光照变化条件下人脸识别技术研究
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摘要
自动人脸识别(AFR)是生物识别领域的研究热点,它涉及到图像处理、模式识别、机器学习和计算机视觉等多个学科的研究内容。人脸识别技术因其具有良好的非接触、非打扰的优势在身份鉴定、门禁系统、监控、法律实施和人机交互等方面有着广泛的应用前景。经过多年的发展,研究者已经提出了很多性能卓越的人脸识别算法,也出现了一些成功的商用系统。但是当人脸图像出现光照、姿态、表情、年龄的变化、遮挡问题或少样本问题的时候,识别率会急剧下降,特别是光照的变化对识别率的影响最大。设计出对光照变化具有高识别率和高鲁棒性的人脸识别算法是人脸识别领域中的一个难点和热点。
本论文针对人脸识别技术中的光照变化问题进行了研究,重点研究在光照变化情况下的人脸特征提取方法。论文的主要研究工作包括以下几个方面:
1.提出了基于Wavelet多尺度LBP/LTP人脸特征提取算法。局部二值模式(LBP)算子具有良好的灰度不变性、旋转不变性和尺度不变性,能够适应人脸图像光照的变化。局部三值模式(LTP)算子在LBP基础上发展而来,在保持了LBP优良的性质之外,还具有抗噪性能,能够更加精细的表达图像的纹理特征。本文利用Wavelet多分辨分析的思想构建了一个人脸图像的塔式结构,在不改变LBP/LTP算子的前提下可以使提取出的人脸特征不仅包含了局部信息,还包含了人脸图像的全局信息,提高了人脸特征的分辨能力。
2.对双树复小波变换(DTCWT)的各子带系数在人脸识别中的重要性进行了研究,同时还研究了DTCWT高频系数的分布情况,并根据分布情况提出了基于系数分割方法的DTCWT边缘增强人脸识别算法。双树复小波变换(DTCWT)具有多分辨和多方向能力,通过提取6个方向的高频系数作为人脸的特征可以去除部分的光照影响。而且DTCWT可以采用传统的离散小波来实现,结构简单,运算速度快,同Gabor变换相比具有明显的优势。本文提出的DTCWT边缘增强算法能够很好地增强人脸图像的边缘信息,提高了人脸的分类能力。
3.在辐照度模型的基础上提出了基于DTCWT的光照不变特征提取方法。辐照度模型是朗伯光照模型的泛化。它没有将光照的影响进行精细的划分,因而分析简单,常常被研究者用于二维人脸识别技术中来处理人脸图像的光照问题。辐照度模型存在一个通用的假设:人脸图像中受光照影响的部分属于图像的低频;而表现图像的纹理特征的部分属于图像的高频部分。本文就是基于这一假设,使用阈值收缩去噪的思想来获得光照不变的纹理特征。实验结果表明该方法提取出的光照不变特征能够滤除大部分的光照影响。
4.提出了基于非下采样Contourlet变换(NSCT)的人脸光照不变特征提取方法。NSCT具有多分辨、多方向和各向异性的优点,具有更强的图像纹理表达能力,提取出的光照不变特征带有更多的信息量,图像的熵更高。本文还结合全变差(TV)模型提出了一种NSCT特征和TV特征融合的人脸识别算法,将算法的识别能力进一步提升。最后从朗伯光照模型出发,从理论上解释了基于DTCWT方法和NSCT方法只能处理部分光照的影响的原因。
Automatic Face Recognition (AFR) is an active topic in Biometric Technology, which involves a surprising number of scientific disciplines, such as image processing, pattern recognition, machine learning and computer vision. Due to its advantage of being natural and non-intrusive, face recognition technology have a wide range of applications in identification, access control systems, surveillance, law enforcement and human-computer interaction. Lots of face recognition algorithms have being proposed. And some successful commercial systems also emerged. Current systems perform well under relatively controlled environments but tend to suffer when variations in different factors (such as pose, illumination, expression etc.) are present. Especially large illumination variations would seriously affect face recognition algorithms. It is still a great challenge to build high performance and robust algorithms for face recognition. At present, recognition under illumination variations is an active topic and difficult task in the field of face recognition technology.
The face recognition algorithms under various lighting conditions are studied. And the focus of our work is extracting face features under illumination variations. The main work and innovation of this dissertation include the following aspects:
1. An algorithm of facial features extraction based on multi-scale LBP/LTP is proposed. Local binary pattern (LBP) operator has good properties of gray-scale invariance, rotation invariance and scale invariance. And it is robust to illumination variations in face images. Local ternary patterns (LTP) which develops from the LBP operator maintains the good properties of LBP as well as noise immunity. A tower structure of face images is produced using the idea of Wavelet multiresolution analyse. The facial features extracted by this way contain the local and global information of face image, while with no need for changing the LBP/LTP operator’s parameters. This structure can enhance the identification ability of facial features.
2. The contribution of each sub-band coefficients of dual-tree complex wavelet transform (DTCWT) to the face recognition performance is studied. And an edge enhancement face recognition algorithm based on DTCWT’s coefficient distribution is proposed. Due to DTCWT’s multi-resolution and multi-directional properties, the facial features extracted from the high-frequency coefficients in 6 directions can partly remove the effect of illumination. Furthermore DTCWT can be implemented by traditional discrete wavelet transformation, which has simple structure and fast speed. The proposed DTCWT edge enhancement algorithm have better recognition rate and higher computation speed than Gabor-based face recognition technology.
3. A novel method of exacting illumination invariant features based on DTCWT is proposed using irradiance lighting model. The irradiance lighting model is the generalization of Lambertian illumination model, which is often used in 2D face recognition technology to deal with face illumination because of its simpleness. There is a common assumption in irradiance model: the illumination in face image is regarded as the low frequency of images, while the texture features of the image belong to the high frequency part. Based on this assumption, the illumination invariant texture feature can be extracted from the face image with the idea of threshold shrinkage denoising. Experiment results show that illumination invariant features extracted by this method can filter out most of the illumination effects.
4. A method of facial feature exaction based on nonsubsampled Contourlet transform (NSCT) is proposed. NSCT has better performance to express the image texture than wavelet with superiority of multi-resolution, multi-direction and anisotropy. The extracted illumination invariant features have more information and higher entropy. A new recognition algorithm fusing the NSCT’s features with TV’s features is proposed, which has better performance. Finally, the reason why the illumination invarian features extracted by DTCWT and NSCT can only deal with part of the illumination is explained using Lambertian illumination model.
本论文针对人脸识别技术中的光照变化问题进行了研究,重点研究在光照变化情况下的人脸特征提取方法。论文的主要研究工作包括以下几个方面:
1.提出了基于Wavelet多尺度LBP/LTP人脸特征提取算法。局部二值模式(LBP)算子具有良好的灰度不变性、旋转不变性和尺度不变性,能够适应人脸图像光照的变化。局部三值模式(LTP)算子在LBP基础上发展而来,在保持了LBP优良的性质之外,还具有抗噪性能,能够更加精细的表达图像的纹理特征。本文利用Wavelet多分辨分析的思想构建了一个人脸图像的塔式结构,在不改变LBP/LTP算子的前提下可以使提取出的人脸特征不仅包含了局部信息,还包含了人脸图像的全局信息,提高了人脸特征的分辨能力。
2.对双树复小波变换(DTCWT)的各子带系数在人脸识别中的重要性进行了研究,同时还研究了DTCWT高频系数的分布情况,并根据分布情况提出了基于系数分割方法的DTCWT边缘增强人脸识别算法。双树复小波变换(DTCWT)具有多分辨和多方向能力,通过提取6个方向的高频系数作为人脸的特征可以去除部分的光照影响。而且DTCWT可以采用传统的离散小波来实现,结构简单,运算速度快,同Gabor变换相比具有明显的优势。本文提出的DTCWT边缘增强算法能够很好地增强人脸图像的边缘信息,提高了人脸的分类能力。
3.在辐照度模型的基础上提出了基于DTCWT的光照不变特征提取方法。辐照度模型是朗伯光照模型的泛化。它没有将光照的影响进行精细的划分,因而分析简单,常常被研究者用于二维人脸识别技术中来处理人脸图像的光照问题。辐照度模型存在一个通用的假设:人脸图像中受光照影响的部分属于图像的低频;而表现图像的纹理特征的部分属于图像的高频部分。本文就是基于这一假设,使用阈值收缩去噪的思想来获得光照不变的纹理特征。实验结果表明该方法提取出的光照不变特征能够滤除大部分的光照影响。
4.提出了基于非下采样Contourlet变换(NSCT)的人脸光照不变特征提取方法。NSCT具有多分辨、多方向和各向异性的优点,具有更强的图像纹理表达能力,提取出的光照不变特征带有更多的信息量,图像的熵更高。本文还结合全变差(TV)模型提出了一种NSCT特征和TV特征融合的人脸识别算法,将算法的识别能力进一步提升。最后从朗伯光照模型出发,从理论上解释了基于DTCWT方法和NSCT方法只能处理部分光照的影响的原因。
Automatic Face Recognition (AFR) is an active topic in Biometric Technology, which involves a surprising number of scientific disciplines, such as image processing, pattern recognition, machine learning and computer vision. Due to its advantage of being natural and non-intrusive, face recognition technology have a wide range of applications in identification, access control systems, surveillance, law enforcement and human-computer interaction. Lots of face recognition algorithms have being proposed. And some successful commercial systems also emerged. Current systems perform well under relatively controlled environments but tend to suffer when variations in different factors (such as pose, illumination, expression etc.) are present. Especially large illumination variations would seriously affect face recognition algorithms. It is still a great challenge to build high performance and robust algorithms for face recognition. At present, recognition under illumination variations is an active topic and difficult task in the field of face recognition technology.
The face recognition algorithms under various lighting conditions are studied. And the focus of our work is extracting face features under illumination variations. The main work and innovation of this dissertation include the following aspects:
1. An algorithm of facial features extraction based on multi-scale LBP/LTP is proposed. Local binary pattern (LBP) operator has good properties of gray-scale invariance, rotation invariance and scale invariance. And it is robust to illumination variations in face images. Local ternary patterns (LTP) which develops from the LBP operator maintains the good properties of LBP as well as noise immunity. A tower structure of face images is produced using the idea of Wavelet multiresolution analyse. The facial features extracted by this way contain the local and global information of face image, while with no need for changing the LBP/LTP operator’s parameters. This structure can enhance the identification ability of facial features.
2. The contribution of each sub-band coefficients of dual-tree complex wavelet transform (DTCWT) to the face recognition performance is studied. And an edge enhancement face recognition algorithm based on DTCWT’s coefficient distribution is proposed. Due to DTCWT’s multi-resolution and multi-directional properties, the facial features extracted from the high-frequency coefficients in 6 directions can partly remove the effect of illumination. Furthermore DTCWT can be implemented by traditional discrete wavelet transformation, which has simple structure and fast speed. The proposed DTCWT edge enhancement algorithm have better recognition rate and higher computation speed than Gabor-based face recognition technology.
3. A novel method of exacting illumination invariant features based on DTCWT is proposed using irradiance lighting model. The irradiance lighting model is the generalization of Lambertian illumination model, which is often used in 2D face recognition technology to deal with face illumination because of its simpleness. There is a common assumption in irradiance model: the illumination in face image is regarded as the low frequency of images, while the texture features of the image belong to the high frequency part. Based on this assumption, the illumination invariant texture feature can be extracted from the face image with the idea of threshold shrinkage denoising. Experiment results show that illumination invariant features extracted by this method can filter out most of the illumination effects.
4. A method of facial feature exaction based on nonsubsampled Contourlet transform (NSCT) is proposed. NSCT has better performance to express the image texture than wavelet with superiority of multi-resolution, multi-direction and anisotropy. The extracted illumination invariant features have more information and higher entropy. A new recognition algorithm fusing the NSCT’s features with TV’s features is proposed, which has better performance. Finally, the reason why the illumination invarian features extracted by DTCWT and NSCT can only deal with part of the illumination is explained using Lambertian illumination model.
引文
[1] Elliott S J, Massie S A, Sutton M J. The Perception of Biometric Technology: A Survey[C]. Proceedings of IEEE Workshop on Automatic Identification Advanced Technologies, 2007: 259-264.
[2]孙冬梅,裘正定.生物特征识别技术综述[J].电子学报, 2001, 29(12): 1744-1748.
[3] Jain A, Lin Hong, Bolle R. On-line fingerprint verification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(4): 302-314.
[4] Yue Feng, Zuo Wang-Meng, Zhang Da-Peng. Survey of Palmprint Recognition Algorithms[J]. Acta Automatica Sinica, 2010, 36(3): 353-365.
[5] Wildes R P. Iris recognition: an emerging biometric technology[J]. Proceedings of the IEEE, 1997, 85(9): 1348-1363.
[6] Li Ma, Tieniu Tan, Yunhong Wang, et al. Efficient iris recognition by characterizing key local variations[J]. IEEE Transactions on Image Processing, 2004, 13(6): 739-750.
[7] Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.
[8] Jain A K, Duin R P W, Jianchang Mao. Statistical pattern recognition: a review[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(1): 4-37.
[9] Wu J, Zhou Z H. Face recognition with one training image per person[J]. Pattern Recognition Letters, 2002, 23(14): 1711-1719.
[10] Gross R, Matthews I, Baker S. Appearance-based face recognition and light-fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(4): 449-465.
[11]孟明,吴仲城,余永,等.基于笔段特征和HMM的在线签名认证方法研究[J].模式识别与人工智能, 2007, 20(1): 95-100.
[12] Vélez J, Sánchezá, Moreno B, et al. Fuzzy shape-memory snakes for the automatic off- line signature verification problem[J].Fuzzy Sets and Systems,2009,160(2): 182-197.
[13] Adriani M, Maeder P, Meuli R, et al. Sound recognition and localization in man: specialized cortical networks and effects of acute circumscribed lesions[J]. Experimental Brain Research, 2003, 153(4): 591-604.
[14] Zeng J, Zhu S F, Yan H. Towards accurate human promoter recognition: a review of currently used sequence features and classification methods[J]. Briefings in BioinformaticS, 2008, 25(8): 498-508.
[15] Raghavendra R, Dorizzi B, Rao A, et al. Designing efficient fusion schemes for multimodal biometric systems using face and palmprint[J]. Pattern Recognition, 2011, 44(5): 1076-1088.
[16] Face Recognition Vendor Tests[EB/OL]. http://www.frvt.org.
[17] Phillips P J, Scruggs W T, O’Toole A J, et al. FRVT 2006 and ICE 2006 Large-Scale Experimental Results[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(5): 831-846.
[18] Zhao, W; Chellappa, R; Phillips, PJ, et al. Face recognition: A literature survey[J]. ACM Computing Surveys, 2003, 35(4): 399-459.
[19]孙粤辉.基于DT-CWT特征的人脸检测与识别研究[D].广州:华南理工大学, 2007.
[20]周杰,卢春雨,张长水等.人脸自动识别方法综述[J].电子学报, 2000, 28(4): 102-106.
[21]章毓晋,程正东,贾慧星.基于子空间的人脸识别[M].北京:清华大学出版社, 2009.
[22]王映辉.人脸识别:原理、方法与技术[M].北京:科学出版社, 2010.
[23] Turk M A, Pentland A P. Face recognition using eigenfaces[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991: 586-591.
[24] Oenev P, Atick J. Local Feature Analysis: A General Statistical Theory for Object Reperesentation[J]. Network: Computation in Neural System, 1996, 7: 477-500.
[25] Lanitis A, Taylor C J, Cootes T F. Automatic interpretation and coding of face images using flexible models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 743-756.
[26] Wiskott L, Fellous J-M, Kuiger N, et al. Face recognition by elastic bunch graph matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 775-779.
[27] Phillips P J, Rauss P J, Der S Z. FERET recognition algorithm development and test results[R]. Technical Report ARL-TR-995, US Army Research Laboratory, 1996.
[28] Kohonen T. Self-organization and associative memory[M]. New York, US:Springer-Verlag New York, 1989.
[29] Ranganath S., Arun K. Face recognition using transform features and neural networks[J]. Pattern Recognition, 1997, 30(10): 1615-1622.
[30] Yang M-H, Ahuja N, Kriegman D. Face recognition using kernel eigenfaces[C]. Proceedings of International Conference on Image Processing, 2000, 1: 37-40.
[31] Yang Ming-Hsuan. Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods[C]. Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002: 215-220.
[32] Liu C, Wechsler H. A Unified Bayesian Framework for Face Recognition[C]. Proceedings of International Conference on Image Processing, 1998, 1: 151-155.
[33] Wang X., Tang X. Dual-space linear discriminant analysis for face recognition[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, 2: 564-569.
[34] Guo G, Li S Z, and Chan K. Face Recognition by Support Vector Machines[C]. Proceedings of Internation Conferenc on Automatic Face and Gesture Recognition. 2000: 196-201.
[35] Jonsson K., Matas J., Kittler J., et al. Learning support vectors for face verification and recognition[C]. Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition. 2000: 208-213.
[36] Blanz V, Vetter T. Face recognition based on fitting a 3D morphable model[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(9): 1063-1074.
[37] Chellappa R., Kruger V., Zhou S. Probabilistic recognition of human faces from video[C]. Proceedings of International Conference on Image Processing. 2002, 1: 41-44.
[38] Wagner A. Wright J. Ganesh A. Zihan Zhou Yi Ma. Towards a practical face recognition system: Robust registration and illumination by sparse representation[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009: 597-604.
[39] Yang A Y, Sastry S S, Ganesh A. Yi Ma. Fast ?1-minimization algorithms and an application in robust face recognition: A review[C]. Proceedings of IEEE International Conference on Image Processing,2010: 1849-1852.
[40] Ahonen T, Hadid A, Pietik?inen M. Face Recognition with Local Binary Patterns[C]. Proceedings of 8th European Conference on Computer Vision,2004: 469-481.
[41] Tan XY,Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions[C]. Proceedings of the 3rd IEEE International Workshop on Analysis and Modeling of Faces and Gestures. 2007, 4778: 168-182.
[42] Kingsbury N G. The dual-tree complex wavelet transform: a new efficient tool for image restoration and enhancement[C]. Proceedings of the 9th European Signal Processing Conference (EUSIPCO 98), 1998.
[43] Zhang Ruo, Tsai Ping-Sing, Cryer J E, et al. Shape-from-shading: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(8): 690-706.
[44]李子荣,杜明辉.基于局部边界鉴别分析的人脸识别[J].电子与信息学报, 2009, 31(3): 527-531.
[45] da Cunha A L, Jianping Zhou, Do M N. The Nonsubsampled Contourlet Transform: Theory, Design, and Applications[J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089-3101.
[46] Chan T.F., Esedoglu S. Aspects of Total Variation Regularized L1 Function Approximation[J]. SIAM Journal on Applied Mathematics, 2005, 65(5): 1817-1837.
[47] Ekenel H K, Fischer M, Tekeli E, et al. Local binary pattern domain local appearance face recognition[C]. Proceedings of IEEE 16th Signal Processing, Communication and Applications Conference, 2008: 1-4.
[48] Zhang Wenchao, Shan Shiguang, Gao Wen, et al. Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition[C]. Proceedings of IEEE Tenth International Conference on Computer Vision, 2005, 1: 786-791.
[49] Tan Xiaoyang, Triggs B. Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions[J]. IEEE Transactions on Image Processing, 2010, 19(6): 1635-1650.
[50] Chen Zen, Sun Shu-Kuo. A Zernike Moment Phase-Based Descriptor for Local Image Representation and Matching[J]. IEEE Transactions on Image Processing, 2010, 19(1): 205-219.
[51]应自炉,蔡淋波,刘召义.基于LBP的拉普拉斯特征映射人脸识别[J].信号处理, 2010, 26(8): 1230-1233.
[52] Ojala T, Pietik?inen M, Harwood D. A comparative study of texture measures with classification based on featured distributions[J].Pattern Recognition,1996, 29(1): 51-59.
[53] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
[54] Ahonen T, Hadid A, Pietikainen M. Face Description with Local Binary Patterns: Application to Face Recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(12): 2037-2041.
[55] Xu Xianchuan, Zhang Qi. Medical Image Retrieval Using Local Binary Patterns with Image Euclidean Distance[C]. Proceedings of International Conference on Information Engineering and Computer Science, 2009: 1-4.
[56] Takala V, Ahonen T, Pietikainen M. Block-based methods for image retrieval using local binary patterns[C]. Proceedings of the 4th Scandinavian Conference on Image Analysis Joensuu, 2005, 3540: 882-891.
[57] Hongliang Jin, Qingshan Liu, Hanqing Lu, et al. Face detection using improved LBP under Bayesian framework[C]. Proceedings of Third International Conference on Image and Graphics, 2004: 306-309.
[58] Toan Thanh Do, Khiem Ngoc Doan, Thai Hoang Le, et al. Boosted of Haar-like Features and Local Binary Pattern Based Face Detection[C]. Proceedings of International Conference on Computing and Communication Technologies, 2009: 1-8.
[59] Jiali Zhao, Haitao Wang, Haibing Ren, et al. LBP Discriminant Analysis for Face Verification[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, 2005: 167.
[60] Petpon A, Srisuk S. Face Recognition with Local Line Binary Pattern[C]. Proceedings of Fifth International Conference on Image and Graphics, 2009: 533-539.
[61] Juefei Xu, Cha M, Heyman J L, et al. Robust local binary pattern feature sets for periocular biometric identification[C]. Proceedings of Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), 2010: 1-8.
[62] Xie Shufu, Shan Shiguang, Chen Xilin, et al. Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1349-1361.
[63]赵灵芝,李伟生.一种基于LTP特征的图像匹配方法[J].计算机应用研究, 2009, 26(10): 3983-3985.
[64]王玮,黄非非,李见为,冯海亮.采用LBP金字塔的人脸描述与识别[J].计算机辅助设计与图形学学报, 2009, 21(1): 94-106.
[65] Rioul O, Vetterli M. Wavelets and signal processing[J]. IEEE Signal Processing Magazine, 1991, 8(4): 14-38.
[66] Antonini M, Barlaud M, Mathieu P, et al. Image coding using wavelet transform[J]. IEEE Transactions on Image Processing, 1992, 1(2): 205-220.
[67] Mallat S, Hwang W L. Singularity detection and processing with wavelets[J]. IEEE Transactions on Information Theory, 1992, 38(2): 617-643.
[68] Lades M, Vorbruggen J C, Buhmann J, et al. Distortion invariant object recognition in the dynamic link architecture[J].IEEE Transactions on Computers,1993,42(3): 300-311.
[69] Mallat S G, Zhifeng Zhang. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.
[70] Shapiro J M. Embedded image coding using zerotrees of wavelet coefficients[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3445-3462.
[71] Donoho D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.
[72] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[73] Mallat S G. A theory for multiresolution signal decomposition: the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693.
[74]程正兴,杨守志,冯晓霞.小波分析的理论算法进展和应用[M].北京:国防工业出版社, 2007.
[75] Daubechies I. The wavelet transform, time-frequency localization and signal analysis[J]. IEEE Transactions on Information Theory, 1990, 36(5): 961-1005.
[76] Mallat S G. Multifrequency channel decompositions of images and wavelet models[J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1989, 37(12): 2091-2110.
[77]程正兴.小波算法与应用[M].西安:西安交通大学出版社, 1998.
[78] Jen-Tzung Chien, Chia-Chen Wu. Discriminant waveletfaces and nearest feature classifiers for face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(12): 1644-1649.
[79] Chen, CJ, Zhang, JS. Wavelet energy entropy as a new feature extractor for face recognition[C]. Proceedings of the Fourth International Conference on Image and Graphics. 2007: 616-619.
[80] Samaria F S, Harter A C. Parameterisation of a stochastic model for human face identification[C]. Proceedings of the Second IEEE Workshop on Applications of Computer Vision, 1994: 138-142.
[81] The ORL Database of Faces[EB/OL]. http://www.cl.cam.ac.uk/research/dtg/attarchive /facedatabase.html.
[82] Yale Face Database[EB/OL]. http://cvc.yale.edu/projects/yalefaces/yalefaces.html.
[83] Mu X Y, Hassoun M H, Watta P. Combining Gabor features: summing vs. voting in human face recognition[C]. Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 2003, 1: 737-743.
[84] Duc B, Fischer S, Bigun J. Face authentication with Gabor information on deformable graphs[J]. IEEE Transactions on Image Processing, 1999, 8(4): 504-516.
[85] Rui Liao, Li S Z. Face recognition based on multiple facial features[C]. Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000: 239-244.
[86] Chengjun Liu, Wechsler H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J]. IEEE Transactions on Image Processing, 2002, 11(4): 467-476.
[87] Ayinde O, Yang Y-H. Face recognition approach based on rank correlation of Gabor-filtered images[J]. Pattern Recognition, 2002, 35(6): 1275-1289.
[88] Zheng Z, Yang F, Tan W, et al. Gabor feature-based face recognition using supervisedlocality preserving projection[J]. Signal Processing, 2007, 87(10): 2473-2483.
[89] Lai J H, Yuen P C, Feng G C. Face recognition using holistic Fourier invariant features[J]. Pattern Recognition, 2001, 34(1): 95-109.
[90] N?s?nen R. Spatial frequency bandwidth used in the recognition of facial images[J]. Vision Research, 1999, 39(23): 3824-3833.
[91]赖剑煌;阮邦志;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[J].中国图象图形学报, 1999, 4(10): 811-817.
[92] Pan Z J, Rust A G, Bolouri H. Image redundancy reduction for neural network classification using discrete cosine transforms[C]. Proceedings of IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000, 3: 149-154.
[93] Garcia C., Zikos G., Tziritas G. A wavelet-based framework for face recognition[C]. Proceedings of Fifth European Conference on Computer Vision,1998: 84–92.
[94] Dai D Q, Yuen P C. Wavelet based discriminant analysis for face recognition[J]. Applied Mathematics and Computation, 2006, 175: 307-318.
[95] Dai D Q, Yan H. Wavelets and face recognition[M]. Vienna, Austria: Kresimir Delac and Mislav Grgic, 1998.
[96] Belzer B, Lina J-M, Villasenor J. Complex, linear-phase filters for efficient image coding[J]. IEEE Transactions on Signal Processing, 1995, 43(10): 2425-2427.
[97] Clonda D, Lina J-M, Goulard B. Complex Daubechies wavelets: properties and statistical image modelling[J]. Signal Processing, 2004, 84(1): 1-23.
[98] Kingsbury N. Rotation-invariant local feature matching with complex wavelets[C]. Proceedings of 14th European Signal Processing Conference, Florence, 2006: 4-8.
[99] Kingsbury N. Image processing with complex wavelets[J]. Philosophical Transactions of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 1999, 357(1760): 2543-2560.
[100] Kingsbury N. The Dual-Tree Complex Wavelet Transform: A New Technique For Shift Invariance And Directional Filters[R]. IEEE Digital Signal Processing Workshop DSP, Bryce Canyon, USA., 1998: 319-322.
[101] Kingsbury N. Complex Wavelets for Shift Invariant Analysis and Filtering of Signals[J]. Applied and Computational Harmonic Analysis, 2001, 10(3): 234-253.
[102] Kingsbury N. Shift invariant properties of the dual-tree complex wavelet transform[C]. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 1999, 3: 1221-1224.
[103] Hill P R, Bull D R, Canagarajah C N. Rotationally invariant texture features using the dual-tree complex wavelet transform[C]. Proceedings of International Conference on Image Processing, 2000, 3: 901-904.
[104] Chen Bo, Geng Zexun, Yang Yang, et al. Dual-tree Complex Wavelets Transforms for Image Denoising[C]. Proceedings of Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing,2007, 1: 70-74.
[105] Wang Hua-Zhang, He Xiao-Hai, Zai Wen Jiao. Texture image retrieval using dual-tree complex wavelet transform[C]. Proceedings of International Conference on Wavelet Analysis and Pattern Recognition, 2007, 1: 230-234.
[106] Narote A S, Narote S P, Waghmare L M, et al. Robust Iris Feature Extraction using Dual Tree Complex Wavelet Transform[C]. Proceedings of IEEE International Conference on Signal Processing and Communications, 2007: 975-978.
[107] Mumtaz A, Gilani S A M, Jameel T. A Novel Texture Image Retrieval System Based On Dual Tree Complex Wavelet Transform and Support Vector Machines[C]. Proceedings of International Conference on Emerging Technologies, 2006: 108-114.
[108] Celik T, Tjahjadi T. Image Resolution Enhancement Using Dual-Tree Complex Wavelet Transform[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(3): 554-557.
[109] Kim D-O, Park R-H. Evaluation of image quality using dual-tree complex wavelet transform and compressive sensing[J]. Electronics Letters, 2010, 46(7): 494 -495.
[110] ?elik T, ?zkaramanlI H, Demirel H. Facial feature extraction using complex dual-tree wavelet transform[J]. Computer Vision and Image Understanding, 2008, 111(2): 229-246.
[111]孙粤辉,杜明辉.基于正交邻域保持投射的DT-CWT特征人脸识别[J].华南理工大学学报(自然科学版), 2007, 35(9): 45-49.
[112] Liu Chao-Chun, Dai Dao-Qing. Face Recognition Using Dual-Tree Complex Wavelet Features[J]. IEEE Transactions on Image Processing, 2009, 18(11): 2593-2599.
[113] Lin Jiang. Face recognition under varying illumination based on Dual-tree complex wavelet transform[C]. Proceedings of International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), 2010: 314-317.
[114] Hu H. Augmented DT-CWT feature based classification using Regularized Neighborhood Projection Discriminant Analysis for face recognition[J]. Pattern Recognition, 2011,44(3):519-531
[115] Hazim Kemal Ekenel, Sankur B. Multiresolution face recognition[J]. Image and Vision Computing, 2005, 23(5): 469-477.
[116] Vetterli M, Herley C. Wavelets and filter banks: theory and design[J]. IEEE Transactions on Signal Processing, 1992, 40(9): 2207-2232.
[117] Lawton, W. Applications of complex valued wavelet transforms to subband decomposition[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3566-3568.
[118] Selesnick I W, Baraniuk R G, Kingsbury N C. The dual-tree complex wavelet transform[J]. IEEE Signal Processing Magazine, 2005, 22(6): 123-151.
[119] Kingsbury N. Design of Q-shift complex wavelets for image processing using frequency domain energy minimization[C]. Proceedings of International Conference on Image Processing. 2003, 3: 1013-1016.
[120] Sim T, Baker S, Bsat M. The CMU Pose, Illumination, and Expression (PIE) database[C]. Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002: 46-51.
[121] CMU Face Pose, Illumination, and Expression (PIE) Database[EB/OL]. http://www.ri. cmu.edu /projects/project_418.html.
[122] Yan Shuicheng, Xu Dong, Zhang Benyu, et al. Graph embedding: a general framework for dimensionality reduction[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 2: 830-837.
[123] Nayar S K, Bolle R M. Computing reflectance ratios from an image[J]. Pattern Recognition, 1993, 26(10): 1529-1542.
[124] Scharcanski J, Jung C R, Clarke R T. Adaptive image denoising using scale and space consistency[J]. IEEE Transactions on Image Processing, , 2002, 11(9): 1092-1101.
[125] Moulin P, Juan Liu. Analysis of multiresolution image denoising schemes usinggeneralized Gaussian and complexity priors[J]. IEEE Transactions on Information Theory, 1999, 45(3): 909-919.
[126] Chambolle A, Lucier B J. Interpreting translation-invariant wavelet shrinkage as a new image smoothing scale space[J]. IEEE Transactions on Image Processing, 2001, 10(7): 993-1000.
[127]侯建华,熊承义,田晓梅.广义高斯分布及其在图像去噪中的应用[J].中南民族大学学报(自然科学版), 2005, 24(3): 44-47.
[128]汪祖媛,庄镇泉,何劲松等.基于形状的小波变换系数广义高斯分布图像检索算法[J].电子学报, 2003, 31(5): 765-768.
[129] Do M N, Vetterli M. Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance[J]. IEEE Transactions on Image Processing, 2002, 11(2): 146-158.
[130] Extended Yale Face Database B[EB/OL]. http://cvc.yale.edu/projects/yalefacesB /yalefacesB.html.
[131] Georghiades A S, Belhumeur P N, Kriegman D J. From few to many: illumination cone models for face recognition under variable lighting and pose[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 643-660.
[132]马晓丽,张亮.全概率公式的推广及其在保险中的应用[J].高等数学研究, 2010, 13(1): 70-71.
[133]安高云.复杂条件人脸识别中若干关键问题的研究[D].北京:北京交通大学, 2008.
[134] Xianfang Sun, Rosin P L, Martin R R, et al. Bas-Relief Generation Using Adaptive Histogram Equalization[J]. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(4): 642-653.
[135] Shan Shiguang, Gao Wen, Cao Bo, et al. Illumination normalization for robust face recognition against varying lighting conditions[C]. Proceedings of IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003: 157-164.
[136] Xie X, Lam K-M. Face recognition under varying illumination based on a 2D face shape model[J]. Pattern Recognition, 2005, 38(2): 221-230.
[137] Xuetao Feng, Yangsheng Wang, Yong Gao. A Framework of Local Illumination Normalization for Face Recognition[C]. Proceedings of IEEE International Workshopon Anti-counterfeiting, Security, Identification, 2007: 199-203.
[138] Savvides M., Kumar v. Illumination Normalization Using Logarithm Transforms for Face Authentication[J]. Audio- and Video-Based Biometric Person Authentication, Lecture Notes in Computer Science, 2003, 2688: 549-556.
[139] Liansheng Zhuang, Xueyi Ye, Yong Wang, et al. An Illumination Processing Algorithm Based on Total Variation Model[C]. Proceedings of the Sixth World Congress on Intelligent Control and Automation, 2006, 2: 10462-10466.
[140] Wong K H, Law H H, Tsang P W. A system for recognising human faces[C]. Proceedings of international conference of acoust,speech,and signal process. 1989, 3: 1636-1642.
[141] Hsu Rein-Lien, Abdel-Mottaleb M, Jain A K. Face detection in color images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 696-706.
[142] Linlin Shen and Li Bai. A review on Gabor wavelets for face recognition[J]. Pattern Analysis & Applications, 2006, 9: 273-292.
[143] Bin Li, Kai-Kuang Ma. Fisherface vs. Eigenface in the Dual-tree Complex Wavelet Domain[C]. Proceedings of Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009: 30-33.
[144] Chen H F, Belhumeur P N, Jacobs D W. In search of illumination invariants[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2000. Proceedings, 2000, 1: 254-261.
[145] Jacobs D W, Belhumeur P N, Basri R. Comparing images under variable illumination[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998: 610-617.
[146] Adini Y, Moses Y, Ullman S. Face recognition: the problem of compensating for changes in illumination direction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 721-732.
[147] Shashua A, Riklin-Raviv T. The quotient image: class-based re-rendering and recognition with varying illuminations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 129-139.
[148] Haitao Wang, Li S Z, Yangsheng Wang. Generalized quotient image[C]. Proceedings ofIEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, 2: 498- 505.
[149] Chen T, Yin W, Zhou X S, et al. Illumination normalization for face recognition and uneven background correction using total variation based image models[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 2: 532-539.
[150] Chen T, Wotao Yin, Xiang Sean Zhou, et al. Total variation models for variable lighting face recognition[J]. IEEE Transactions on,Pattern Analysis and Machine Intelligence, 2006, 28(9): 1519-1524.
[151] Wen Yi Zhao, Chellappa R. Illumination-insensitive face recognition using symmetric shape-from-shading[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2000. Proceedings, 2000, 1: 286-293.
[152] Sim T, Kanade T. Combining models and exemplars for face recognition: an illuminating example[C]. Proceedings of Workshop on Models versus Exemplars in Computer Vision, CVPR 2001. 2001.
[153] Georghiades A S, Kriegman D J, Belhurneur P N. Illumination cones for recognition under variable lighting: faces[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998: 52-58.
[154] Georghiades A S, Belhumeur P N, Kriegman D J. From few to many: illumination cone models for face recognition under variable lighting and pose[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 643-660.
[155] Basri R, Jacobs D W. Lambertian reflectance and linear subspaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(2): 218-233.
[156] Ramamoorthi R. Analytic PCA construction for theoretical analysis of lighting variability in images of a Lambertian object[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(10): 1322-1333.
[157] Lie Zhang, Samaras D. Face recognition under variable lighting using harmonic image exemplars[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, 1: 19-25.
[158] Cernuschi-Frias B, Cooper D B. 3-D Space Location and Orientation ParameterEstimation of Lambertian Spheres and Cylinders From a Single 2-D Image By Fitting Lines and Ellipses to Thresholded Data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(4): 430-441.
[159] Nayar S K, Ikeuchi K, Kanade T. Shape and reflectance from an image sequence generated using extended sources[C]. Proceedings of IEEE International Conference on Robotics and Automation, 1989: 28-35.
[160] Horn B.K.P. Robot Vision[M]. Cambridge,US: MIT Press, 1986.
[161] Ramamoorthi R, Hanrahan P. A signal-processing framework for inverse rendering[C]. Proceedings of the 28th annual conference on computer graphics and interactive techniques 2001: 117-128.
[162] Horn B.K.P. Determining lightness from an image[J]. Computer Graphics and Image Processing, 1974, 3(4): 277-299.
[163] Land E.H. and McCann J.J. Lightness and retinex theory[J]. Journal of the Optical Society of America, 1971, 61(1): 1-11.
[164] Zhang T, Fang B, Yuan Y, et al. Multiscale facial structure representation for face recognition under varying illumination[J]. Pattern Recognition, 2009, 42(2): 251-258.
[165] Donoho D L. Adaptive signal representations: How much is too much?[C]. Proceedings of IEEE-IMS Workshop on Information Theory and Statistics, 1994: 53.
[166] Donoho D L, Johnstone I M. Threshold selection for wavelet shrinkage of noisy data[C]. Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1994: 24-25.
[167] Qu Tianshu, Wang Shuxun, Chen Haihua, et al. Adaptive denoising based on wavelet thresholding method[C]. Proceedings of 6th International Conference on Signal Processing, 2002, 1: 120-123.
[168] Cheng Y, Hou Y, Zhao C, et al. Robust face recognition based on illumination invariant in nonsubsampled contourlet transform domain[J]. Neurocomputing, 2010, 73(10-12): 2217-2224.
[169] Shuicheng Yan, Dong Xu, Benyu Zhang, et al. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51.
[170] Cai D., He X., Zhou K., et al. Locality sensitive discriminant analysis[C]. Proceedings of Internationa Joint Conference on Artificial Intelligence(IJCAI'07), 2007: 708-713.
[171]焦李成,谭山.图像的多尺度几何分析:回顾和展望[J].电子学报, 2003, 31(12): 1975-1981.
[172] Do M N, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.
[173] Guoan Yang, Zhiqiang Tian, Chongyuan Bi, et al. An Image Coding Approach Using Wavelet-Based Adaptive Contourlet Transform[C]. Proceedings of International Joint Conference on Computational Sciences and Optimization, 2009, 1: 811-815.
[174] Li Ding, Han Chongzhao. Muti-focus Image Fusion Using Wavelet Based Contourlet Transform and Region[C]. Proceedings of International Conference on Information Management and Engineering, 2009: 90-93.
[175] Zhang Qidong, Wu Jianhua, Gao Liqun. Image retrieval based on contourlet transform and local binary patterns[C]. Proceedings of 4th IEEE Conference on Industrial Electronics and Applications, 2009: 2682-2685.
[176] Cai Lin-Bo, Ying Zi-Lu. A new approach of facial expression recognition based on Contourlet Transform[C]. Proceedings of International Conference on Wavelet Analysis and Pattern Recognition, 2009: 275-280.
[177] da Cunha A L, Zhou J, Do M N. Nonsubsampled contourilet transform: filter design and applications in denoising[C]. Proceedings of IEEE International Conference on Image Processing, 2005, 1: 749-752.
[178] Liang-liang Huang, Hong-zhi Wang, Bo Zhu. Adaptive Thresholds Algorithm of Image Denoising Based on Nonsubsampled Contourlet Transform[C]. Proceedings of International Conference on omputer Science and Software Engineering, 2008, 6: 209-212.
[179] Yuxin Ma, Jiancang Xie, Jungang Luo. Image Enhancement Based on Nonsubsampled Contourlet Transform[C]. Proceedings of the Fifth International Conference on Information Assurance and Security, 2009, 1: 31-34.
[180] Wang Hai, Feng Zijun. Image fusion algorithm based on nonsubsampled contourlettransform[C]. Proceedings of the 2nd International Conference on Future Computer and Communication (ICFCC), 2010, 1: 220-224.
[181]Leonid I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D, 1992, 60(1-4): 259-268.
[182]安高云.复杂条件人脸识别中若干关键问题的研究[D].北京:北京交通大学, 2008.
[183]陈天华.数字图像处理[M].第1版.北京:清华大学出版社, 2007.
[184] An G, Wu J, Ruan Q. An illumination normalization model for face recognition under varied lighting conditions[J]. Pattern Recognition Letters, 2010, 31(9): 1056-1067.
[185]吴亚东,孙世新,张红英等.一种基于图割的全变差图像去噪算法[J].电子学报, 2007, 35(2): 265-268.
[186] You-Wei Wen, Ng M K, Yu-Mei Huang. Efficient Total Variation Minimization Methods for Color Image Restoration[J]. IEEE Transactions on Image Processing, 2008, 17(11): 2081-2088.
[187] Kumar M, Dass S. A Total Variation-Based Algorithm for Pixel-Level Image Fusion[J]. IEEE Transactions on Image Processing, 2009, 18(9): 2137-2143.
[188] Chartrand R, Staneva V. Total variation regularisation of images corrupted by non-Gaussian noise using a quasi-Newton method[J]. IET Image Processing, 2008, 2(6): 295-303.
[189] Yin, W., Goldfarb, D., Osher, S. The total variation regularized L1 for multiscale decomposition[J]. Multiscale Modeling and Simulation, 2006, 6(1): 190–211.
[2]孙冬梅,裘正定.生物特征识别技术综述[J].电子学报, 2001, 29(12): 1744-1748.
[3] Jain A, Lin Hong, Bolle R. On-line fingerprint verification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(4): 302-314.
[4] Yue Feng, Zuo Wang-Meng, Zhang Da-Peng. Survey of Palmprint Recognition Algorithms[J]. Acta Automatica Sinica, 2010, 36(3): 353-365.
[5] Wildes R P. Iris recognition: an emerging biometric technology[J]. Proceedings of the IEEE, 1997, 85(9): 1348-1363.
[6] Li Ma, Tieniu Tan, Yunhong Wang, et al. Efficient iris recognition by characterizing key local variations[J]. IEEE Transactions on Image Processing, 2004, 13(6): 739-750.
[7] Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.
[8] Jain A K, Duin R P W, Jianchang Mao. Statistical pattern recognition: a review[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(1): 4-37.
[9] Wu J, Zhou Z H. Face recognition with one training image per person[J]. Pattern Recognition Letters, 2002, 23(14): 1711-1719.
[10] Gross R, Matthews I, Baker S. Appearance-based face recognition and light-fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(4): 449-465.
[11]孟明,吴仲城,余永,等.基于笔段特征和HMM的在线签名认证方法研究[J].模式识别与人工智能, 2007, 20(1): 95-100.
[12] Vélez J, Sánchezá, Moreno B, et al. Fuzzy shape-memory snakes for the automatic off- line signature verification problem[J].Fuzzy Sets and Systems,2009,160(2): 182-197.
[13] Adriani M, Maeder P, Meuli R, et al. Sound recognition and localization in man: specialized cortical networks and effects of acute circumscribed lesions[J]. Experimental Brain Research, 2003, 153(4): 591-604.
[14] Zeng J, Zhu S F, Yan H. Towards accurate human promoter recognition: a review of currently used sequence features and classification methods[J]. Briefings in BioinformaticS, 2008, 25(8): 498-508.
[15] Raghavendra R, Dorizzi B, Rao A, et al. Designing efficient fusion schemes for multimodal biometric systems using face and palmprint[J]. Pattern Recognition, 2011, 44(5): 1076-1088.
[16] Face Recognition Vendor Tests[EB/OL]. http://www.frvt.org.
[17] Phillips P J, Scruggs W T, O’Toole A J, et al. FRVT 2006 and ICE 2006 Large-Scale Experimental Results[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(5): 831-846.
[18] Zhao, W; Chellappa, R; Phillips, PJ, et al. Face recognition: A literature survey[J]. ACM Computing Surveys, 2003, 35(4): 399-459.
[19]孙粤辉.基于DT-CWT特征的人脸检测与识别研究[D].广州:华南理工大学, 2007.
[20]周杰,卢春雨,张长水等.人脸自动识别方法综述[J].电子学报, 2000, 28(4): 102-106.
[21]章毓晋,程正东,贾慧星.基于子空间的人脸识别[M].北京:清华大学出版社, 2009.
[22]王映辉.人脸识别:原理、方法与技术[M].北京:科学出版社, 2010.
[23] Turk M A, Pentland A P. Face recognition using eigenfaces[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991: 586-591.
[24] Oenev P, Atick J. Local Feature Analysis: A General Statistical Theory for Object Reperesentation[J]. Network: Computation in Neural System, 1996, 7: 477-500.
[25] Lanitis A, Taylor C J, Cootes T F. Automatic interpretation and coding of face images using flexible models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 743-756.
[26] Wiskott L, Fellous J-M, Kuiger N, et al. Face recognition by elastic bunch graph matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 775-779.
[27] Phillips P J, Rauss P J, Der S Z. FERET recognition algorithm development and test results[R]. Technical Report ARL-TR-995, US Army Research Laboratory, 1996.
[28] Kohonen T. Self-organization and associative memory[M]. New York, US:Springer-Verlag New York, 1989.
[29] Ranganath S., Arun K. Face recognition using transform features and neural networks[J]. Pattern Recognition, 1997, 30(10): 1615-1622.
[30] Yang M-H, Ahuja N, Kriegman D. Face recognition using kernel eigenfaces[C]. Proceedings of International Conference on Image Processing, 2000, 1: 37-40.
[31] Yang Ming-Hsuan. Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods[C]. Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002: 215-220.
[32] Liu C, Wechsler H. A Unified Bayesian Framework for Face Recognition[C]. Proceedings of International Conference on Image Processing, 1998, 1: 151-155.
[33] Wang X., Tang X. Dual-space linear discriminant analysis for face recognition[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, 2: 564-569.
[34] Guo G, Li S Z, and Chan K. Face Recognition by Support Vector Machines[C]. Proceedings of Internation Conferenc on Automatic Face and Gesture Recognition. 2000: 196-201.
[35] Jonsson K., Matas J., Kittler J., et al. Learning support vectors for face verification and recognition[C]. Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition. 2000: 208-213.
[36] Blanz V, Vetter T. Face recognition based on fitting a 3D morphable model[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(9): 1063-1074.
[37] Chellappa R., Kruger V., Zhou S. Probabilistic recognition of human faces from video[C]. Proceedings of International Conference on Image Processing. 2002, 1: 41-44.
[38] Wagner A. Wright J. Ganesh A. Zihan Zhou Yi Ma. Towards a practical face recognition system: Robust registration and illumination by sparse representation[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009: 597-604.
[39] Yang A Y, Sastry S S, Ganesh A. Yi Ma. Fast ?1-minimization algorithms and an application in robust face recognition: A review[C]. Proceedings of IEEE International Conference on Image Processing,2010: 1849-1852.
[40] Ahonen T, Hadid A, Pietik?inen M. Face Recognition with Local Binary Patterns[C]. Proceedings of 8th European Conference on Computer Vision,2004: 469-481.
[41] Tan XY,Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions[C]. Proceedings of the 3rd IEEE International Workshop on Analysis and Modeling of Faces and Gestures. 2007, 4778: 168-182.
[42] Kingsbury N G. The dual-tree complex wavelet transform: a new efficient tool for image restoration and enhancement[C]. Proceedings of the 9th European Signal Processing Conference (EUSIPCO 98), 1998.
[43] Zhang Ruo, Tsai Ping-Sing, Cryer J E, et al. Shape-from-shading: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(8): 690-706.
[44]李子荣,杜明辉.基于局部边界鉴别分析的人脸识别[J].电子与信息学报, 2009, 31(3): 527-531.
[45] da Cunha A L, Jianping Zhou, Do M N. The Nonsubsampled Contourlet Transform: Theory, Design, and Applications[J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089-3101.
[46] Chan T.F., Esedoglu S. Aspects of Total Variation Regularized L1 Function Approximation[J]. SIAM Journal on Applied Mathematics, 2005, 65(5): 1817-1837.
[47] Ekenel H K, Fischer M, Tekeli E, et al. Local binary pattern domain local appearance face recognition[C]. Proceedings of IEEE 16th Signal Processing, Communication and Applications Conference, 2008: 1-4.
[48] Zhang Wenchao, Shan Shiguang, Gao Wen, et al. Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition[C]. Proceedings of IEEE Tenth International Conference on Computer Vision, 2005, 1: 786-791.
[49] Tan Xiaoyang, Triggs B. Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions[J]. IEEE Transactions on Image Processing, 2010, 19(6): 1635-1650.
[50] Chen Zen, Sun Shu-Kuo. A Zernike Moment Phase-Based Descriptor for Local Image Representation and Matching[J]. IEEE Transactions on Image Processing, 2010, 19(1): 205-219.
[51]应自炉,蔡淋波,刘召义.基于LBP的拉普拉斯特征映射人脸识别[J].信号处理, 2010, 26(8): 1230-1233.
[52] Ojala T, Pietik?inen M, Harwood D. A comparative study of texture measures with classification based on featured distributions[J].Pattern Recognition,1996, 29(1): 51-59.
[53] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
[54] Ahonen T, Hadid A, Pietikainen M. Face Description with Local Binary Patterns: Application to Face Recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(12): 2037-2041.
[55] Xu Xianchuan, Zhang Qi. Medical Image Retrieval Using Local Binary Patterns with Image Euclidean Distance[C]. Proceedings of International Conference on Information Engineering and Computer Science, 2009: 1-4.
[56] Takala V, Ahonen T, Pietikainen M. Block-based methods for image retrieval using local binary patterns[C]. Proceedings of the 4th Scandinavian Conference on Image Analysis Joensuu, 2005, 3540: 882-891.
[57] Hongliang Jin, Qingshan Liu, Hanqing Lu, et al. Face detection using improved LBP under Bayesian framework[C]. Proceedings of Third International Conference on Image and Graphics, 2004: 306-309.
[58] Toan Thanh Do, Khiem Ngoc Doan, Thai Hoang Le, et al. Boosted of Haar-like Features and Local Binary Pattern Based Face Detection[C]. Proceedings of International Conference on Computing and Communication Technologies, 2009: 1-8.
[59] Jiali Zhao, Haitao Wang, Haibing Ren, et al. LBP Discriminant Analysis for Face Verification[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, 2005: 167.
[60] Petpon A, Srisuk S. Face Recognition with Local Line Binary Pattern[C]. Proceedings of Fifth International Conference on Image and Graphics, 2009: 533-539.
[61] Juefei Xu, Cha M, Heyman J L, et al. Robust local binary pattern feature sets for periocular biometric identification[C]. Proceedings of Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), 2010: 1-8.
[62] Xie Shufu, Shan Shiguang, Chen Xilin, et al. Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1349-1361.
[63]赵灵芝,李伟生.一种基于LTP特征的图像匹配方法[J].计算机应用研究, 2009, 26(10): 3983-3985.
[64]王玮,黄非非,李见为,冯海亮.采用LBP金字塔的人脸描述与识别[J].计算机辅助设计与图形学学报, 2009, 21(1): 94-106.
[65] Rioul O, Vetterli M. Wavelets and signal processing[J]. IEEE Signal Processing Magazine, 1991, 8(4): 14-38.
[66] Antonini M, Barlaud M, Mathieu P, et al. Image coding using wavelet transform[J]. IEEE Transactions on Image Processing, 1992, 1(2): 205-220.
[67] Mallat S, Hwang W L. Singularity detection and processing with wavelets[J]. IEEE Transactions on Information Theory, 1992, 38(2): 617-643.
[68] Lades M, Vorbruggen J C, Buhmann J, et al. Distortion invariant object recognition in the dynamic link architecture[J].IEEE Transactions on Computers,1993,42(3): 300-311.
[69] Mallat S G, Zhifeng Zhang. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.
[70] Shapiro J M. Embedded image coding using zerotrees of wavelet coefficients[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3445-3462.
[71] Donoho D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.
[72] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[73] Mallat S G. A theory for multiresolution signal decomposition: the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693.
[74]程正兴,杨守志,冯晓霞.小波分析的理论算法进展和应用[M].北京:国防工业出版社, 2007.
[75] Daubechies I. The wavelet transform, time-frequency localization and signal analysis[J]. IEEE Transactions on Information Theory, 1990, 36(5): 961-1005.
[76] Mallat S G. Multifrequency channel decompositions of images and wavelet models[J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1989, 37(12): 2091-2110.
[77]程正兴.小波算法与应用[M].西安:西安交通大学出版社, 1998.
[78] Jen-Tzung Chien, Chia-Chen Wu. Discriminant waveletfaces and nearest feature classifiers for face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(12): 1644-1649.
[79] Chen, CJ, Zhang, JS. Wavelet energy entropy as a new feature extractor for face recognition[C]. Proceedings of the Fourth International Conference on Image and Graphics. 2007: 616-619.
[80] Samaria F S, Harter A C. Parameterisation of a stochastic model for human face identification[C]. Proceedings of the Second IEEE Workshop on Applications of Computer Vision, 1994: 138-142.
[81] The ORL Database of Faces[EB/OL]. http://www.cl.cam.ac.uk/research/dtg/attarchive /facedatabase.html.
[82] Yale Face Database[EB/OL]. http://cvc.yale.edu/projects/yalefaces/yalefaces.html.
[83] Mu X Y, Hassoun M H, Watta P. Combining Gabor features: summing vs. voting in human face recognition[C]. Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 2003, 1: 737-743.
[84] Duc B, Fischer S, Bigun J. Face authentication with Gabor information on deformable graphs[J]. IEEE Transactions on Image Processing, 1999, 8(4): 504-516.
[85] Rui Liao, Li S Z. Face recognition based on multiple facial features[C]. Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000: 239-244.
[86] Chengjun Liu, Wechsler H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J]. IEEE Transactions on Image Processing, 2002, 11(4): 467-476.
[87] Ayinde O, Yang Y-H. Face recognition approach based on rank correlation of Gabor-filtered images[J]. Pattern Recognition, 2002, 35(6): 1275-1289.
[88] Zheng Z, Yang F, Tan W, et al. Gabor feature-based face recognition using supervisedlocality preserving projection[J]. Signal Processing, 2007, 87(10): 2473-2483.
[89] Lai J H, Yuen P C, Feng G C. Face recognition using holistic Fourier invariant features[J]. Pattern Recognition, 2001, 34(1): 95-109.
[90] N?s?nen R. Spatial frequency bandwidth used in the recognition of facial images[J]. Vision Research, 1999, 39(23): 3824-3833.
[91]赖剑煌;阮邦志;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[J].中国图象图形学报, 1999, 4(10): 811-817.
[92] Pan Z J, Rust A G, Bolouri H. Image redundancy reduction for neural network classification using discrete cosine transforms[C]. Proceedings of IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000, 3: 149-154.
[93] Garcia C., Zikos G., Tziritas G. A wavelet-based framework for face recognition[C]. Proceedings of Fifth European Conference on Computer Vision,1998: 84–92.
[94] Dai D Q, Yuen P C. Wavelet based discriminant analysis for face recognition[J]. Applied Mathematics and Computation, 2006, 175: 307-318.
[95] Dai D Q, Yan H. Wavelets and face recognition[M]. Vienna, Austria: Kresimir Delac and Mislav Grgic, 1998.
[96] Belzer B, Lina J-M, Villasenor J. Complex, linear-phase filters for efficient image coding[J]. IEEE Transactions on Signal Processing, 1995, 43(10): 2425-2427.
[97] Clonda D, Lina J-M, Goulard B. Complex Daubechies wavelets: properties and statistical image modelling[J]. Signal Processing, 2004, 84(1): 1-23.
[98] Kingsbury N. Rotation-invariant local feature matching with complex wavelets[C]. Proceedings of 14th European Signal Processing Conference, Florence, 2006: 4-8.
[99] Kingsbury N. Image processing with complex wavelets[J]. Philosophical Transactions of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 1999, 357(1760): 2543-2560.
[100] Kingsbury N. The Dual-Tree Complex Wavelet Transform: A New Technique For Shift Invariance And Directional Filters[R]. IEEE Digital Signal Processing Workshop DSP, Bryce Canyon, USA., 1998: 319-322.
[101] Kingsbury N. Complex Wavelets for Shift Invariant Analysis and Filtering of Signals[J]. Applied and Computational Harmonic Analysis, 2001, 10(3): 234-253.
[102] Kingsbury N. Shift invariant properties of the dual-tree complex wavelet transform[C]. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 1999, 3: 1221-1224.
[103] Hill P R, Bull D R, Canagarajah C N. Rotationally invariant texture features using the dual-tree complex wavelet transform[C]. Proceedings of International Conference on Image Processing, 2000, 3: 901-904.
[104] Chen Bo, Geng Zexun, Yang Yang, et al. Dual-tree Complex Wavelets Transforms for Image Denoising[C]. Proceedings of Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing,2007, 1: 70-74.
[105] Wang Hua-Zhang, He Xiao-Hai, Zai Wen Jiao. Texture image retrieval using dual-tree complex wavelet transform[C]. Proceedings of International Conference on Wavelet Analysis and Pattern Recognition, 2007, 1: 230-234.
[106] Narote A S, Narote S P, Waghmare L M, et al. Robust Iris Feature Extraction using Dual Tree Complex Wavelet Transform[C]. Proceedings of IEEE International Conference on Signal Processing and Communications, 2007: 975-978.
[107] Mumtaz A, Gilani S A M, Jameel T. A Novel Texture Image Retrieval System Based On Dual Tree Complex Wavelet Transform and Support Vector Machines[C]. Proceedings of International Conference on Emerging Technologies, 2006: 108-114.
[108] Celik T, Tjahjadi T. Image Resolution Enhancement Using Dual-Tree Complex Wavelet Transform[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(3): 554-557.
[109] Kim D-O, Park R-H. Evaluation of image quality using dual-tree complex wavelet transform and compressive sensing[J]. Electronics Letters, 2010, 46(7): 494 -495.
[110] ?elik T, ?zkaramanlI H, Demirel H. Facial feature extraction using complex dual-tree wavelet transform[J]. Computer Vision and Image Understanding, 2008, 111(2): 229-246.
[111]孙粤辉,杜明辉.基于正交邻域保持投射的DT-CWT特征人脸识别[J].华南理工大学学报(自然科学版), 2007, 35(9): 45-49.
[112] Liu Chao-Chun, Dai Dao-Qing. Face Recognition Using Dual-Tree Complex Wavelet Features[J]. IEEE Transactions on Image Processing, 2009, 18(11): 2593-2599.
[113] Lin Jiang. Face recognition under varying illumination based on Dual-tree complex wavelet transform[C]. Proceedings of International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), 2010: 314-317.
[114] Hu H. Augmented DT-CWT feature based classification using Regularized Neighborhood Projection Discriminant Analysis for face recognition[J]. Pattern Recognition, 2011,44(3):519-531
[115] Hazim Kemal Ekenel, Sankur B. Multiresolution face recognition[J]. Image and Vision Computing, 2005, 23(5): 469-477.
[116] Vetterli M, Herley C. Wavelets and filter banks: theory and design[J]. IEEE Transactions on Signal Processing, 1992, 40(9): 2207-2232.
[117] Lawton, W. Applications of complex valued wavelet transforms to subband decomposition[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3566-3568.
[118] Selesnick I W, Baraniuk R G, Kingsbury N C. The dual-tree complex wavelet transform[J]. IEEE Signal Processing Magazine, 2005, 22(6): 123-151.
[119] Kingsbury N. Design of Q-shift complex wavelets for image processing using frequency domain energy minimization[C]. Proceedings of International Conference on Image Processing. 2003, 3: 1013-1016.
[120] Sim T, Baker S, Bsat M. The CMU Pose, Illumination, and Expression (PIE) database[C]. Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002: 46-51.
[121] CMU Face Pose, Illumination, and Expression (PIE) Database[EB/OL]. http://www.ri. cmu.edu /projects/project_418.html.
[122] Yan Shuicheng, Xu Dong, Zhang Benyu, et al. Graph embedding: a general framework for dimensionality reduction[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 2: 830-837.
[123] Nayar S K, Bolle R M. Computing reflectance ratios from an image[J]. Pattern Recognition, 1993, 26(10): 1529-1542.
[124] Scharcanski J, Jung C R, Clarke R T. Adaptive image denoising using scale and space consistency[J]. IEEE Transactions on Image Processing, , 2002, 11(9): 1092-1101.
[125] Moulin P, Juan Liu. Analysis of multiresolution image denoising schemes usinggeneralized Gaussian and complexity priors[J]. IEEE Transactions on Information Theory, 1999, 45(3): 909-919.
[126] Chambolle A, Lucier B J. Interpreting translation-invariant wavelet shrinkage as a new image smoothing scale space[J]. IEEE Transactions on Image Processing, 2001, 10(7): 993-1000.
[127]侯建华,熊承义,田晓梅.广义高斯分布及其在图像去噪中的应用[J].中南民族大学学报(自然科学版), 2005, 24(3): 44-47.
[128]汪祖媛,庄镇泉,何劲松等.基于形状的小波变换系数广义高斯分布图像检索算法[J].电子学报, 2003, 31(5): 765-768.
[129] Do M N, Vetterli M. Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance[J]. IEEE Transactions on Image Processing, 2002, 11(2): 146-158.
[130] Extended Yale Face Database B[EB/OL]. http://cvc.yale.edu/projects/yalefacesB /yalefacesB.html.
[131] Georghiades A S, Belhumeur P N, Kriegman D J. From few to many: illumination cone models for face recognition under variable lighting and pose[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 643-660.
[132]马晓丽,张亮.全概率公式的推广及其在保险中的应用[J].高等数学研究, 2010, 13(1): 70-71.
[133]安高云.复杂条件人脸识别中若干关键问题的研究[D].北京:北京交通大学, 2008.
[134] Xianfang Sun, Rosin P L, Martin R R, et al. Bas-Relief Generation Using Adaptive Histogram Equalization[J]. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(4): 642-653.
[135] Shan Shiguang, Gao Wen, Cao Bo, et al. Illumination normalization for robust face recognition against varying lighting conditions[C]. Proceedings of IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003: 157-164.
[136] Xie X, Lam K-M. Face recognition under varying illumination based on a 2D face shape model[J]. Pattern Recognition, 2005, 38(2): 221-230.
[137] Xuetao Feng, Yangsheng Wang, Yong Gao. A Framework of Local Illumination Normalization for Face Recognition[C]. Proceedings of IEEE International Workshopon Anti-counterfeiting, Security, Identification, 2007: 199-203.
[138] Savvides M., Kumar v. Illumination Normalization Using Logarithm Transforms for Face Authentication[J]. Audio- and Video-Based Biometric Person Authentication, Lecture Notes in Computer Science, 2003, 2688: 549-556.
[139] Liansheng Zhuang, Xueyi Ye, Yong Wang, et al. An Illumination Processing Algorithm Based on Total Variation Model[C]. Proceedings of the Sixth World Congress on Intelligent Control and Automation, 2006, 2: 10462-10466.
[140] Wong K H, Law H H, Tsang P W. A system for recognising human faces[C]. Proceedings of international conference of acoust,speech,and signal process. 1989, 3: 1636-1642.
[141] Hsu Rein-Lien, Abdel-Mottaleb M, Jain A K. Face detection in color images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 696-706.
[142] Linlin Shen and Li Bai. A review on Gabor wavelets for face recognition[J]. Pattern Analysis & Applications, 2006, 9: 273-292.
[143] Bin Li, Kai-Kuang Ma. Fisherface vs. Eigenface in the Dual-tree Complex Wavelet Domain[C]. Proceedings of Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009: 30-33.
[144] Chen H F, Belhumeur P N, Jacobs D W. In search of illumination invariants[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2000. Proceedings, 2000, 1: 254-261.
[145] Jacobs D W, Belhumeur P N, Basri R. Comparing images under variable illumination[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998: 610-617.
[146] Adini Y, Moses Y, Ullman S. Face recognition: the problem of compensating for changes in illumination direction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 721-732.
[147] Shashua A, Riklin-Raviv T. The quotient image: class-based re-rendering and recognition with varying illuminations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 129-139.
[148] Haitao Wang, Li S Z, Yangsheng Wang. Generalized quotient image[C]. Proceedings ofIEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, 2: 498- 505.
[149] Chen T, Yin W, Zhou X S, et al. Illumination normalization for face recognition and uneven background correction using total variation based image models[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 2: 532-539.
[150] Chen T, Wotao Yin, Xiang Sean Zhou, et al. Total variation models for variable lighting face recognition[J]. IEEE Transactions on,Pattern Analysis and Machine Intelligence, 2006, 28(9): 1519-1524.
[151] Wen Yi Zhao, Chellappa R. Illumination-insensitive face recognition using symmetric shape-from-shading[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2000. Proceedings, 2000, 1: 286-293.
[152] Sim T, Kanade T. Combining models and exemplars for face recognition: an illuminating example[C]. Proceedings of Workshop on Models versus Exemplars in Computer Vision, CVPR 2001. 2001.
[153] Georghiades A S, Kriegman D J, Belhurneur P N. Illumination cones for recognition under variable lighting: faces[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998: 52-58.
[154] Georghiades A S, Belhumeur P N, Kriegman D J. From few to many: illumination cone models for face recognition under variable lighting and pose[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 643-660.
[155] Basri R, Jacobs D W. Lambertian reflectance and linear subspaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(2): 218-233.
[156] Ramamoorthi R. Analytic PCA construction for theoretical analysis of lighting variability in images of a Lambertian object[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(10): 1322-1333.
[157] Lie Zhang, Samaras D. Face recognition under variable lighting using harmonic image exemplars[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, 1: 19-25.
[158] Cernuschi-Frias B, Cooper D B. 3-D Space Location and Orientation ParameterEstimation of Lambertian Spheres and Cylinders From a Single 2-D Image By Fitting Lines and Ellipses to Thresholded Data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(4): 430-441.
[159] Nayar S K, Ikeuchi K, Kanade T. Shape and reflectance from an image sequence generated using extended sources[C]. Proceedings of IEEE International Conference on Robotics and Automation, 1989: 28-35.
[160] Horn B.K.P. Robot Vision[M]. Cambridge,US: MIT Press, 1986.
[161] Ramamoorthi R, Hanrahan P. A signal-processing framework for inverse rendering[C]. Proceedings of the 28th annual conference on computer graphics and interactive techniques 2001: 117-128.
[162] Horn B.K.P. Determining lightness from an image[J]. Computer Graphics and Image Processing, 1974, 3(4): 277-299.
[163] Land E.H. and McCann J.J. Lightness and retinex theory[J]. Journal of the Optical Society of America, 1971, 61(1): 1-11.
[164] Zhang T, Fang B, Yuan Y, et al. Multiscale facial structure representation for face recognition under varying illumination[J]. Pattern Recognition, 2009, 42(2): 251-258.
[165] Donoho D L. Adaptive signal representations: How much is too much?[C]. Proceedings of IEEE-IMS Workshop on Information Theory and Statistics, 1994: 53.
[166] Donoho D L, Johnstone I M. Threshold selection for wavelet shrinkage of noisy data[C]. Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1994: 24-25.
[167] Qu Tianshu, Wang Shuxun, Chen Haihua, et al. Adaptive denoising based on wavelet thresholding method[C]. Proceedings of 6th International Conference on Signal Processing, 2002, 1: 120-123.
[168] Cheng Y, Hou Y, Zhao C, et al. Robust face recognition based on illumination invariant in nonsubsampled contourlet transform domain[J]. Neurocomputing, 2010, 73(10-12): 2217-2224.
[169] Shuicheng Yan, Dong Xu, Benyu Zhang, et al. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51.
[170] Cai D., He X., Zhou K., et al. Locality sensitive discriminant analysis[C]. Proceedings of Internationa Joint Conference on Artificial Intelligence(IJCAI'07), 2007: 708-713.
[171]焦李成,谭山.图像的多尺度几何分析:回顾和展望[J].电子学报, 2003, 31(12): 1975-1981.
[172] Do M N, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.
[173] Guoan Yang, Zhiqiang Tian, Chongyuan Bi, et al. An Image Coding Approach Using Wavelet-Based Adaptive Contourlet Transform[C]. Proceedings of International Joint Conference on Computational Sciences and Optimization, 2009, 1: 811-815.
[174] Li Ding, Han Chongzhao. Muti-focus Image Fusion Using Wavelet Based Contourlet Transform and Region[C]. Proceedings of International Conference on Information Management and Engineering, 2009: 90-93.
[175] Zhang Qidong, Wu Jianhua, Gao Liqun. Image retrieval based on contourlet transform and local binary patterns[C]. Proceedings of 4th IEEE Conference on Industrial Electronics and Applications, 2009: 2682-2685.
[176] Cai Lin-Bo, Ying Zi-Lu. A new approach of facial expression recognition based on Contourlet Transform[C]. Proceedings of International Conference on Wavelet Analysis and Pattern Recognition, 2009: 275-280.
[177] da Cunha A L, Zhou J, Do M N. Nonsubsampled contourilet transform: filter design and applications in denoising[C]. Proceedings of IEEE International Conference on Image Processing, 2005, 1: 749-752.
[178] Liang-liang Huang, Hong-zhi Wang, Bo Zhu. Adaptive Thresholds Algorithm of Image Denoising Based on Nonsubsampled Contourlet Transform[C]. Proceedings of International Conference on omputer Science and Software Engineering, 2008, 6: 209-212.
[179] Yuxin Ma, Jiancang Xie, Jungang Luo. Image Enhancement Based on Nonsubsampled Contourlet Transform[C]. Proceedings of the Fifth International Conference on Information Assurance and Security, 2009, 1: 31-34.
[180] Wang Hai, Feng Zijun. Image fusion algorithm based on nonsubsampled contourlettransform[C]. Proceedings of the 2nd International Conference on Future Computer and Communication (ICFCC), 2010, 1: 220-224.
[181]Leonid I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D, 1992, 60(1-4): 259-268.
[182]安高云.复杂条件人脸识别中若干关键问题的研究[D].北京:北京交通大学, 2008.
[183]陈天华.数字图像处理[M].第1版.北京:清华大学出版社, 2007.
[184] An G, Wu J, Ruan Q. An illumination normalization model for face recognition under varied lighting conditions[J]. Pattern Recognition Letters, 2010, 31(9): 1056-1067.
[185]吴亚东,孙世新,张红英等.一种基于图割的全变差图像去噪算法[J].电子学报, 2007, 35(2): 265-268.
[186] You-Wei Wen, Ng M K, Yu-Mei Huang. Efficient Total Variation Minimization Methods for Color Image Restoration[J]. IEEE Transactions on Image Processing, 2008, 17(11): 2081-2088.
[187] Kumar M, Dass S. A Total Variation-Based Algorithm for Pixel-Level Image Fusion[J]. IEEE Transactions on Image Processing, 2009, 18(9): 2137-2143.
[188] Chartrand R, Staneva V. Total variation regularisation of images corrupted by non-Gaussian noise using a quasi-Newton method[J]. IET Image Processing, 2008, 2(6): 295-303.
[189] Yin, W., Goldfarb, D., Osher, S. The total variation regularized L1 for multiscale decomposition[J]. Multiscale Modeling and Simulation, 2006, 6(1): 190–211.