特征提取和模式分类问题在人脸识别中的应用与研究
|
摘要
人脸识别从狭义的角度来说,就是将给定人脸上的特征检测并提取出来,与己知类别的样本特征进行比对从而进行识别的过程,主要涉及特征提取和模式分类两方面的内容。特征提取和模式分类都是人脸识别中的关键技术,其优劣直接影响到整个人脸识别系统的性能。人脸图像的维数通常都比较高,如果直接对原始人脸图像进行分析,算法的复杂度会相对较高,而且对计算机的硬件设备也要求比较高,因此特征提取是人脸识别领域中需要解决的一个关键问题。分类器应该根据所选特征的特点进行设计,从而得到更精确的结果。
目前,人脸识别已经得到了深入广泛的研究,一些相关技术成果也已经成功应用于各个领域,但其中依然面临着许多挑战,本文针对人脸识别中的特征提取和模式分类两个关键问题进行了相关的探讨,并在此基础上提出了几种更加有效的特征抽取方法和模式分类方法,通过将其与现阶段的主流方法进行比较,验证了本文方法的有效性。除了灰度图像人脸识别技术之外,本文对彩色人脸图像的识别也进行了一定的研究和分析。论文主要研究工作如下：
(1)对稀疏表示和字典学习方法进行了研究,提出了一种基于字典学习的核稀疏分类方法。
近年来稀疏表示方法和字典学习方法吸引了研究人员的广泛兴趣。受Metafaces方法的启发,提出了一种基于字典学习的核稀疏分类方法并成功应用于人脸识别。首先,借鉴Metafaces字典学习方法,在高维特征空间进行字典学习得到一组核变换后的字典基；然后,采用核技术将稀疏表示方法推广到高维空间得到核稀疏分类方法；最后,利用学习得到的字典基重构样本,并根据样本与重构样本之间的残差最小原则,利用核稀疏方法对人脸图像进行分类。在AR、ORL和Yale人脸数据库上的实验表明我们提出的方法具有良好的识别性能。
(2)研究了线性回归算法和基于Gabor小波的特征提取算法,利用核技巧,提出了一种Gabor核线性回归的方法(GKLRC)。
Gabor特征是在人脸识别中十分有效的识别特征,它对光照、表情变化比较鲁棒,并已在人脸识别领域得到成功应用。我们通过下采样方法,使用加强的Gabor特征与线性回归分类方法LRC相结合,针对人脸识别问题提出了Gabor核线性回归的方法。该方法不但继承了Gabor特征和线性回归方法的优点,同时通过核技术使得在非线性分布的样本上也能取得令人满意的结果。在几个标准人脸数据库上的实验结果表明我们提出的方法优于INaseem在2010年PAMI上提出的LRC线性回归等分类方法。
(3)研究了反卷积滤波特征提取算法,提出一种反卷积滤波学习和字典学习相结合的分类方法,并引入到人脸识别领域。
反卷积滤波通过对图像进行卷积滤波分解,可以得到图像不同层级信息,多用于图像表示。本文将反卷积滤波方法和字典学习方法相结合引入到人脸识别当中,通过实验,证实可以达到较好的识别性能。
(4)分析了彩色图像不同色彩空间数据,提出多彩色空间典型相关分析的彩色人脸图像识别算法。
目前很多研究表明,相对于灰度人脸图像,利用彩色图像的颜色信息能改进人脸图像的识别率。本文对二维的Contourlet变换特性进行了分析和讨论,利用Contourlet的多尺度、方向性和各向异性等特点,提出了一种基于Contourlet变换的彩色人脸识别算法。算法对原图进行Contourlet分解,对分解得到的低频和高频图像进行CCA分析得到投影系数,最后,采用决策级融合最近邻分类器对不同色彩空间的判别相结合完成彩色人脸识别。在对彩色人脸数据库AR的识别实验中,改进的算法优于Daubiches分解算法,而且在运行时间效率方面也有较强的优势。
From the narrow sense, face recognition is regarded as the process of extracting and detecting the features from the given human face, and then been compared to the known classes of samples, which is mainly related to two aspects of the feature extraction and classification. Feature extraction and classification are both the key techniques for face recognition, which directly impact on the performance of the entire system. However, the data sets collected from the real application problems are always with the characters of high-dimension. If processing on the original image directly, the algorithm has very high complexity, and the computer hardware performance is also a challenge, so in the field of face recognition feature extraction is the key to solve the problem. And the classifier should be selected according to the extracted features to get better performance.
At present, face recognition as the target, a variety of relevant technical methods have been successfully applied to various fields, but it still faces many challenges. Our work is focusing on the feature detection and classification on the face image recognition, on this basis we put forward some more effective feature extraction methods and classification methods. Compared with the current mainstream methods, we verified the validity of our methods. Except the gray face image recognition, we also research on the color face image recognition. The main research works and contributions of this thesis are summarized as follows:
(1) Inspired by sparse representation and dictionary learning, a dictionary learning based kernel sparse representation classification method is presented for face recognition.
Dictionary learning and sparse representation have attracted many researchers. Inspired by Metafaces, a dictionary learning based on kernel sparse classification method is presented for face recognition. First, the kernel dictionary bases are learned based on Metafaces framework. Second, a kernel sparse representation classifier is proposed by extending sparse representation classifier to high dimensional space via kernel functions. At last, we reconstruct the samples by kernel dictionary and classify the face image according to the residual. The experimental results on AR, ORL and Yale face databases show that the proposed method works well.
(2) Based on Gabor feature extraction method and linear regression classification method, we propose the method of Gabor kernel linear regression classification (GKLRC) for automatic face recognition.
Gabor features are very effective in face recognition, which is robust to illumination and expression variations and has been successfully used in face recognition area. Our paper addresses the method of Gabor kernel linear regression (GKLR) for automatic face recognition. Based on the assumption that the patterns from a single-object class lie on a linear subspace, linear regression (LR) for classification is proposed and it is a regularized least-squares method to develop the linear dependency between a probe image and class-specific galleries. We employ the kernel technique and Gabor features in the linear regression classification to perform analysis in a high-dimensional feature space to extract the significant nonlinear Gabor features which have been widely used in pattern recognition. We demonstrate the Gabor kernel linear regression method on the face database. Experimental results on several standard face databases demonstrate that the proposed algorithm significantly outperforms LRC and so on. Therefore, the new method should be considered in recognition of face.
(3) We have learned the Deconvolutional filters and the new method of face recognition with minimal convolution error measure oriented dictionary learning was proposed.
Face images are modeled using a deconvolutional decomposition of images under a sparsity constraint, which can capture the mid-level cues spontaneously arising from image data. We also introduce a well learned dictionary matrix to achieve better FR performance with less dictionary atoms. So minimal convolution error oriented dictionary learning is proposed in this paper and the experimental results on face image databases demonstrated the effectiveness of our method.
(4) We studied on different color spaces, and proposed the method of canonical correlation analysis on multiple color space.
As opposed to gray face images, the color information of face images can be applied to improve the face image recognition rate. Canonical correlation analysis on multiple color spaces was proposed in this paper. First, the source images were transformed into contourlet domain and get the LP and HP images. Then we used CCA to get the projection matrix. At last, nearest neighbor classifier is selected to perform face recognition on multiple color spaces. Experimental results on color AR face database show that the proposed algorithm is more effective and faster than the method of Daubiches transformation.
目前,人脸识别已经得到了深入广泛的研究,一些相关技术成果也已经成功应用于各个领域,但其中依然面临着许多挑战,本文针对人脸识别中的特征提取和模式分类两个关键问题进行了相关的探讨,并在此基础上提出了几种更加有效的特征抽取方法和模式分类方法,通过将其与现阶段的主流方法进行比较,验证了本文方法的有效性。除了灰度图像人脸识别技术之外,本文对彩色人脸图像的识别也进行了一定的研究和分析。论文主要研究工作如下：
(1)对稀疏表示和字典学习方法进行了研究,提出了一种基于字典学习的核稀疏分类方法。
近年来稀疏表示方法和字典学习方法吸引了研究人员的广泛兴趣。受Metafaces方法的启发,提出了一种基于字典学习的核稀疏分类方法并成功应用于人脸识别。首先,借鉴Metafaces字典学习方法,在高维特征空间进行字典学习得到一组核变换后的字典基；然后,采用核技术将稀疏表示方法推广到高维空间得到核稀疏分类方法；最后,利用学习得到的字典基重构样本,并根据样本与重构样本之间的残差最小原则,利用核稀疏方法对人脸图像进行分类。在AR、ORL和Yale人脸数据库上的实验表明我们提出的方法具有良好的识别性能。
(2)研究了线性回归算法和基于Gabor小波的特征提取算法,利用核技巧,提出了一种Gabor核线性回归的方法(GKLRC)。
Gabor特征是在人脸识别中十分有效的识别特征,它对光照、表情变化比较鲁棒,并已在人脸识别领域得到成功应用。我们通过下采样方法,使用加强的Gabor特征与线性回归分类方法LRC相结合,针对人脸识别问题提出了Gabor核线性回归的方法。该方法不但继承了Gabor特征和线性回归方法的优点,同时通过核技术使得在非线性分布的样本上也能取得令人满意的结果。在几个标准人脸数据库上的实验结果表明我们提出的方法优于INaseem在2010年PAMI上提出的LRC线性回归等分类方法。
(3)研究了反卷积滤波特征提取算法,提出一种反卷积滤波学习和字典学习相结合的分类方法,并引入到人脸识别领域。
反卷积滤波通过对图像进行卷积滤波分解,可以得到图像不同层级信息,多用于图像表示。本文将反卷积滤波方法和字典学习方法相结合引入到人脸识别当中,通过实验,证实可以达到较好的识别性能。
(4)分析了彩色图像不同色彩空间数据,提出多彩色空间典型相关分析的彩色人脸图像识别算法。
目前很多研究表明,相对于灰度人脸图像,利用彩色图像的颜色信息能改进人脸图像的识别率。本文对二维的Contourlet变换特性进行了分析和讨论,利用Contourlet的多尺度、方向性和各向异性等特点,提出了一种基于Contourlet变换的彩色人脸识别算法。算法对原图进行Contourlet分解,对分解得到的低频和高频图像进行CCA分析得到投影系数,最后,采用决策级融合最近邻分类器对不同色彩空间的判别相结合完成彩色人脸识别。在对彩色人脸数据库AR的识别实验中,改进的算法优于Daubiches分解算法,而且在运行时间效率方面也有较强的优势。
From the narrow sense, face recognition is regarded as the process of extracting and detecting the features from the given human face, and then been compared to the known classes of samples, which is mainly related to two aspects of the feature extraction and classification. Feature extraction and classification are both the key techniques for face recognition, which directly impact on the performance of the entire system. However, the data sets collected from the real application problems are always with the characters of high-dimension. If processing on the original image directly, the algorithm has very high complexity, and the computer hardware performance is also a challenge, so in the field of face recognition feature extraction is the key to solve the problem. And the classifier should be selected according to the extracted features to get better performance.
At present, face recognition as the target, a variety of relevant technical methods have been successfully applied to various fields, but it still faces many challenges. Our work is focusing on the feature detection and classification on the face image recognition, on this basis we put forward some more effective feature extraction methods and classification methods. Compared with the current mainstream methods, we verified the validity of our methods. Except the gray face image recognition, we also research on the color face image recognition. The main research works and contributions of this thesis are summarized as follows:
(1) Inspired by sparse representation and dictionary learning, a dictionary learning based kernel sparse representation classification method is presented for face recognition.
Dictionary learning and sparse representation have attracted many researchers. Inspired by Metafaces, a dictionary learning based on kernel sparse classification method is presented for face recognition. First, the kernel dictionary bases are learned based on Metafaces framework. Second, a kernel sparse representation classifier is proposed by extending sparse representation classifier to high dimensional space via kernel functions. At last, we reconstruct the samples by kernel dictionary and classify the face image according to the residual. The experimental results on AR, ORL and Yale face databases show that the proposed method works well.
(2) Based on Gabor feature extraction method and linear regression classification method, we propose the method of Gabor kernel linear regression classification (GKLRC) for automatic face recognition.
Gabor features are very effective in face recognition, which is robust to illumination and expression variations and has been successfully used in face recognition area. Our paper addresses the method of Gabor kernel linear regression (GKLR) for automatic face recognition. Based on the assumption that the patterns from a single-object class lie on a linear subspace, linear regression (LR) for classification is proposed and it is a regularized least-squares method to develop the linear dependency between a probe image and class-specific galleries. We employ the kernel technique and Gabor features in the linear regression classification to perform analysis in a high-dimensional feature space to extract the significant nonlinear Gabor features which have been widely used in pattern recognition. We demonstrate the Gabor kernel linear regression method on the face database. Experimental results on several standard face databases demonstrate that the proposed algorithm significantly outperforms LRC and so on. Therefore, the new method should be considered in recognition of face.
(3) We have learned the Deconvolutional filters and the new method of face recognition with minimal convolution error measure oriented dictionary learning was proposed.
Face images are modeled using a deconvolutional decomposition of images under a sparsity constraint, which can capture the mid-level cues spontaneously arising from image data. We also introduce a well learned dictionary matrix to achieve better FR performance with less dictionary atoms. So minimal convolution error oriented dictionary learning is proposed in this paper and the experimental results on face image databases demonstrated the effectiveness of our method.
(4) We studied on different color spaces, and proposed the method of canonical correlation analysis on multiple color space.
As opposed to gray face images, the color information of face images can be applied to improve the face image recognition rate. Canonical correlation analysis on multiple color spaces was proposed in this paper. First, the source images were transformed into contourlet domain and get the LP and HP images. Then we used CCA to get the projection matrix. At last, nearest neighbor classifier is selected to perform face recognition on multiple color spaces. Experimental results on color AR face database show that the proposed algorithm is more effective and faster than the method of Daubiches transformation.
引文
[1]S. Pankanti, R. M. Bolle, A. Jain. Biometrics:the future of identification. Computer.2000, 33(2):46-49.
[2]A. Sarnal and P. A. Iyengar. Automatic recognition and analysis of human faces and facial expressions:a survey. Pattern Recognition,1992,25(1):65-77.
[3]R. Chellappa, C. L. Wilson, S. Sirohey. Human and machine recognition of faces:a survey. Proc. IEEE,1995,83(5):705-740.
[4]A. Muron, J. Pospfsil. The human Iris structure and its usages [J]. Acta Universitatis Palackianae Olomucensis. Facultas Rerun Naturaliam, Mathematical:Physical,2000(39): 87-95.
[5]W. Kong, D. Zhang, W. Li. Palmprint feature extraction using 2D Gabor filters. Pattern Recognition,2003,36:2339-2347.
[6]J. You, W. Li, D. Zhang. Hierarchical palmprint identification via multiple feature extraction. Pattern Recognition,2002,35(4):847-859.
[7]L. Yuan, Z. C. Mu, Y. Zhang, et al. Ear recognition using improved non-negative matrix factorization [C]. International conference on pattern recognition,2006,4:501-504.
[8]P. Motlicek, F. ValenteValente. Application of out-of-language detection to spoken term detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Dallas, USA. IEEE,2010:5098-5101.
[9]J. Mantyjarvi, J, Koivumaki, P. Vuori. Keystroke recognition for virtual keyboard [A]. IEEE International Conference on Multimedia and Expo[C], Lausanne, Switzerland,2002: 429-432.
[10]生物识别技术新趋势：人脸识别及多重生物识别.http://www.cps.com.cn.
[11]A. F.Abate, M. Nappi, D. Riccio, G. Sabatino,2D and 3D face recognition:A survey. Pattern Recognition Letters,2007,28:1885-1906.
[12]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].中国图像图形学报,1999.4(10)：811-817.
[13]山世光.人脸识别中若干关键问题的研究[博士学位论文].北京：中国科学院研究生院.2004.
[14]A. J. Goldstein, L. D. Harmon, A. B. Lesk. Identification of human faces, Proceedings of the IEEE,1971,59(5):748-760.
[15]张翠平,苏光大.人脸识别技术综述.中国图像图形学报,2005,5(11)：885-894.
[16]H. Chan, W. W. Bledsoe. A man-machine facial recognition system:some preliminary results. Technical report, Panoramic Research Inc, Cal,1965.
[17]M. Turk and A. Pentland, Eigenfaces for recognition, J. Cogn. Neurosci,1991,3(1):71-86.
[18]A. Rosenfeld. Survey:Image analysis and computer vision:1994. Computer Vision and Image Understanding,1995,62(1):90-143.
[19]V. Bruce. Recognizing faces. London:Erlbaum,1988.
[20]M. J., Conlin, A rule-based high-level vision system. SPIE,1986:314-320.
[21]S. H. Jeng, et al, Facial feature detection using geometrical face model:an efficient approach. Pattern Recognition,1998,3(31):273-282.
[22]P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features[A]. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition[C], Hawaii, USA,2001:511-518.
[23]Taiping Zhang, Bin Fang, Yuan Yan Tang, Guanghui He, JingWen. Topology preserving non-negative matrix factorization for face recognition [J], IEEE Transactions on Image Processing,2008,17(4):574-584.
[24]S. Y. Lee, Y. K. Ham, R. H. Park. Recognition of human front faces using knowledge-based feature extraction and neuro-fuzzy algorithm. Pattern Recognition,1996,29(11):1863-1876.
[25]王华,李介谷.人脸斜视图像的特征抽取与恢复.上海交通大学学报,1997,31(1)：101-104.
[26]Y. Dai, Y. Nakano. Face-texture model based on SGLD and its application in face detection in a color scene. Pattern Recognition,1996,29(6):1007-1017.
[27]A. Lanitis, C. J. Taylor, T. F. Cootes. Automatic Interpretation and Coding of Face images Using Flexible Templates. IEEE Transactions on Pattern Analysis Machine Intelligence,1997, 19:743-756.
[28]T. Darrell et al. Active face tracking and pose estimation in an interactive room. Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1996,6:67-72.
[29]N. Intrator et al, Face recognition using a hybrid supervised/unsupervised neural network. Pattern Recognition Letters,1996,17(1):67-76.
[30]K. S. Yoon et al. Hybrid approaches to frontal view face recognition using the hidden Markov model and neural network. Pattern Recognition,1998,31(3):283-293.
[31]H. A. Rowley, S. Baluja, T. Kanade, Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1):23-40.
[32]H. A. Roudey. Neural network-based face detection. Proc. of Image Understanding Workshop,1996:725-735.
[33]B. S. Manjunath, R. Chellappa, A feature based approach to face recognition[C]. Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR,1992,6:373-378.
[34]高西奇,周洪祥,何振亚.基于小波变换的主元分析人脸图象识别.东南大学学报,1996,26(2)：137-141.
[35]T. Kohonen. Self-organization and associative memory [M]. Berlin:Springer,1988.
[36]R. Gottumukkal, K. Vijayan. Asari, An improved face recognition technique based on modular PCA approach, Pattern Recognition Letters,2004,25:429-436.
[37]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[J].电子学报,2002,(09).
[38]张生亮.大类别及少量训练样本的人脸识别问题研究.[博士学位论文].南京,南京理工大学：2005.
[39]Zhang Zhong, Ding Qun, Liu Mingliang, et al. A novel facial recognition method [J]. Journal of Communication and Computer,2010,7(1):57-62.
[40]R. E. Bellman. Adaptive Control Process:A Guided Tour [M], Princeton, New Jersey: Princeton University Press,1961.
[41]M. Bischel and A. Pentland, Human face recognition and face image set's topology. CVGIP: Image Understanding,1994,59(2):254-261.
[42]M. A. Turk, A. P. Pentland. Face processing:Models for recognition. Proc. Intelligent Robots and Computer Vision Ⅷ, SPIE,1989,192(1):22-32.
[43]Peter N. Belhumeur, J.P. Hespanha, D.J. Kriegman. Eigenfaces vs. Fisherfaces:Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell.1997,19(7): 711-720.
[44]A. Hyvarinen, E. Oja. Independent component analysis:algorithms and applications. Neural Networks,2000,13(4-5):411-430.
[45]C. J. Liu, H. Wechsler. Independent Component Analysis of Gabor features for face recognition. IEEE Transactions on Neural Networks,2003,14(4):919-928.
[46]C. Jutten, J.Herault. Independent component analysis versus PCA. Proceeding of European Signal Processing Conf,1988:643-646.
[47]P. Comon. Independent Component Analysis, a new concept? Signal Process,1994,36(3): 287-314.
[48]Z Jin, J Y Yang, Z S Hu et al. Face recognition based on the uncorrelated discriminant transformation. Pattern Recognition,2001,34:1405-1416.
[49]Z Jin, J. Y. Yang, Z. M. Tang, Z.S. Hu. A theorem on uncorrelated optimal discriminant vectors, Pattern Recognition,2001,34(10):2041-2047.
[50]J. Wright, A. Y. Yang, et.al. Robust face recognition via sparse representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227.
[51]HYu, J Yang. Direct LDA algorithm for high dimensional data with application to face recognition J]. Pattern Recognition,2001,34 (10):2067-2070.
[52]S. Muller, F. Wallhoff, F. Hulsken, et al. Facial expression recognition using pseudo 3-D hidden Markov models[C]. Proceedings of International Conference on Pattern Recognition. 2002:32-35.
[53]Y. Zhang, Q. Ji. Active and dynamic information fusion for facial expression understanding from image sequences [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(5):699-714.
[54]Xiao-ming Wang, Chang Huang, Xiao-ying Fang, Jin gao Liu.2DPCA vs.2DLDA:Face Recognition using Two-Dimensional Methol. InternationalConference on Artificial Intelligence and Computational Intelligence.2009:357-360.
[55]Noushath S, Hemantha Kumar G, Shivakumara P. (2D)2LDA:Anefficient approach for face recognition[J]. Pattern Recognition,2006,39 (7):1396-1400.
[56]P. J. Phillips, H. Wechsler, J. Huang, and P. J. Rauss, The FERET database and evaluation procedure for face recognition algorithms, Image vis. Comput.,1998,16(5):295-306.
[57]P. J. Phillips, M. Hyeonjoon, S. A. Rizvi, and P. J. Rauss, The FERET evaluation methodology for face recognition algorithms, IEEE Trans. Pattern Anal. Mach. Intell.,2000, 22(10):1090-1104.
[58]A. M. Martinez and R. Benavente, The AR face database, CVC, Barcelona, Spain, Tech. Rep.24,1998.
[59]T. Sim, S. Baker, and M. Bsat, The CMU pose, illumination, and expression database, IEEE Trans. Pattern Anal. Mach. Intell.2003,25(12):1615-1618.
[60]E.B.Bailliere, S.Bengio, F.Bimbot, M.Hamouz, J.Kittler, J.Mariethoz, J.Matas, K.Messer, V.Popovici, F.Poree, B.Ruiz, and J.P.Thiran, The BANCA database and evaluation protocol, Proc. Int. Conf. Audio Video-Based biometric person authentivation,2003:625-638.
[61]Y. Wang, Y. D. Jia, C. B. Hu, M. Turk. Fisher non-negative matrix factorization for learning local fearures. Asian Conference on Computer Vision,2004:806-811.
[62]Feng T, Li S Z, Shum H Y, Zhang H J. Local non-negative matrix factorization as a visual representation. Proceedings of the 2nd International Conference on Development and Learning[C]. IEEE,2002:178-183.
[63]Jian Yang. David Zhang, Alejandro F Frangi, et al. Two-dimensional PCA:a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26 (1):131-137.
[64]Jian Yang,Alejandro F Frangi. KPCA Plus LDA:A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2005,27 (2):230-243.
[65]Kwang In Kim, Keechul Jung, Hang Joon Kim. Face recognition using kernel principal components analysis. IEEE Signal Processing Letters,2002,9 (2):40-42.
[66]J B Tenenbaum, V de Silva, J C Langford. A global geometric frame work for nonlinear dimensionality reduction [J]. Science,2000,290(22):2319-2323.
[67]S T Roweis, L K Saul. Nonlinear dimensionality reduction by locally linear embedding [J]. Science,2000,290(22):2323-2326.
[68]X. He, S. Yan, Y. Hu, P. Niyogi, H. Zhang, Face recognition using Laplacianfaces [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3):328-340.
[69]J. Yang, D. Zhang, J.Y. Yang, B. Niu. Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(4):650-664.
[70]He Xiaofei, Niyogi Partha. Locality preserving projections. Advances in Neural Information Processing Systems 16. Cambridge:MIT Press,2004:327-334.
[71]Zhu J K, Vai M I, Mark P U. Gabor wavelets transform and extended nearest feature space classifier for face recognition. Third International Conference Proceedings on Image and Graphics.2004:246-249.
[72]桑农,张荣,张天序,一种改进的最小距离分类器的增量学习算法,模式识别与人工智能,2007,3：3736-3745.
[73]M.Aharon, M.Elad, and A.Bruckstein, K-SVD:An algorithm for designing overcomplete dictionaries for sparse representations, IEEE Trans. On Signal Process,2006,54:4311-4322.
[74]W. Zhao, R.Chellappa, A. Rosenfeld, P.J.Phillips, Face recognition:A literature survey[R], ACM Computing Surveys,2003:399-458.
[75]LI Wu-Jun, WANG Chong-Jun. ZHANG Wei, CHEN Shi-Fu. A Survey of Face Recognition [J]. Pattern Recognition and Artificial Intelligence.2006,19(1):58-66
[76]W. Di, L. Zhang, D. Zhang, Q. Pan, Studies on hyperspectral face recognition in visible spectrum with feature band selection[J], IEEE Trans. on System, Man and Cybernetics, Part A,2010,40(6):1354-1361
[77]B. Zhang, L. Zhang, D. Zhang, L. Shen, Directional binary code with application to PolyU near-infrared face database[J], Pattern Recognition Letters,2010:2337-2344.
[78]B.A. Olshausen and D.J. Field. Sparse coding of sensory inputs. Cur. Op. Neurobiology, 2004,14(4).
[79]浦剑,张军平.基于词典学习和稀疏表示的超分辨率方法[J].模式识别与人工智能,2010,23(3)：335-340.
[80]蔡泽民,赖剑煌.一种基于超完备字典学习的图像去噪方法[J].电子学报,2009,37(2)：347-350.
[81]Jean Jacques Fuchs, On sparse representations in arbitrary redundant bases [J], IEEE Transactions on Information Theory,2004,50(6):1341-1344.
[82]S. Mika, G. Ratsch, K. R. Muller. A mathematical programming approach to the kernel Fisher algorithm [J]. Advances in Neural Information Processing Systems,2001,13:591-597.
[83]V.Kyrki, J.K.Kamarainen, H.Kalviainen, Simple Gabor Feature Space for Invariant Object Recognition, Pattern Recognition Letters,2004,25(3):311-318.
[84]Y. Pang, Y. Yuan, and X. Li, Gabor-based region covariance matrices for face recognition, IEEE Trans. Circuits and Systems for Video Technology,2008,18:989-993.
[85]K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, B. Schollkopf, An introduction to kernel-based learning algorithms, IEEE Transactions on Neural Networks,2001,12(2):181-201.
[86]B. Scholkopf, C.J.C. Burges, A. Smola, Advances in Kernel Methods:Support Vector Learning, MIT Press, Cambridge, MA,1998.
[87]J. Shawe-Taylor, N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge,2004.
[88]Y. Cheng, K. Liu, J. Yang, Y. Zhuang, and N. Gu, Human Face Recognition Method Based on the Statistical Model of Small Sample Size, SPIE Proc. Intelligent Robots and Computer Vision X:Algorithms and Technology,1991:85-95.
[89]C. Liu, Gabor-based kernel pca with fractional power polynomial models for face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(5): 572-581.
[90]F. R. Bach, M. I. Jordan. Kernel independent component analysis [J]. Journal of Machine Learning Research,2003(3):1-48.
[91]Matthew D. Zeiler, Dilip Kirshnan, Graham W. Taylor, and Rob Fergus, Deconvolutional Networks, Computer Vision and Pattern Recognition,2010(6):13-18.
[92]M. Aharon, M. Elad, and A.M. Bruckstein. The K-SVD:An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation. IEEE Trans. On Signal Processing, 2006,54(11):4311-4322.
[93]Meng Yang, Lei Zhang, Jian Yang, David Zhang. Metaface learning for sparse representation based face recognition [A]. ICIP 2010.
[94]S.Gao, L.W.-H. Tsang, and L.-T. Chia, Kernel sparse representation for image classification and face recognition. Proceedings of the 11th European conference on Computer vision:Part Ⅳ,2010.
[95]J. Yang, C. Liu. Color image discriminant models and algorithms for face recognition [J], IEEE Transactions on Neural Networks,2008,19 (12):2088-2098.
[96]J H Lai, P C Yuen, G C Feng. Face recognition using holistic Fourier invariant features [J]. Pattern Recognition.2001,34(1):95-109.
[97]J-T Chien, C-C Wu. Discriminate wavelet faces and nearest feature classifiers for face recognition [J]. IEEE Trans. Pattern Anal. Mach. Intell.,2002,24(12):1644-1649.
[98]H Sellahewa, S Jassim. Wavelet-based face verification for constrained platforms [C]. Proc. SPIE Biometric Technol. Human Identification Ⅱ, Mar.2005,5779:173-183.
[99]H K Ekenel, B Sankur. Multiresolution face recognition [J]. Image Vis. Compute,2005,. 23(5):469-477.
[100]H Sellahewa. Wavelet-based automatic face recognition for constrained devices [D]. Ph.D. dissertation, Univ. Buckingham, Buckingham, U.K.,2006.
[101]H Sellahewa, S A Jassim. Illumination and expression invariant face recognition:Toward sample quality-based adaptive fusion [C]. Proc.2nd IEEE Int. Conf. Biometrics, Theory, Appl. Syst, Sep.2008:1-6.
[102]S A Jassim, H Sellahewa. Multi-stream face recognition on dedicated mobile devices for crime-fighting [C]. Proc. SPIE Opt. Photon. Counterterrorism Crime Fighting II,2006,9.
[103]Q. Sun, S. Zeng, Y. Liu, et al. A new method of feature fusion and its application in image recognition [J]. Pattern Recognition,2005.38:2437-2448.
[104]M N Do, M Vetterli. Contourlets [M]. Beyond Wavelets, J. Stoeckler and G.V. Welland, Eds. Academic Press, New York,2003.
[105]S. Yu. Direct blind channel equalization via the programmable canonical correlation analysis [J]. Signal Processing,2001,81:1715-1724.
[106]M. Borga. Canonical correlation:A tutorial [EB/OL]. (2001).http://www.imt.liu.Se-magnus/cca/2001.
[107]M. Borga. Learning multi-dimensional signal processing [D]. Linkping University, Sweden,1998.
[108]H. Hotelling. Relations between two sets of variants [J]. Biometrika,1936,28:321-377.
[109]S. Becker. Mutual information maximization:Models of cortical self organization [J]. Network:Computation in Neural Systems,1996,7:7-31.
[110]M N Do, M Vetterli. Contourlets:a directional multiresolution image representation [C]. IEEE International Conference on Image Processing. Rochester, NY:IEEE,2002:357-360.
[111]M N Do, M Vetterli. The Contourlet Transform:An Efficient Directional Multiresolution Image Representation [J], IEEE Transactions on Image Processing,2005,14(12):2091-2106.
[112]M N Do. Directional Multiresolution Image Representations [D], Ph.D. Thesis, Department of Communication Systems, Swiss Federal Institute of Technology Lausanne, Nov.2001.
[113]T. Melzer, M. Reiter, H. Bischof. Appearance models based on kernel canonical correlation analysis[J]. Pattern Recognition,2003,36:1961-1971.
[114]W. Zheng, X. Zhou, C. Zou, et al. Facial expression recognition using Kernel Canonical Correlation Analysis (KCCA) [J]. IEEE Trans on Neural Networks,2006,17(1):233-238.
[115]Yu X L, Wang X G, Liu B Y. Supervised kernel neighborhood preserving projection for radar target recognition [J]. Signal Processing,2008.88(9):2335-2339.
[116]Ye Guiyun, Liu Changzheng. Image compression based on fast lifting wavelet transform [J]. Networking and Digital Society,2009 (1):261-264.
[117]B V Ginneken, A F Frangi, et al. A non-linear gray-level appearance model improves active shape model segmentation [C].IEEE Workshop on Mathematical Models in Biomedical Image Analysis (MMBIA), Hawaii, USA,2001.
[118]S C Yan, C Liu, S Z Li, et al. Face alignment using texture-constrained active shape models [J]. Image and Vision Computing,2003,21 (1):69-75.
[119]顾华,苏光大,杜成.人脸关键特征点的自动定位[J],光电子·激光,2004,15(8).
[120]Jones C F. Color Face Recognition Using Quatemionic Gabor Filters [D]. Blacksburg Virginia State, USA:Virginia Polytechnic Institute and State University,2004.
[121]P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, IEEE CVPR,2000.
[122]P. Viola and M. Jones, Robust real-time face detection, International Journal of Computer Vision,2004,57(2),137-154.
[123]R. Lienhart, J. Maydt, An extended set of Haar-like features for rapid objection detection, IEEE ICIP,2002,900-903.
[124]B. Wu, H. Ai, C. Huang, et al., Fast rotation invariant multi-view face detection based on real adaboost, In Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR'04),2004.
[125]李英,赖剑煌,阮邦志.多模板ASM方法及其在人脸特征点检测中的应用[J],计算机研究和发展,2007,44(1)：133-140.
[126]王红光,梁德群,田原.基于角点检测、Zernike矩和神经网络的人脸特征点提取方法,西安交通大学学报,1999,33(12).
[127]范玉华,马建伟.ASM及其改进的人脸面部特征定位算法[J],计算机辅助设计与图形学学报,2007,11(11).
[128]罗亚篮,陈锻生.基于彩色Gabor特征的人脸识别技术[J],中国图象图形学报,2008,13(2).
[129]Q. Liu, R. Huang, H. Lu, S. Ma, Face recognition using kernel based fisher discriminant analysis, in:Proceedings of the International Conference on Automatic Face and Gesture Recognition, Washington,2002,5:197-201.
[130]K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun. What is the best multi-stage architecture for object recognition? In ICCV,2009.
[131]L. Zhu, Y. Chen, and A. L. Yuille. Learning a hierarchical deformable template for rapid deformable object parsing. PAMI, March 2009.
[132]S. Sardy, A. G. Bruce, and P. Tseng, Block coordinate relaxation methods for nonparametric wavelet denoising, J. Comput. Graph. Statist,2000,9:361-379.
[133]H. Zhang, A. Berg, M. Maire, and J. Malik. SVM-KNN:Discriminative nearest neighbor classification for visual category recognition. In Proc. CVPR,2006.
[134]Yinggang Xie, Jiaoli Kuang, Nan Ye, K-Means Clustering-Based Approach for Face Recognition, Journal of Information and Computational Science,2010,7(1):169-175.
[135]R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng. Self-taught learning. In NIPS Workshop on Learning when test and training inputs have different distributions,2006.
[136]I. Daubechies, M. Defrise, and C. Mol. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint [J]. Communications on Pure and Applied Math,2004,57: 1413-1457.
[137]R.R. Coifman,S. Lafon. Diffusion maps. Appl. Comput. Harmon. Anal.2006,21:5-30.
[138]Xiaohu Ma, Yanqi Tan, Yaying Zhao, Hongbo Tian, Face Recognition Based on Maximizing Margin and Discriminant Locality Preserving Projection [J], Journal of Information and Computational Science,2010,7(7):1551-1559.
[139]J. Wright, Y. Ma. J. Mairal, G. Sapiro, T. Huang, and S. Yan. Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE,2010,98(6):1031-1044.
[140]Qingshan Yang, Chengan Guo, Parallel Face Recognition Approach Based on LGBPHS with Homogeneous PC Cluster, Journal of Information and Computational Science,2010, 7(3):637-648.
[141]Y. Amit, D. Geman, and X. Fan. A coarse-to-fine strategy for multi-class shape detection. IEEE Trans. PAMI,2004.
[142]Yihu Yi; Daokui Qu; Fang Xu, A Face Recognition Algorithm Based on Global and Local Feature Fusion, Journal of Information and Computational Science,2010,7(4):925-931.
[143]J. Tropp and S. Wright. Computational methods for sparse solution of linear inverse problems. Proceedings of the IEEE,2010,98:948-958.
[144]Shen L L, Bai L, Fairhurst M, Gabor wavelets and general discriminant analysis for face identification and verification, Image and Vision Computing,2007,25 (5):553-631.
[145]Zhang YM, Ming Z X, Guo Y C, Face recognition base on low dimension Gabor feature using direct fractional-step LDA, Proceedings of International Conference on Computer Graphics,Imaging and Vision:New Trends-Washington DC, USA:IEEE Computer Society Press,2005:103-108.
[146]Luo Yalan, Chen Duansheng, Face recognition based on color Gabor features, Journal of Image and Graphics,2007,13 (2):242-246.
[147]Lai P L, Fyfe C, A neural network implementation of canonicalcorrelation analysis, Neural Network,1999,12 (10):1391-1397.
[148]G Baudat, F.E. Anouar, Generalized discriminant analysis using a kernel approach, Neural Computation,2000,12 (10):2385-2404.
[149]I. Naseem, R. Togneri, and M. Bennamoun, Linear Regression for Face Recognition, IEEE transactions on pattern analysis and machine intelligence PAMI,2010,32(11).
[150]X. Chen, W. Pan, J. Kwok, and J. Garbonell, Accelerated gradient method for multi-task sparse learning problem, In International Conference on Data Mining. IEEE,2009.3.
[151]P Shih, C Liu, Improving the face recognition grand challenge baseline performance using color configurations across color spaces, IEEE International Conference on Image Processing, ICIP 2006, October 8-11, Atlanta, GA.
[152]P Shin, Facial analysis in video:detection and recognition [D], Ph.D.Dissertation, New Jersey Institiute of Technology,2006.
[153]C Jones III, A L Abbott, Color face recognition by hypercomplex gabor analysis, Seventh International Conference on Automatic Face and Gesture Recognition (FGR 2006), April, 2006.
[154]J Yang, C Liu, A. Discriminant color space method for face representation and verification on a large-scale database, International Conference on Pattern Recognition 2008 (ICPR 2008), Tampa, Florida, USA,2008.
[155]Wang Shoujue, Wang Bainan, Analysis and theory of high dimensional space geometry for artificial neural networks, Acta Electronica Sinica,2002,30(1):1-4.
[156]Huang Xiaohua, Wang Chunmao, ZHENGWenming, An Information Fusion and Recognition Method for Color Face Images, Journal of Image and Graphics,2010,15 (3): 422-428.
[157]Wang Shoujue, Sun Hua, Mo Huayi, A new feature space transformation algorithm in color image and its application, Acta Electronica Sinica,2007,35(2):193-196.
[158]J. Yang, C. Liu, A general discriminant model for color face recognition[C], IEEE 11th International Conference on Computer Vision,2007,10:1-6.
[159]M H Yang, D J Kriegman, N Ahuja, Detecting faces in images:a survey, IEEE Trans.Pattern Analysis and Machine Intelligence,2002,24 (1):34-58.
[160]SarginM E, Erzin E, Yemez Y, et al, Multimodal speaker identification using canonical correlation analysis, Proceedings of IEEE International Conference on Acoustics,Speech and Signal Processingl Washington DC, USA:IEEE Computer Society,2006,1:613-616.
[161]Zhi-Kai Huang, Hui-Ming Huang, Yun-Min,Xie, Ling-Ying Hou, Face Recognition Using Model of the Scene Shape Unsupervised Image Segmentation Using EM Algorithm by Histogram, Journal of Computational Information Systems,2008,4:919-928.
[162]L. Wiskott, J. Fellous, N. Kruger, C. von der Malsburg, Face recognition by elastic bunch graph matching, IEEE Trans. PAMI,1997,19(7):775-669.
[163]IR Fasel, MS Bartlett, and JR Movellan, A comparison of Gabor filter methods for automatic detection of facial landmarks, In Proceedings of the 5th International Conference on Face and Gesture Recognition,2002.
[164]Jianzhong Wang, Baoxue Zhang etc, An adaptively weighted sub-pattern locality preserving projection for face recognition, Journal of Network and Computer Applications 2010(33): 323-332.
[165]M.Swain and D.Ballard, Color indexing, International Journal of Computer Vision,1991, 7(1):11-32.
[166]M. Turk and A. Pentland, Face Recognition Using Eigenfaces, Proc. IEEE Conf. on Computer Vision and Pattern Recognition,1991:586-591.
[167]M.S. Bartlett, J.R. Movellan, T.J. Sejnowski, Face recognition by independent component analysis, IEEE Transactions on Neural Networks 2002,13(6):1450-1464.
[168]M. Yang and L. Zhang, Gabor feature based sparse representation for face recognition with gabor occlusion dictionary, ECCV,2010.
[169]Su, Y., g. Shan, S., Chen, X.L., Gao, W, Hierarchical ensemble of global and local classifiers for face recognition, IEEE IP,2009,18:1885-1896.
[170]Zenglin Xu, Kaizhu Huang, A novel kernel-based maximum a posteriori classification method, Neural Networks,2009,22:977-987.
[171]Wencheng Wang, Faliang Chang, Guoqiang Zhang, Xiaoyan Sun, A Precise Eye Localization Method Based on Ratio Local Binary Pattern, JCIT:Journal of Convergence Information Technology,2011,6(1):126-134.
[172]Seyed Zeinolabedin Moussavi, Saeedreza Ehteram, Ali Sadr, The New Face Recognition Technique With the use of PCA and LDA, JCIT:Journal of Convergence Information Technology,2008,3(2):34-38.
[173]KeZheng Lin, Ying Xu, Yuan Zhong, Using Kernel Discriminant Analysis and 2DGabor Local Features Fusion for Face Recognition, JDCTA:International Journal of Digital Content Technology and its Applications,20104(8):232-241.
[174]Wankou Yang, Changyin Sun, Lei Zhang, A multi-manifold discriminant analysis method for image feature extraction, Pattern Recognition,2011,44(8):1649-1657.
[175]Wankou Yang, Jianguo Wang, Mingwu Ren, Jingyu Yang, Feature extraction based on laplacian bidirectional maximum margin criterion, Pattern Recognition,2009,42(11): 2327-2334.
[176]C. Schmid, R. Mohr, C. Bauckhage. Evaluation of interest point detectors [J]. International Journal of Computer Vision,2000,37(2):151-172.
[177]Ni Fei, Fu Zhuang, Cao Qi-xin, et al. Image processing method for eyes location based on segmentation texture [J].Sensors and Actuators A,2008,143:439-451.
[178]Law M H C, Jain A K. Incremental Nonlinear Dimensionality Reduction by Manifold Learning. IEEE Trans on Pattern Analysis and Machine Intelligence,2006,28(3):377-391.
[179]Choi Wai-Pak, Lam Kin-Man, Siu Wan-Chi. Extraction of the Euclidean Skeleton Based on a Connectivity Criterion [J]. Pattern Recognition,2003,36(3):721-729.
[180]孙达,唐降龙,刘家锋等.基于概率密度的兴趣点检测算法[J],自动化学报,2008,8,34(8)：854-860.
[181]Lee JA, Lendasse A, Verleysen M. Nonlinear projection with curvilinear distances: ISOMAP versus curvilinear distance analysis. Neurocomputing,2004,57(1):49-76.
[182]S. A. Coleman, D. Kerr, B. W. Scotney. Concurrent Edge and Corner Detection [J]. IEEE ICIP, San Antonio, USA,2007:273-276.
[183]Geng X, D C Zhang, Z H Zhou. Supervised nonlinear dimensionality reduction for visualization and classification [J]. IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics,2005,35(6):1098-1107.
[184]Li Ruifan, Wang Cong, Hao Hongwei, Tu Xuyan. Classifying Faces with Discriminate Isometric Feature Mapping [J]. MICAI 2005,3789:565-573.
[185]Camara Chavez G, Precioso F, Cord M. Shot Boundary Detection by a Hierarchical Supervised Approach [C]. Proc of the 14th International Conference on System s, Signals and Image Processing. Maribor, Slovenia,2007:197-200.
[186]Pati Y C, Rezaiifar R, Krishnaprasad P. S. Orthogonal Matching Pursuit:Recursive Function Approximation with Applications to Wavelet Decomposition [A]. Proc of the 27th Asilomar Conference on Signals, Systems and Computers. Pacific Grove, USA,1993,11(1):40-44.
[187]Allen Y. Yang, Yi Ma, John Wright, S. Shankar Sastry. Feature Selection in Face Recognition:A Sparse Representation Perspective [J]. IEEE TRANS. PAMI,2007,8:1-34.
[188]Cheng J, Liu Q S, Lu H Q, et al. Supervised kernel locality preserving projections for face recognition [J]. Neurocomputing,2005,67:443-449.
[189]S.J. Kim, K. Koh, M. Lustig, S. Boyd, D. Gorinevsky. A method for large-scale 11-regularized least squares [J]. IEEE Journal on Selected Topics in Signal Processing,2007, 1(4):606-617.
[190]张莉,周伟达,焦李成.核聚类算法[J].计算机学报,2002,25(6)：587-590.
[191]田捷,杨鑫.生物特征识别理论与应用[M].北京：清华大学出版社,2009.
[192]A.K.Jain, A. Ross, S. Prabhakar. An introduction to Biometric Recognition [C]. IEEE Transactions on Circuits and Systems for Video Technology,2004,14(1):4-20.
[193]R. Hietmeyer, Biometric identification promises fast and secure processing of airline passengers, The International Civil Aviation Organization Journal,2000,55(9):10-11.
[194]Jain A K. Biometric recognition:overview and recent advances. In:Proceedings of the 12th Iberoamerican Conference on Progress in Pattern Recognition, Image Analysis and Applications. Berlin, Germany:Springer-Verlag,2007:13-19.
[195]孙冬梅,裘正定.生物特征识别技术综述[J].电子学报,2001,,29(12A)：1744-1748.
[196]李武军,王崇骏,张炜等.人脸识别研究综述[J].模式识别与人工智能,2006,5(11)：885-894.
[197]J Yang, J Y Yang. Generalized KL transform based combined feature extraction. Pattern Recognition,2002,35(1):295-297.
[198]山世光,高文,陈熙霖.基于纹理分布和变形模板的面部特征提取[J].软件学报,2001,12(4).
[199]S.GKong et al., Recent advances in visual and infrared face recognition-a review, Computer Vision and Image Understanding,2005,97:103-135.
[200]Guodong Guo, Stan Z. Li, Kap Luk Chan, Support vector machines for face recognition, Image and Vision Computing,2001,19(2):631-638.
[201]康学雷,邵凌,张立明.一种基于肤色和模板的人脸检测方法[J].红外与毫米波学报,2000,19(3)：209-214.
[202]F. I. Parke, Computer generated animation of faces, in Proc. ACM Ann. Conf.,1972,1: 451-457.
[203]A.S.Tolla, A.H.EI-Baz, and A.A.EI-Harby. Face recognition:a literature review. International Journal of Signal Processing.2006,2(4):88-103.
[204]尹洪涛,付平,沙学军.基于DCT和线性判别分析的人脸识别[J].电子学报,2009,37(10)：2211-2214.
[205]P. Campadelli, R. Lanzarotti. Automatic facial feature extraction for face recognition [M]. Face Recognition, Kresimir Delac and Mislav Grgic,2007:558.
[206]Xiaoyuan Jing, Qian Liu, Chao Lan, et al. Holistic orthogonal analysis of discriminant transforms for color face recognition [C]. Proceedings of 2010 IEEE 17th-International Conference on Image Processing,2010:26-29.
[207]严云洋,郭志波,杨静宇.基于多尺度奇异值特征的人脸识别[J].清华大学学报,2008,,48(10)：1692-1696.
[208]S. H. Kim and H. S. Lee, Frontal face detection based on skin color segmentation and partial face classifier, ITC-CSCC 2009,7:541-544.
[209]罗思维,赵连伟.基于谱图理论的流形学习算法[J].计算机研究与发展,2006,41(7)：1173-1179.
[210]E. Pennec, S. Mallat. Sparse geometric image representation with bandelets. IEEE Trans Image Processing,2005,14(4):423-438.
[211]R. Gribonval. and P. Vandergheynst. On the exponential convergence of matching pursuits in quasi-incoherent dictionaries [J]. IEEE Trans Inform. Theory,2006,52(1):255-261.
[212]I. J. Cox, J. Ghosn, P. N. Yianilos. Feature-based face recognition using mixture-distance. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1996:209-216.
[213]Songcan Chen, Jun Liu, Zhihua Zhou. Making FLDA applicable to face recognition with one sample per person. Pattern Recognition,2004,37(7):1553-1555.
[214]P. L. Phillips. Support vector machines applied to face recognition. Advances in Neural information Proeessing Systems,1999,11:803-809.
[215]Y. Z. Goh, et al. Wavelet Based illumination invariant preprocessing in face recognition. 2008 Congress on Image and Signal Processing,421-425.
[216]S. C. Yan, X. F. He, Y. X. Hu, et al. Bayesian shape localization for face recognition using global and local textures. IEEE Transactions on Circuits and Systems for Video Technology, 2004,14(1):102-113.
[217]S. Z. Li, R. Chu, et al. Illumination invariant face recognition using near-infrared images. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(4):627-639.
[218]R. Lienhart, A. Kuranov, V. Pisarevsky, Empirical analysis of detection cascades of boosted classifiers for rapid object detection [A]. In Proceedings of the 25th German Pattern Recognition Symposium [C], Magdeburg, German,2003:297-304.
[219]程永清,庄永明,杨静宇.基于矩阵相似度的图象特征抽取和识别[J].计算机研究与发展,1992,29(11)：42-48.
[220]J.C.Bezdek, J.Keller, R.Krishnapuram. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Publishers, Dordrecht,1999.
[221]Yu K, Ji L, Zhang X G.. Kernel nearest-neighbor algorithm. Neural Processing Letters.2002. 15:147-156
[222]T. F. Cootes, C. J. Taylor. Locating Faces Using Statistical Feature Detectors[C]. Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition.1996.
[223]Y. Tian, T. Kanade, J. Cohn. Evaluation of Gabor wavelet-based facial action unit recognition in image sequences of increasing complexity[C]. Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition.2002:26-30.
[224]Kappop A, Qi Y, Picard R W. Fully automatic upper facial action recognition[C]. Proceedings of Analysis and Modeling of Faces and Gestures.2003:195-202.
[225]C. Liu, H. Y. Shum. Kullback-Leibler boosting [C]. Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition.2003:587-594.
[2]A. Sarnal and P. A. Iyengar. Automatic recognition and analysis of human faces and facial expressions:a survey. Pattern Recognition,1992,25(1):65-77.
[3]R. Chellappa, C. L. Wilson, S. Sirohey. Human and machine recognition of faces:a survey. Proc. IEEE,1995,83(5):705-740.
[4]A. Muron, J. Pospfsil. The human Iris structure and its usages [J]. Acta Universitatis Palackianae Olomucensis. Facultas Rerun Naturaliam, Mathematical:Physical,2000(39): 87-95.
[5]W. Kong, D. Zhang, W. Li. Palmprint feature extraction using 2D Gabor filters. Pattern Recognition,2003,36:2339-2347.
[6]J. You, W. Li, D. Zhang. Hierarchical palmprint identification via multiple feature extraction. Pattern Recognition,2002,35(4):847-859.
[7]L. Yuan, Z. C. Mu, Y. Zhang, et al. Ear recognition using improved non-negative matrix factorization [C]. International conference on pattern recognition,2006,4:501-504.
[8]P. Motlicek, F. ValenteValente. Application of out-of-language detection to spoken term detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Dallas, USA. IEEE,2010:5098-5101.
[9]J. Mantyjarvi, J, Koivumaki, P. Vuori. Keystroke recognition for virtual keyboard [A]. IEEE International Conference on Multimedia and Expo[C], Lausanne, Switzerland,2002: 429-432.
[10]生物识别技术新趋势：人脸识别及多重生物识别.http://www.cps.com.cn.
[11]A. F.Abate, M. Nappi, D. Riccio, G. Sabatino,2D and 3D face recognition:A survey. Pattern Recognition Letters,2007,28:1885-1906.
[12]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].中国图像图形学报,1999.4(10)：811-817.
[13]山世光.人脸识别中若干关键问题的研究[博士学位论文].北京：中国科学院研究生院.2004.
[14]A. J. Goldstein, L. D. Harmon, A. B. Lesk. Identification of human faces, Proceedings of the IEEE,1971,59(5):748-760.
[15]张翠平,苏光大.人脸识别技术综述.中国图像图形学报,2005,5(11)：885-894.
[16]H. Chan, W. W. Bledsoe. A man-machine facial recognition system:some preliminary results. Technical report, Panoramic Research Inc, Cal,1965.
[17]M. Turk and A. Pentland, Eigenfaces for recognition, J. Cogn. Neurosci,1991,3(1):71-86.
[18]A. Rosenfeld. Survey:Image analysis and computer vision:1994. Computer Vision and Image Understanding,1995,62(1):90-143.
[19]V. Bruce. Recognizing faces. London:Erlbaum,1988.
[20]M. J., Conlin, A rule-based high-level vision system. SPIE,1986:314-320.
[21]S. H. Jeng, et al, Facial feature detection using geometrical face model:an efficient approach. Pattern Recognition,1998,3(31):273-282.
[22]P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features[A]. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition[C], Hawaii, USA,2001:511-518.
[23]Taiping Zhang, Bin Fang, Yuan Yan Tang, Guanghui He, JingWen. Topology preserving non-negative matrix factorization for face recognition [J], IEEE Transactions on Image Processing,2008,17(4):574-584.
[24]S. Y. Lee, Y. K. Ham, R. H. Park. Recognition of human front faces using knowledge-based feature extraction and neuro-fuzzy algorithm. Pattern Recognition,1996,29(11):1863-1876.
[25]王华,李介谷.人脸斜视图像的特征抽取与恢复.上海交通大学学报,1997,31(1)：101-104.
[26]Y. Dai, Y. Nakano. Face-texture model based on SGLD and its application in face detection in a color scene. Pattern Recognition,1996,29(6):1007-1017.
[27]A. Lanitis, C. J. Taylor, T. F. Cootes. Automatic Interpretation and Coding of Face images Using Flexible Templates. IEEE Transactions on Pattern Analysis Machine Intelligence,1997, 19:743-756.
[28]T. Darrell et al. Active face tracking and pose estimation in an interactive room. Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1996,6:67-72.
[29]N. Intrator et al, Face recognition using a hybrid supervised/unsupervised neural network. Pattern Recognition Letters,1996,17(1):67-76.
[30]K. S. Yoon et al. Hybrid approaches to frontal view face recognition using the hidden Markov model and neural network. Pattern Recognition,1998,31(3):283-293.
[31]H. A. Rowley, S. Baluja, T. Kanade, Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1):23-40.
[32]H. A. Roudey. Neural network-based face detection. Proc. of Image Understanding Workshop,1996:725-735.
[33]B. S. Manjunath, R. Chellappa, A feature based approach to face recognition[C]. Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR,1992,6:373-378.
[34]高西奇,周洪祥,何振亚.基于小波变换的主元分析人脸图象识别.东南大学学报,1996,26(2)：137-141.
[35]T. Kohonen. Self-organization and associative memory [M]. Berlin:Springer,1988.
[36]R. Gottumukkal, K. Vijayan. Asari, An improved face recognition technique based on modular PCA approach, Pattern Recognition Letters,2004,25:429-436.
[37]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[J].电子学报,2002,(09).
[38]张生亮.大类别及少量训练样本的人脸识别问题研究.[博士学位论文].南京,南京理工大学：2005.
[39]Zhang Zhong, Ding Qun, Liu Mingliang, et al. A novel facial recognition method [J]. Journal of Communication and Computer,2010,7(1):57-62.
[40]R. E. Bellman. Adaptive Control Process:A Guided Tour [M], Princeton, New Jersey: Princeton University Press,1961.
[41]M. Bischel and A. Pentland, Human face recognition and face image set's topology. CVGIP: Image Understanding,1994,59(2):254-261.
[42]M. A. Turk, A. P. Pentland. Face processing:Models for recognition. Proc. Intelligent Robots and Computer Vision Ⅷ, SPIE,1989,192(1):22-32.
[43]Peter N. Belhumeur, J.P. Hespanha, D.J. Kriegman. Eigenfaces vs. Fisherfaces:Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell.1997,19(7): 711-720.
[44]A. Hyvarinen, E. Oja. Independent component analysis:algorithms and applications. Neural Networks,2000,13(4-5):411-430.
[45]C. J. Liu, H. Wechsler. Independent Component Analysis of Gabor features for face recognition. IEEE Transactions on Neural Networks,2003,14(4):919-928.
[46]C. Jutten, J.Herault. Independent component analysis versus PCA. Proceeding of European Signal Processing Conf,1988:643-646.
[47]P. Comon. Independent Component Analysis, a new concept? Signal Process,1994,36(3): 287-314.
[48]Z Jin, J Y Yang, Z S Hu et al. Face recognition based on the uncorrelated discriminant transformation. Pattern Recognition,2001,34:1405-1416.
[49]Z Jin, J. Y. Yang, Z. M. Tang, Z.S. Hu. A theorem on uncorrelated optimal discriminant vectors, Pattern Recognition,2001,34(10):2041-2047.
[50]J. Wright, A. Y. Yang, et.al. Robust face recognition via sparse representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227.
[51]HYu, J Yang. Direct LDA algorithm for high dimensional data with application to face recognition J]. Pattern Recognition,2001,34 (10):2067-2070.
[52]S. Muller, F. Wallhoff, F. Hulsken, et al. Facial expression recognition using pseudo 3-D hidden Markov models[C]. Proceedings of International Conference on Pattern Recognition. 2002:32-35.
[53]Y. Zhang, Q. Ji. Active and dynamic information fusion for facial expression understanding from image sequences [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(5):699-714.
[54]Xiao-ming Wang, Chang Huang, Xiao-ying Fang, Jin gao Liu.2DPCA vs.2DLDA:Face Recognition using Two-Dimensional Methol. InternationalConference on Artificial Intelligence and Computational Intelligence.2009:357-360.
[55]Noushath S, Hemantha Kumar G, Shivakumara P. (2D)2LDA:Anefficient approach for face recognition[J]. Pattern Recognition,2006,39 (7):1396-1400.
[56]P. J. Phillips, H. Wechsler, J. Huang, and P. J. Rauss, The FERET database and evaluation procedure for face recognition algorithms, Image vis. Comput.,1998,16(5):295-306.
[57]P. J. Phillips, M. Hyeonjoon, S. A. Rizvi, and P. J. Rauss, The FERET evaluation methodology for face recognition algorithms, IEEE Trans. Pattern Anal. Mach. Intell.,2000, 22(10):1090-1104.
[58]A. M. Martinez and R. Benavente, The AR face database, CVC, Barcelona, Spain, Tech. Rep.24,1998.
[59]T. Sim, S. Baker, and M. Bsat, The CMU pose, illumination, and expression database, IEEE Trans. Pattern Anal. Mach. Intell.2003,25(12):1615-1618.
[60]E.B.Bailliere, S.Bengio, F.Bimbot, M.Hamouz, J.Kittler, J.Mariethoz, J.Matas, K.Messer, V.Popovici, F.Poree, B.Ruiz, and J.P.Thiran, The BANCA database and evaluation protocol, Proc. Int. Conf. Audio Video-Based biometric person authentivation,2003:625-638.
[61]Y. Wang, Y. D. Jia, C. B. Hu, M. Turk. Fisher non-negative matrix factorization for learning local fearures. Asian Conference on Computer Vision,2004:806-811.
[62]Feng T, Li S Z, Shum H Y, Zhang H J. Local non-negative matrix factorization as a visual representation. Proceedings of the 2nd International Conference on Development and Learning[C]. IEEE,2002:178-183.
[63]Jian Yang. David Zhang, Alejandro F Frangi, et al. Two-dimensional PCA:a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26 (1):131-137.
[64]Jian Yang,Alejandro F Frangi. KPCA Plus LDA:A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2005,27 (2):230-243.
[65]Kwang In Kim, Keechul Jung, Hang Joon Kim. Face recognition using kernel principal components analysis. IEEE Signal Processing Letters,2002,9 (2):40-42.
[66]J B Tenenbaum, V de Silva, J C Langford. A global geometric frame work for nonlinear dimensionality reduction [J]. Science,2000,290(22):2319-2323.
[67]S T Roweis, L K Saul. Nonlinear dimensionality reduction by locally linear embedding [J]. Science,2000,290(22):2323-2326.
[68]X. He, S. Yan, Y. Hu, P. Niyogi, H. Zhang, Face recognition using Laplacianfaces [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3):328-340.
[69]J. Yang, D. Zhang, J.Y. Yang, B. Niu. Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(4):650-664.
[70]He Xiaofei, Niyogi Partha. Locality preserving projections. Advances in Neural Information Processing Systems 16. Cambridge:MIT Press,2004:327-334.
[71]Zhu J K, Vai M I, Mark P U. Gabor wavelets transform and extended nearest feature space classifier for face recognition. Third International Conference Proceedings on Image and Graphics.2004:246-249.
[72]桑农,张荣,张天序,一种改进的最小距离分类器的增量学习算法,模式识别与人工智能,2007,3：3736-3745.
[73]M.Aharon, M.Elad, and A.Bruckstein, K-SVD:An algorithm for designing overcomplete dictionaries for sparse representations, IEEE Trans. On Signal Process,2006,54:4311-4322.
[74]W. Zhao, R.Chellappa, A. Rosenfeld, P.J.Phillips, Face recognition:A literature survey[R], ACM Computing Surveys,2003:399-458.
[75]LI Wu-Jun, WANG Chong-Jun. ZHANG Wei, CHEN Shi-Fu. A Survey of Face Recognition [J]. Pattern Recognition and Artificial Intelligence.2006,19(1):58-66
[76]W. Di, L. Zhang, D. Zhang, Q. Pan, Studies on hyperspectral face recognition in visible spectrum with feature band selection[J], IEEE Trans. on System, Man and Cybernetics, Part A,2010,40(6):1354-1361
[77]B. Zhang, L. Zhang, D. Zhang, L. Shen, Directional binary code with application to PolyU near-infrared face database[J], Pattern Recognition Letters,2010:2337-2344.
[78]B.A. Olshausen and D.J. Field. Sparse coding of sensory inputs. Cur. Op. Neurobiology, 2004,14(4).
[79]浦剑,张军平.基于词典学习和稀疏表示的超分辨率方法[J].模式识别与人工智能,2010,23(3)：335-340.
[80]蔡泽民,赖剑煌.一种基于超完备字典学习的图像去噪方法[J].电子学报,2009,37(2)：347-350.
[81]Jean Jacques Fuchs, On sparse representations in arbitrary redundant bases [J], IEEE Transactions on Information Theory,2004,50(6):1341-1344.
[82]S. Mika, G. Ratsch, K. R. Muller. A mathematical programming approach to the kernel Fisher algorithm [J]. Advances in Neural Information Processing Systems,2001,13:591-597.
[83]V.Kyrki, J.K.Kamarainen, H.Kalviainen, Simple Gabor Feature Space for Invariant Object Recognition, Pattern Recognition Letters,2004,25(3):311-318.
[84]Y. Pang, Y. Yuan, and X. Li, Gabor-based region covariance matrices for face recognition, IEEE Trans. Circuits and Systems for Video Technology,2008,18:989-993.
[85]K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, B. Schollkopf, An introduction to kernel-based learning algorithms, IEEE Transactions on Neural Networks,2001,12(2):181-201.
[86]B. Scholkopf, C.J.C. Burges, A. Smola, Advances in Kernel Methods:Support Vector Learning, MIT Press, Cambridge, MA,1998.
[87]J. Shawe-Taylor, N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge,2004.
[88]Y. Cheng, K. Liu, J. Yang, Y. Zhuang, and N. Gu, Human Face Recognition Method Based on the Statistical Model of Small Sample Size, SPIE Proc. Intelligent Robots and Computer Vision X:Algorithms and Technology,1991:85-95.
[89]C. Liu, Gabor-based kernel pca with fractional power polynomial models for face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(5): 572-581.
[90]F. R. Bach, M. I. Jordan. Kernel independent component analysis [J]. Journal of Machine Learning Research,2003(3):1-48.
[91]Matthew D. Zeiler, Dilip Kirshnan, Graham W. Taylor, and Rob Fergus, Deconvolutional Networks, Computer Vision and Pattern Recognition,2010(6):13-18.
[92]M. Aharon, M. Elad, and A.M. Bruckstein. The K-SVD:An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation. IEEE Trans. On Signal Processing, 2006,54(11):4311-4322.
[93]Meng Yang, Lei Zhang, Jian Yang, David Zhang. Metaface learning for sparse representation based face recognition [A]. ICIP 2010.
[94]S.Gao, L.W.-H. Tsang, and L.-T. Chia, Kernel sparse representation for image classification and face recognition. Proceedings of the 11th European conference on Computer vision:Part Ⅳ,2010.
[95]J. Yang, C. Liu. Color image discriminant models and algorithms for face recognition [J], IEEE Transactions on Neural Networks,2008,19 (12):2088-2098.
[96]J H Lai, P C Yuen, G C Feng. Face recognition using holistic Fourier invariant features [J]. Pattern Recognition.2001,34(1):95-109.
[97]J-T Chien, C-C Wu. Discriminate wavelet faces and nearest feature classifiers for face recognition [J]. IEEE Trans. Pattern Anal. Mach. Intell.,2002,24(12):1644-1649.
[98]H Sellahewa, S Jassim. Wavelet-based face verification for constrained platforms [C]. Proc. SPIE Biometric Technol. Human Identification Ⅱ, Mar.2005,5779:173-183.
[99]H K Ekenel, B Sankur. Multiresolution face recognition [J]. Image Vis. Compute,2005,. 23(5):469-477.
[100]H Sellahewa. Wavelet-based automatic face recognition for constrained devices [D]. Ph.D. dissertation, Univ. Buckingham, Buckingham, U.K.,2006.
[101]H Sellahewa, S A Jassim. Illumination and expression invariant face recognition:Toward sample quality-based adaptive fusion [C]. Proc.2nd IEEE Int. Conf. Biometrics, Theory, Appl. Syst, Sep.2008:1-6.
[102]S A Jassim, H Sellahewa. Multi-stream face recognition on dedicated mobile devices for crime-fighting [C]. Proc. SPIE Opt. Photon. Counterterrorism Crime Fighting II,2006,9.
[103]Q. Sun, S. Zeng, Y. Liu, et al. A new method of feature fusion and its application in image recognition [J]. Pattern Recognition,2005.38:2437-2448.
[104]M N Do, M Vetterli. Contourlets [M]. Beyond Wavelets, J. Stoeckler and G.V. Welland, Eds. Academic Press, New York,2003.
[105]S. Yu. Direct blind channel equalization via the programmable canonical correlation analysis [J]. Signal Processing,2001,81:1715-1724.
[106]M. Borga. Canonical correlation:A tutorial [EB/OL]. (2001).http://www.imt.liu.Se-magnus/cca/2001.
[107]M. Borga. Learning multi-dimensional signal processing [D]. Linkping University, Sweden,1998.
[108]H. Hotelling. Relations between two sets of variants [J]. Biometrika,1936,28:321-377.
[109]S. Becker. Mutual information maximization:Models of cortical self organization [J]. Network:Computation in Neural Systems,1996,7:7-31.
[110]M N Do, M Vetterli. Contourlets:a directional multiresolution image representation [C]. IEEE International Conference on Image Processing. Rochester, NY:IEEE,2002:357-360.
[111]M N Do, M Vetterli. The Contourlet Transform:An Efficient Directional Multiresolution Image Representation [J], IEEE Transactions on Image Processing,2005,14(12):2091-2106.
[112]M N Do. Directional Multiresolution Image Representations [D], Ph.D. Thesis, Department of Communication Systems, Swiss Federal Institute of Technology Lausanne, Nov.2001.
[113]T. Melzer, M. Reiter, H. Bischof. Appearance models based on kernel canonical correlation analysis[J]. Pattern Recognition,2003,36:1961-1971.
[114]W. Zheng, X. Zhou, C. Zou, et al. Facial expression recognition using Kernel Canonical Correlation Analysis (KCCA) [J]. IEEE Trans on Neural Networks,2006,17(1):233-238.
[115]Yu X L, Wang X G, Liu B Y. Supervised kernel neighborhood preserving projection for radar target recognition [J]. Signal Processing,2008.88(9):2335-2339.
[116]Ye Guiyun, Liu Changzheng. Image compression based on fast lifting wavelet transform [J]. Networking and Digital Society,2009 (1):261-264.
[117]B V Ginneken, A F Frangi, et al. A non-linear gray-level appearance model improves active shape model segmentation [C].IEEE Workshop on Mathematical Models in Biomedical Image Analysis (MMBIA), Hawaii, USA,2001.
[118]S C Yan, C Liu, S Z Li, et al. Face alignment using texture-constrained active shape models [J]. Image and Vision Computing,2003,21 (1):69-75.
[119]顾华,苏光大,杜成.人脸关键特征点的自动定位[J],光电子·激光,2004,15(8).
[120]Jones C F. Color Face Recognition Using Quatemionic Gabor Filters [D]. Blacksburg Virginia State, USA:Virginia Polytechnic Institute and State University,2004.
[121]P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, IEEE CVPR,2000.
[122]P. Viola and M. Jones, Robust real-time face detection, International Journal of Computer Vision,2004,57(2),137-154.
[123]R. Lienhart, J. Maydt, An extended set of Haar-like features for rapid objection detection, IEEE ICIP,2002,900-903.
[124]B. Wu, H. Ai, C. Huang, et al., Fast rotation invariant multi-view face detection based on real adaboost, In Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR'04),2004.
[125]李英,赖剑煌,阮邦志.多模板ASM方法及其在人脸特征点检测中的应用[J],计算机研究和发展,2007,44(1)：133-140.
[126]王红光,梁德群,田原.基于角点检测、Zernike矩和神经网络的人脸特征点提取方法,西安交通大学学报,1999,33(12).
[127]范玉华,马建伟.ASM及其改进的人脸面部特征定位算法[J],计算机辅助设计与图形学学报,2007,11(11).
[128]罗亚篮,陈锻生.基于彩色Gabor特征的人脸识别技术[J],中国图象图形学报,2008,13(2).
[129]Q. Liu, R. Huang, H. Lu, S. Ma, Face recognition using kernel based fisher discriminant analysis, in:Proceedings of the International Conference on Automatic Face and Gesture Recognition, Washington,2002,5:197-201.
[130]K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun. What is the best multi-stage architecture for object recognition? In ICCV,2009.
[131]L. Zhu, Y. Chen, and A. L. Yuille. Learning a hierarchical deformable template for rapid deformable object parsing. PAMI, March 2009.
[132]S. Sardy, A. G. Bruce, and P. Tseng, Block coordinate relaxation methods for nonparametric wavelet denoising, J. Comput. Graph. Statist,2000,9:361-379.
[133]H. Zhang, A. Berg, M. Maire, and J. Malik. SVM-KNN:Discriminative nearest neighbor classification for visual category recognition. In Proc. CVPR,2006.
[134]Yinggang Xie, Jiaoli Kuang, Nan Ye, K-Means Clustering-Based Approach for Face Recognition, Journal of Information and Computational Science,2010,7(1):169-175.
[135]R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng. Self-taught learning. In NIPS Workshop on Learning when test and training inputs have different distributions,2006.
[136]I. Daubechies, M. Defrise, and C. Mol. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint [J]. Communications on Pure and Applied Math,2004,57: 1413-1457.
[137]R.R. Coifman,S. Lafon. Diffusion maps. Appl. Comput. Harmon. Anal.2006,21:5-30.
[138]Xiaohu Ma, Yanqi Tan, Yaying Zhao, Hongbo Tian, Face Recognition Based on Maximizing Margin and Discriminant Locality Preserving Projection [J], Journal of Information and Computational Science,2010,7(7):1551-1559.
[139]J. Wright, Y. Ma. J. Mairal, G. Sapiro, T. Huang, and S. Yan. Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE,2010,98(6):1031-1044.
[140]Qingshan Yang, Chengan Guo, Parallel Face Recognition Approach Based on LGBPHS with Homogeneous PC Cluster, Journal of Information and Computational Science,2010, 7(3):637-648.
[141]Y. Amit, D. Geman, and X. Fan. A coarse-to-fine strategy for multi-class shape detection. IEEE Trans. PAMI,2004.
[142]Yihu Yi; Daokui Qu; Fang Xu, A Face Recognition Algorithm Based on Global and Local Feature Fusion, Journal of Information and Computational Science,2010,7(4):925-931.
[143]J. Tropp and S. Wright. Computational methods for sparse solution of linear inverse problems. Proceedings of the IEEE,2010,98:948-958.
[144]Shen L L, Bai L, Fairhurst M, Gabor wavelets and general discriminant analysis for face identification and verification, Image and Vision Computing,2007,25 (5):553-631.
[145]Zhang YM, Ming Z X, Guo Y C, Face recognition base on low dimension Gabor feature using direct fractional-step LDA, Proceedings of International Conference on Computer Graphics,Imaging and Vision:New Trends-Washington DC, USA:IEEE Computer Society Press,2005:103-108.
[146]Luo Yalan, Chen Duansheng, Face recognition based on color Gabor features, Journal of Image and Graphics,2007,13 (2):242-246.
[147]Lai P L, Fyfe C, A neural network implementation of canonicalcorrelation analysis, Neural Network,1999,12 (10):1391-1397.
[148]G Baudat, F.E. Anouar, Generalized discriminant analysis using a kernel approach, Neural Computation,2000,12 (10):2385-2404.
[149]I. Naseem, R. Togneri, and M. Bennamoun, Linear Regression for Face Recognition, IEEE transactions on pattern analysis and machine intelligence PAMI,2010,32(11).
[150]X. Chen, W. Pan, J. Kwok, and J. Garbonell, Accelerated gradient method for multi-task sparse learning problem, In International Conference on Data Mining. IEEE,2009.3.
[151]P Shih, C Liu, Improving the face recognition grand challenge baseline performance using color configurations across color spaces, IEEE International Conference on Image Processing, ICIP 2006, October 8-11, Atlanta, GA.
[152]P Shin, Facial analysis in video:detection and recognition [D], Ph.D.Dissertation, New Jersey Institiute of Technology,2006.
[153]C Jones III, A L Abbott, Color face recognition by hypercomplex gabor analysis, Seventh International Conference on Automatic Face and Gesture Recognition (FGR 2006), April, 2006.
[154]J Yang, C Liu, A. Discriminant color space method for face representation and verification on a large-scale database, International Conference on Pattern Recognition 2008 (ICPR 2008), Tampa, Florida, USA,2008.
[155]Wang Shoujue, Wang Bainan, Analysis and theory of high dimensional space geometry for artificial neural networks, Acta Electronica Sinica,2002,30(1):1-4.
[156]Huang Xiaohua, Wang Chunmao, ZHENGWenming, An Information Fusion and Recognition Method for Color Face Images, Journal of Image and Graphics,2010,15 (3): 422-428.
[157]Wang Shoujue, Sun Hua, Mo Huayi, A new feature space transformation algorithm in color image and its application, Acta Electronica Sinica,2007,35(2):193-196.
[158]J. Yang, C. Liu, A general discriminant model for color face recognition[C], IEEE 11th International Conference on Computer Vision,2007,10:1-6.
[159]M H Yang, D J Kriegman, N Ahuja, Detecting faces in images:a survey, IEEE Trans.Pattern Analysis and Machine Intelligence,2002,24 (1):34-58.
[160]SarginM E, Erzin E, Yemez Y, et al, Multimodal speaker identification using canonical correlation analysis, Proceedings of IEEE International Conference on Acoustics,Speech and Signal Processingl Washington DC, USA:IEEE Computer Society,2006,1:613-616.
[161]Zhi-Kai Huang, Hui-Ming Huang, Yun-Min,Xie, Ling-Ying Hou, Face Recognition Using Model of the Scene Shape Unsupervised Image Segmentation Using EM Algorithm by Histogram, Journal of Computational Information Systems,2008,4:919-928.
[162]L. Wiskott, J. Fellous, N. Kruger, C. von der Malsburg, Face recognition by elastic bunch graph matching, IEEE Trans. PAMI,1997,19(7):775-669.
[163]IR Fasel, MS Bartlett, and JR Movellan, A comparison of Gabor filter methods for automatic detection of facial landmarks, In Proceedings of the 5th International Conference on Face and Gesture Recognition,2002.
[164]Jianzhong Wang, Baoxue Zhang etc, An adaptively weighted sub-pattern locality preserving projection for face recognition, Journal of Network and Computer Applications 2010(33): 323-332.
[165]M.Swain and D.Ballard, Color indexing, International Journal of Computer Vision,1991, 7(1):11-32.
[166]M. Turk and A. Pentland, Face Recognition Using Eigenfaces, Proc. IEEE Conf. on Computer Vision and Pattern Recognition,1991:586-591.
[167]M.S. Bartlett, J.R. Movellan, T.J. Sejnowski, Face recognition by independent component analysis, IEEE Transactions on Neural Networks 2002,13(6):1450-1464.
[168]M. Yang and L. Zhang, Gabor feature based sparse representation for face recognition with gabor occlusion dictionary, ECCV,2010.
[169]Su, Y., g. Shan, S., Chen, X.L., Gao, W, Hierarchical ensemble of global and local classifiers for face recognition, IEEE IP,2009,18:1885-1896.
[170]Zenglin Xu, Kaizhu Huang, A novel kernel-based maximum a posteriori classification method, Neural Networks,2009,22:977-987.
[171]Wencheng Wang, Faliang Chang, Guoqiang Zhang, Xiaoyan Sun, A Precise Eye Localization Method Based on Ratio Local Binary Pattern, JCIT:Journal of Convergence Information Technology,2011,6(1):126-134.
[172]Seyed Zeinolabedin Moussavi, Saeedreza Ehteram, Ali Sadr, The New Face Recognition Technique With the use of PCA and LDA, JCIT:Journal of Convergence Information Technology,2008,3(2):34-38.
[173]KeZheng Lin, Ying Xu, Yuan Zhong, Using Kernel Discriminant Analysis and 2DGabor Local Features Fusion for Face Recognition, JDCTA:International Journal of Digital Content Technology and its Applications,20104(8):232-241.
[174]Wankou Yang, Changyin Sun, Lei Zhang, A multi-manifold discriminant analysis method for image feature extraction, Pattern Recognition,2011,44(8):1649-1657.
[175]Wankou Yang, Jianguo Wang, Mingwu Ren, Jingyu Yang, Feature extraction based on laplacian bidirectional maximum margin criterion, Pattern Recognition,2009,42(11): 2327-2334.
[176]C. Schmid, R. Mohr, C. Bauckhage. Evaluation of interest point detectors [J]. International Journal of Computer Vision,2000,37(2):151-172.
[177]Ni Fei, Fu Zhuang, Cao Qi-xin, et al. Image processing method for eyes location based on segmentation texture [J].Sensors and Actuators A,2008,143:439-451.
[178]Law M H C, Jain A K. Incremental Nonlinear Dimensionality Reduction by Manifold Learning. IEEE Trans on Pattern Analysis and Machine Intelligence,2006,28(3):377-391.
[179]Choi Wai-Pak, Lam Kin-Man, Siu Wan-Chi. Extraction of the Euclidean Skeleton Based on a Connectivity Criterion [J]. Pattern Recognition,2003,36(3):721-729.
[180]孙达,唐降龙,刘家锋等.基于概率密度的兴趣点检测算法[J],自动化学报,2008,8,34(8)：854-860.
[181]Lee JA, Lendasse A, Verleysen M. Nonlinear projection with curvilinear distances: ISOMAP versus curvilinear distance analysis. Neurocomputing,2004,57(1):49-76.
[182]S. A. Coleman, D. Kerr, B. W. Scotney. Concurrent Edge and Corner Detection [J]. IEEE ICIP, San Antonio, USA,2007:273-276.
[183]Geng X, D C Zhang, Z H Zhou. Supervised nonlinear dimensionality reduction for visualization and classification [J]. IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics,2005,35(6):1098-1107.
[184]Li Ruifan, Wang Cong, Hao Hongwei, Tu Xuyan. Classifying Faces with Discriminate Isometric Feature Mapping [J]. MICAI 2005,3789:565-573.
[185]Camara Chavez G, Precioso F, Cord M. Shot Boundary Detection by a Hierarchical Supervised Approach [C]. Proc of the 14th International Conference on System s, Signals and Image Processing. Maribor, Slovenia,2007:197-200.
[186]Pati Y C, Rezaiifar R, Krishnaprasad P. S. Orthogonal Matching Pursuit:Recursive Function Approximation with Applications to Wavelet Decomposition [A]. Proc of the 27th Asilomar Conference on Signals, Systems and Computers. Pacific Grove, USA,1993,11(1):40-44.
[187]Allen Y. Yang, Yi Ma, John Wright, S. Shankar Sastry. Feature Selection in Face Recognition:A Sparse Representation Perspective [J]. IEEE TRANS. PAMI,2007,8:1-34.
[188]Cheng J, Liu Q S, Lu H Q, et al. Supervised kernel locality preserving projections for face recognition [J]. Neurocomputing,2005,67:443-449.
[189]S.J. Kim, K. Koh, M. Lustig, S. Boyd, D. Gorinevsky. A method for large-scale 11-regularized least squares [J]. IEEE Journal on Selected Topics in Signal Processing,2007, 1(4):606-617.
[190]张莉,周伟达,焦李成.核聚类算法[J].计算机学报,2002,25(6)：587-590.
[191]田捷,杨鑫.生物特征识别理论与应用[M].北京：清华大学出版社,2009.
[192]A.K.Jain, A. Ross, S. Prabhakar. An introduction to Biometric Recognition [C]. IEEE Transactions on Circuits and Systems for Video Technology,2004,14(1):4-20.
[193]R. Hietmeyer, Biometric identification promises fast and secure processing of airline passengers, The International Civil Aviation Organization Journal,2000,55(9):10-11.
[194]Jain A K. Biometric recognition:overview and recent advances. In:Proceedings of the 12th Iberoamerican Conference on Progress in Pattern Recognition, Image Analysis and Applications. Berlin, Germany:Springer-Verlag,2007:13-19.
[195]孙冬梅,裘正定.生物特征识别技术综述[J].电子学报,2001,,29(12A)：1744-1748.
[196]李武军,王崇骏,张炜等.人脸识别研究综述[J].模式识别与人工智能,2006,5(11)：885-894.
[197]J Yang, J Y Yang. Generalized KL transform based combined feature extraction. Pattern Recognition,2002,35(1):295-297.
[198]山世光,高文,陈熙霖.基于纹理分布和变形模板的面部特征提取[J].软件学报,2001,12(4).
[199]S.GKong et al., Recent advances in visual and infrared face recognition-a review, Computer Vision and Image Understanding,2005,97:103-135.
[200]Guodong Guo, Stan Z. Li, Kap Luk Chan, Support vector machines for face recognition, Image and Vision Computing,2001,19(2):631-638.
[201]康学雷,邵凌,张立明.一种基于肤色和模板的人脸检测方法[J].红外与毫米波学报,2000,19(3)：209-214.
[202]F. I. Parke, Computer generated animation of faces, in Proc. ACM Ann. Conf.,1972,1: 451-457.
[203]A.S.Tolla, A.H.EI-Baz, and A.A.EI-Harby. Face recognition:a literature review. International Journal of Signal Processing.2006,2(4):88-103.
[204]尹洪涛,付平,沙学军.基于DCT和线性判别分析的人脸识别[J].电子学报,2009,37(10)：2211-2214.
[205]P. Campadelli, R. Lanzarotti. Automatic facial feature extraction for face recognition [M]. Face Recognition, Kresimir Delac and Mislav Grgic,2007:558.
[206]Xiaoyuan Jing, Qian Liu, Chao Lan, et al. Holistic orthogonal analysis of discriminant transforms for color face recognition [C]. Proceedings of 2010 IEEE 17th-International Conference on Image Processing,2010:26-29.
[207]严云洋,郭志波,杨静宇.基于多尺度奇异值特征的人脸识别[J].清华大学学报,2008,,48(10)：1692-1696.
[208]S. H. Kim and H. S. Lee, Frontal face detection based on skin color segmentation and partial face classifier, ITC-CSCC 2009,7:541-544.
[209]罗思维,赵连伟.基于谱图理论的流形学习算法[J].计算机研究与发展,2006,41(7)：1173-1179.
[210]E. Pennec, S. Mallat. Sparse geometric image representation with bandelets. IEEE Trans Image Processing,2005,14(4):423-438.
[211]R. Gribonval. and P. Vandergheynst. On the exponential convergence of matching pursuits in quasi-incoherent dictionaries [J]. IEEE Trans Inform. Theory,2006,52(1):255-261.
[212]I. J. Cox, J. Ghosn, P. N. Yianilos. Feature-based face recognition using mixture-distance. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1996:209-216.
[213]Songcan Chen, Jun Liu, Zhihua Zhou. Making FLDA applicable to face recognition with one sample per person. Pattern Recognition,2004,37(7):1553-1555.
[214]P. L. Phillips. Support vector machines applied to face recognition. Advances in Neural information Proeessing Systems,1999,11:803-809.
[215]Y. Z. Goh, et al. Wavelet Based illumination invariant preprocessing in face recognition. 2008 Congress on Image and Signal Processing,421-425.
[216]S. C. Yan, X. F. He, Y. X. Hu, et al. Bayesian shape localization for face recognition using global and local textures. IEEE Transactions on Circuits and Systems for Video Technology, 2004,14(1):102-113.
[217]S. Z. Li, R. Chu, et al. Illumination invariant face recognition using near-infrared images. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(4):627-639.
[218]R. Lienhart, A. Kuranov, V. Pisarevsky, Empirical analysis of detection cascades of boosted classifiers for rapid object detection [A]. In Proceedings of the 25th German Pattern Recognition Symposium [C], Magdeburg, German,2003:297-304.
[219]程永清,庄永明,杨静宇.基于矩阵相似度的图象特征抽取和识别[J].计算机研究与发展,1992,29(11)：42-48.
[220]J.C.Bezdek, J.Keller, R.Krishnapuram. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Publishers, Dordrecht,1999.
[221]Yu K, Ji L, Zhang X G.. Kernel nearest-neighbor algorithm. Neural Processing Letters.2002. 15:147-156
[222]T. F. Cootes, C. J. Taylor. Locating Faces Using Statistical Feature Detectors[C]. Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition.1996.
[223]Y. Tian, T. Kanade, J. Cohn. Evaluation of Gabor wavelet-based facial action unit recognition in image sequences of increasing complexity[C]. Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition.2002:26-30.
[224]Kappop A, Qi Y, Picard R W. Fully automatic upper facial action recognition[C]. Proceedings of Analysis and Modeling of Faces and Gestures.2003:195-202.
[225]C. Liu, H. Y. Shum. Kullback-Leibler boosting [C]. Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition.2003:587-594.