., Woodell, G.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. On Image Processing 6 (1997);
[104]H. Wang, S. Z. Li, and Y. Wang, "Generalized Quotient Image", IEEE International Conference on Computer Vision and Pattern Recognition, 2004
[105]B. K, P. Horn, "Robot Vision", MIT Press, 1986;
[106]Ralph Gross, Vladimir Brajovic, "An Image Preprocessing Algorithm for Illumination Invariant Face Recognition", 4th International Conference on Audio-and Video Based Biometric Person Authentication(AVBPA), 2003
[107]J. Tumblin, G. Turk, "LCIS: A boundary hierarchy for detail-preserving contrast reduction," In: ACM SIGGRAPH. (1999);
[108]L. Rudin, S. Osher, and E. Fatemi, "Nolinear total variation based noise removal algorithms" Physica D, vol. 60, pp. 259-268, 1992;
[109] T. F. Chan, and S. Esedoglu, "Aspects of Total Variation Regularized L1 Function Approximation" UCLA CAM Report 04-07, Feb, 2004;
[110]T. Sim, S. Baker, and M. Bsat, " The CMU Pose, Illumination, and Expression (PIE) Database", Processing of the IEEE International Conference on Automatic Face and Gesture Recognition. May 2002;
[111]B. Moghaddam and A. Pentland, "Probabilistic Visual Learning for Object representation," IEEE Trans. On PAMI, vol.20, no. 7, pp696-710, July, 1997;
[112]B. Moghaddam, T. Jebara, A. Pentland, "Bayesian Face Recognition," Pattern Recognition Vol.33 (2000), ppl771-1782, 2000;
[113]E. Bailly-Bailliere, S. Bengio, F. Bimbot, etc. "The BANCA database and evaluation protocol," In Audio- and Video-Based Biometric Person Authentication (AVBPA), pages 625-638, 2003;
[114]Wen Gao, Bo Cao, Shiguang Shan, etc. "The CAS-PEAL Large-Scale Chinese Face Database and Evaluation Protocols," Technical Report No. JDL_TR_04_FR_001, Joint Research U Development Laboratory, CAS, 2004;
[115] A. R. Martinez and R. Benavente, "The AR face database," Technical Report 24, Computer Vision Center (CVC) Technical Report, Barcelona, Spain, June 1998;
[116] F. S. Samaria, and A. C. Harter, "Parameterisation of a Stochastic Model for Human Face Identification, " Proceedings of the 2~(nd) IEEE Workshop on Applications of Computer Vision, December 1994.
[117] K. Messer, J. Matas, J. Kittler, etc. "XM2VTSDB: The extended M2VTS database," In Second International Conference on Audio- and Video-Based Biometric PersonAuthentication, March 1999;
[118]M. Turk, A. Pentland, "Eigen-faces for Recognition," Jounal of cognitive neuroscience, 3(1), pp 71-86, 1991;
[119]S, Z. Li, A. K. Jain, "Handbook of Face Recognition," Springer Verlag, New York. ISBN# 0-387-40595-x, 2004;
[120]Hyoja-Dong, and Nam-Gu, "Asian Face Image Database PF01," Pohang University of Science and Technology;
[121]B. —W. Hwang, H. Byun, M.-C. Roh, and S.-W. Lee, "Performance evaluation of face recognition algorithms on the asian face database, KFDB," In Audio- and Video-Based Biometric Person Authentication (AVBPA), pages 557-565, 2003;
[122]K. Messer, K., J. Kittler, M. Sadeghi, et al. Face Authentication Test on the BANCA Database. Proc. the 17th International Conference on Pattern Recognition (ICPR04), pp. 523-532, 2004.
[123]C.Liu. "Statistical and evolutionary approaches for face recognition," George Mason University, Ph.d Dissertation, 1999.
[124]L. F. Chen, H. M. Liao, J. C. Lin, M. T. Ko, and G. J. Yu. "A New LDA-based Face Recognition System Which Can Solve the Small Sample Size Problem," Pattern Recognition, vol. 33, no. 10, pp. 1713-1726, 2000.
[125]C. Liu and H. Wechsler, "A Shape and Texture Based Enhanced Fisher classifier for Face Recognition," IEEE Trans. on Image Processing, vol. 10, no. 4, pp. 598-608, 2001.
[126]C. Liu, H. Wechsler, "Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition," IEEE Trans. Image Processing, vol. 11, no. 4 pp. 467-476, 2002;
[127]H. Yu and J. Yang, "A Direct LDA Algorithm for High-dimensional Data - with Application to Face Recognition," Pattern Recognition, Vol. 34, pp 2067-2070, 2001.
[128]M. H. Yang, N. Ahuja, D. Kriegman Face Recognition Using Kernel Eigenfaces, Int. Conf. on Image Processing, 2000, vol.1, pp. 37-40;
[129]Q. S. Liu, R.Huang, H. Q. Lu and S. D. Ma, "Face Recognition Using Kernel Based Fisher Discriminant Analysis" , Proc. Int'l Conf. Automatic Face and Gesture Recognition, pp. 197-201, 2002.
[130] M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, etc., "Distortion Invariant Object Recognition in the Dynamic Link Architecture," IEEE Trans. On Computers, 42(3), pp300-311, 1993;
[131]L. Wiskott, J. M. Fellous, N. Kruger, C. v. d. Malsburg, "Face Recognition by Elastic Bunch Graph Matching," IEEE Trans. On PAMI, Vol. 19, No. 7, pp775-779, 1997;
[132] C. L. Rotropoulos, A. Tefas, I. Pitas, "Frontal Face Authentication Using Discriminating Grids with Morphological Feature Vectors," IEEE Trans. On Multimedia, Vol. 2, No. 1, pp14-26 March, 2000;
[133] A. Tefas, C. Kotropoulos, and I. Pitas, Using Support Vector Machines to Enhance the Performance of Elastic Graph Matching for Frontal Face Authentication, IEEE Transactions On Pattern Analysis And Machine Intelligence, VOL. 23, NO. 7, pp735-746, JULY 2001.
[134] 丁嵘，苏光大，林行刚，“使用关键点信息改进弹性匹配人脸识别算法，”电子学报，Vol．30，No．9：1292-1294，2002：
[135] B. Moghaddam, W. Wahid, and A. Pentland, "Beyond eigenface: probabilistic matching for face recognition," Proc. 3rd IEEE Int. Conf. on Automatic Face and Gesture Recognition (FGR9B), pp. 30-35, April, 1998.
[136] B. Moghaddam. "Principal Manifolds and Probabilistic Subspaces for Visual Recognition," IEEE Trans. on PAMI, Vol. 24, No. 6, pp. 780-788, June 2002.
[137] Athinodoros S. Georghiades, Peter N. Belhumeur, David J. Kriegman, "From Few to Many: Illumination Cone Models for Face Recognition Under Variable Lighting and Pose," IEEE Trans. on PAMI, 23(6) pp. 643660-139, 2001. 6
[138] P. N. Bellhumer, J. Hespanha, and D. Kriegman. "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Face Recognition, 17(7): 711—720, 1997
[139] L. Denes, P. Metes, and Y. Liu, "Hyperspectral face database," Technical report, Robotics Institute, Carnegie Mellon University, 2002;
[140] D. Socolinsky, L. Wolff, J. Neuheisel, and C. Eveland, "Illumination invariant face recognition using thermal infrared imagery," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2001;
[141] P. Flynn, K. Bowyer, and P. J. Phillips, "Assesment of time dependency in face recognition: an initial study," In Audio- and Video-Based Biometric Person Authentication (AVBPA), pp. 44-51, 2003;
[142] A. O' Vooole, J. Harms, S. Snow, D. R. nurst, M. Pappas, and H. Abdi, "A video database of moving faces and people," submitted, 2003;
[143] D. Graham and N, Allison, "Characterizing virtual eigensignatures for general purpose face recognition," In H. Wechsler, P. J. Phillips, V. Bruce, F. Fogelman-Soulie, and T. Huang, editors, Face Recognition: From Theory to Applications," NATO ASI Series F. pages 446-456, 1998;
[144] NIST databse, http://www.nistgov/srd/nistsd18.htm;
[145] R. Brunelli, T. Poggio, Face recognition: features vs. templates. IEEE Trans. on PAMI, 15(10): 1042-1052, 1993;
[146] FRGC, http://www.frvt.org/FRGC/;
[147] T. Sim and Y. Kanade, "Illuminating the Face," Technology report CMU-RI-TR-01-31, Robotics Institute, Carnegie Mellon University, September, 2001;
[148] G. D. Finlayson, S. D. Hordley, M. S. Drew, "Removing Shadows from Images Using Retinex[A]," Proceedings of IS&T/ SID Tenth Color Imaging Conference: Color Science, Systems andApplications [C]. Scottsdale, Arizona: IS&T/SID, 2002. 73-79.
[149] Edwin H Land, "Recent advances in retinex theory[J], " Vision Research, 1986, 26(1): 7-21;
[150] D. J. Jobson, Z. Rahman, G. A. Woodell, "Properties and performance of a center/surround retinex[J], " IEEE Trans. on image processing, 1997, 6(3): 451-462.
[151] 唐亮，谢维信，黄建军，肖志级，“城市航空影像中基于Retinex的阴影消除”，电子学报，Vol．33，No．3，Mar．2005：
[152] 熊志勇等，基于本征特征的弹性模板匹配法，计算机研究与发展，1999．7第7期：
[153] 丁嵘等，使用部件信息改进弹性匹配人脸识别，光电子·激光，2002．8 Vol．13；
[154] N. Kruger, "An algorithm for the learning of weights in discrimination functions usingapriori constraints [J]," IEEE Trans. on PAMI, 1997, 19: 764-768;
[155] Wurtz R P. Object recognition robust under translations, deformation, and changes in background[J]. IEEE Trans on PAMI, 1997, 19: 769-774;
[156] M. Kass, A. Witkin, andD. Terzopoulos. "Snakes: Active contour models," Int. Journal of Computer Vision, pp. 321-331,1988;
[157] A. L. Yuille, "Deformable templates for face detection," J. Cogn. neurosci. 3, 59-70, 1991.
[158] A. L. Yuille, P. W. Hallinan, D. S. Cohen, "Feature extraction from faces using deformable templates," International Journal of Computer Vision, 8, 99-111. 1992;
[159] T. F. Cootes, C. J. Taylor, et al. Active Shape Models——Their Training and Application. Computer Vision and Image Understanding, January 1995, Vol. 61, No. 1, PP. 38-59;
[160] T. F. Cootes, C. J. Taylor, et al. Training Models of Shape from Sets of Examples. In Prec. British Machine Vision Conference. Springer-Verlag, 1992, pp550-557;
[161] T. F. Cootes and C. J. Taylor. Statistical Models of Appearance for Computer Vision. Technical Report, Imaging Science and Biomedical Engineeing, University of Manchester, 2000;
[162] L. Wiskott andC. v. d. Malsburg, "Face Recognition by Dynamic Link Matching". Internal Report IROINI 96-05, Institut for Neuroinformatik, Ruhr-University Bochum, 1996.
[163] L. Wiskott, J. M. Fellous, N. Kruger and C. v. d. Malsburg, "Face Recognition and Gender Determination", Prec. Int' 1 Workshop on Automatic Face and Gesture Recognition, pp. 92-97, 1995;
[164] 陶亮，庄镇泉。复杂背景下自动人眼定位。计算机辅助设计与图形学学报，2003．1 Vol．15；
[165] C Y. Xu, and J. L. Prince, "Snakes, Shapes, and Gradient Vector Flow", IEEE Transactions on Image Processing, Vol. 7, No. 3, March 1998;
[166] G. Hamarneh, T. Gustavsson, "Statistically Constrained Snake Deformations", Image Analysis Group, Chalmers University of Technology, Sweden, 2000, IEEE;
[167] Peter Kovesi, "Image Features From Phase Congruency," Videre. Volume 1, Number 3, summer 1999;
[168] Peter Kovesi, "Image Features from Phase Congruency[R]," Technical Report 95/4, University of Western Australia, Robotics and Vision Group, 1995.
[169] D. J. Field, "Relation between the Statistics of Natural Images and the Response Properties of Cortical Ceils," JOSA A, 4(12): 2379-2394, December 1987.
[170] T. Stockam, "Image processing in the context of a visual model," Proceedings of the IEEE 60 (1972) 828-848.
[171] Jianke Zhu, Mang I Yai and Peng Un Mak, "Face Recognition Using 2D DCT with PCA", in The 4nd Chinese Conference on Biometric Recognition (Sinobiometrics' 03) at Beijing, P. R. China, Dec. 7-8, 2003;
[172] R. Brunelli and T. Poggio, Face Recognition through Geometrical Features, Proceedings of ECCV '92, pp792-800;
[173] B. S. Manjunath, R. Chellappa, C.v.d. Malsburg, "A Feature based approach to face recognition," in Pro. IEEE Computer Soc. Con. On Computer Vision and Pattern Recognition, 1992, pp373-378
[174] Z. Hong, "Algebraic feature extraction of image for recognition," Pattern Recognition, 24(3), pp211-219, 1991;
[175] 洪子泉，杨静宇．用于图像识别的图像代数特征抽取．自动化学报，1992，18(2)： 233～237．
[176] 洪子泉，杨静宇．基于奇异值特征和统计模型的人像识别算法．计算机研究与发展，1994，31(3)：60～65．
[177] 黄修武，基于代数方法的人脸图像特征提取与识别，南京理工大学博士论文，1998；
[178] P. Penev and J. Atick, "Local Feature Analysis: A General Statistical Theory for Object Representation," Network: Computation in Neural Systems, vol. 7, pp. 477-500, 1996;
[178] Jianhuang Lai, P. C. Yuen, G. C. Feng, "Spectroface: A Fourier-based Approach for Human Face Recognition," ICMI, Vol. 2, 115-120, 1999;
[179] 赖剑煌，阮帮志，冯国灿，“频谱脸：一种基于小波变换和Fourier变换的人像识别新方法”，中国图像图形学报，(A版)，4(10)，811-817，1999：
[180] Jianhuang Lai, P.C. Yuen, G. C. Feng, "Face recognition using Holistic Fourier Invariant Features", Pattern Recognition, 34(1), 95-109, 2001;
[181] Jen-Tzung Chien, Chia-Chen Wu, "Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition," IEEE Transactions On Pattern Analysis And Machine Intelligence, VOL. 24, NO. 12, pp1644-1649, DECEMBER 2002;
[182] T. S. Lee. "Image Representation Using 2d Gabor Wavelets." IEEE Trans. Pattern Analysis and Machine Intelligence, 18(10): 959—971, 1996;
[183] J. G. Daugman, "Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters," J. Optical Soc. Amer., vol. 2, no. 7, pp. 1, 160-1,169, 1985.
[184] V. Krueger, "Gabor wavelet networks for object representation," DAGM Symposium, Kiel, Germany, 9, 13-15, 2000.
[185] Y. Freund and R. Schapire, "A short introduction to boosting," Journal of Japanese Society for Artificial Intelligence, vol. 14, no. 5, pp. 771-780, 1999.
[186] Paul Viola, M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple," CVPR-2001;
[187] Paul Viola, M. Jones, "Fast and Robust Classification using Asymmetric," NIPS-2001;
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