复杂背景下快速人脸检测与识别
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摘要
人脸的检测与识别的研究涉及模式识别、图像处理、生理学、心理学、认知科学等许多领域，与基于其它生物特征的身份鉴别方法以及计算机人机交互领域都有密切联系。由于各领域对人脸检测与识别技术的需求日益迫切(特别是安全领域)，这使得人脸检测与识别技术的研究已经成为当今的热点问题之一。
本文主要研究了复杂背景下快速人脸检测与识别的基本理论和关键技术。重点讨论了人脸表示、分类器的设计和复杂背景下快速人脸检测与识别系统架构问题。论文的主要研究工作和主要成果包括以下几个方面。
首先，研究了不同彩色空间下不同的肤色模型。通过实验表明，在HSI彩色空间的简单高斯模型和线性模型比较适合于复杂背景下的快速、准确的人脸检测。通过肤色检测能快速去除复杂背景，缩小检测的范围，为复杂背景下快速准确的人脸检测奠定了基础。但是肤色模型易受光照等外界因素影响，本文结合K-L变换提高了肤色检测的鲁棒性。
其次，研究了人脸表示及其快速算法。通过对PCA、LDA、Gabor和Like-Harr人脸表示方法的研究，分别为人脸检测和识别选取了较为稳定而准确的人脸表示；初步尝试了利用AdaBoost分类器进行特征选择从而消除冗余特征，并且提出了采用级联表示方法快速表征人脸，从而实现由粗到精、由简单到复杂的快速人脸表示，这样既提高了人脸的检测和识别的速度，还有利于检测率和识别率的提高。
随后，从三个方面研究了分类器的构造。首先研究了在人脸检测和识别中常用的分类器，比如符号函数、最近邻、神经网络、SVM、Adaboost等，选择了适合于人脸检测和识别的分类器，并提出了结合PCA特征和RBF进行人脸姿态的判别方法：其次研究了具有特征选择功能的分类器发计，这为人脸的级联表示提供了条什，也为快速准确的人脸检测和识别提供了可能；最后，对组合分类器设计进行了研究，提出了适于复杂背景下快速人脸检测和识别的有效分类器设计方案，这使得人脸检测和识别能够快速剔除不感兴趣区域，为复杂背景下实时人脸检测和大型人脸库的快速识别提供了可能。
最后，结合人脸的级联表示和组合分类器的设计思想提出了一个复杂背景下快速的人脸检测和识别系统架构，并实现了该系统。该系统实现了在复杂背景下快速的人脸检测和识别，在P4 1.6的机子上检测和识别的总耗时160ms，约为6帧／秒。实验结果验证了该系统的实时性、准确性和鲁棒性。
Face detection and recognition research is related to the fields of pattern recognition, image processing, physiology, cognitive science and so on. At the same time, it is tied with other researches such as Biometric Authentication and Human-Computer Interaction. It has become one of the key issues because of its practical essentiality in many aspects, especially in the field of security.
In the thesis, the fundamental theories and the key technologies of fast face detection and recognition from images with complex background are mainly researched. The topics such as face representation, classifier design and the design of Face Detection and Recognition System for images with complex background are discussed in details. Research work includes the following contents.
Firstly, different skin models are researched in four different color spaces. The experiments show that Linear Model or Single Gaussian Model in HIS color space can get better results under complex background. With the help of skin models, it can get rid of the complexity of the background, confine the range of valid areas and lay a basis for fast and accurate face detection. However, it is easily affected by illumination. The robustness is improved by considering the K.-L transformation.
Secondly, the face representation and its fast algorithms are summarized. There are some research approaches such as PCA, LDA, Gabor and Like-Harr etc to get features, and some stable and efficient facial features are obtained by these means. The method of selecting features by Adaboost is used to discard redundant features. Cascade representation is adopted to represent face rapidly to improve the fast face representation from coarse to fine, from simple to complex, and to improve the speed and accuracy of the processing.
Subsequently, designs of the classifiers are described in three aspects. First of all, some common classifiers are firstly discussed such as SF(Sign Function), NN(Nearest Neighbour),
Adaboost, SVM(Support Vector Machine), ANN(Artificial Neural Network) etc. Then appropriate classifiers for face detection and recognition are selected. After that, the method of detecting poses of faces by RBF and PCA is proposed. The classifier with ability of feature selection is studied to prepare for face cascade representation and to make it possible to detect and recognize face fast and accurately. Finally, construction of an array of classifiers is researched, and an effective method to design classifiers of fast face detection and recognition with complex background is presented, which is able to radically discard redundant areas and realize a robust real-time face detection designed for complex background and recognition system with large face database.
Finally, a fast face detection and recognition system for images with complex background is proposed and implemented by combining face cascade representation and classifier design. The model system accomplishes face detection and recognition from images with complex background robustly and accurately. When applied, the system runs at 6 frames per second in P4
1.6G.
本文主要研究了复杂背景下快速人脸检测与识别的基本理论和关键技术。重点讨论了人脸表示、分类器的设计和复杂背景下快速人脸检测与识别系统架构问题。论文的主要研究工作和主要成果包括以下几个方面。
首先，研究了不同彩色空间下不同的肤色模型。通过实验表明，在HSI彩色空间的简单高斯模型和线性模型比较适合于复杂背景下的快速、准确的人脸检测。通过肤色检测能快速去除复杂背景，缩小检测的范围，为复杂背景下快速准确的人脸检测奠定了基础。但是肤色模型易受光照等外界因素影响，本文结合K-L变换提高了肤色检测的鲁棒性。
其次，研究了人脸表示及其快速算法。通过对PCA、LDA、Gabor和Like-Harr人脸表示方法的研究，分别为人脸检测和识别选取了较为稳定而准确的人脸表示；初步尝试了利用AdaBoost分类器进行特征选择从而消除冗余特征，并且提出了采用级联表示方法快速表征人脸，从而实现由粗到精、由简单到复杂的快速人脸表示，这样既提高了人脸的检测和识别的速度，还有利于检测率和识别率的提高。
随后，从三个方面研究了分类器的构造。首先研究了在人脸检测和识别中常用的分类器，比如符号函数、最近邻、神经网络、SVM、Adaboost等，选择了适合于人脸检测和识别的分类器，并提出了结合PCA特征和RBF进行人脸姿态的判别方法：其次研究了具有特征选择功能的分类器发计，这为人脸的级联表示提供了条什，也为快速准确的人脸检测和识别提供了可能；最后，对组合分类器设计进行了研究，提出了适于复杂背景下快速人脸检测和识别的有效分类器设计方案，这使得人脸检测和识别能够快速剔除不感兴趣区域，为复杂背景下实时人脸检测和大型人脸库的快速识别提供了可能。
最后，结合人脸的级联表示和组合分类器的设计思想提出了一个复杂背景下快速的人脸检测和识别系统架构，并实现了该系统。该系统实现了在复杂背景下快速的人脸检测和识别，在P4 1.6的机子上检测和识别的总耗时160ms，约为6帧／秒。实验结果验证了该系统的实时性、准确性和鲁棒性。
Face detection and recognition research is related to the fields of pattern recognition, image processing, physiology, cognitive science and so on. At the same time, it is tied with other researches such as Biometric Authentication and Human-Computer Interaction. It has become one of the key issues because of its practical essentiality in many aspects, especially in the field of security.
In the thesis, the fundamental theories and the key technologies of fast face detection and recognition from images with complex background are mainly researched. The topics such as face representation, classifier design and the design of Face Detection and Recognition System for images with complex background are discussed in details. Research work includes the following contents.
Firstly, different skin models are researched in four different color spaces. The experiments show that Linear Model or Single Gaussian Model in HIS color space can get better results under complex background. With the help of skin models, it can get rid of the complexity of the background, confine the range of valid areas and lay a basis for fast and accurate face detection. However, it is easily affected by illumination. The robustness is improved by considering the K.-L transformation.
Secondly, the face representation and its fast algorithms are summarized. There are some research approaches such as PCA, LDA, Gabor and Like-Harr etc to get features, and some stable and efficient facial features are obtained by these means. The method of selecting features by Adaboost is used to discard redundant features. Cascade representation is adopted to represent face rapidly to improve the fast face representation from coarse to fine, from simple to complex, and to improve the speed and accuracy of the processing.
Subsequently, designs of the classifiers are described in three aspects. First of all, some common classifiers are firstly discussed such as SF(Sign Function), NN(Nearest Neighbour),
Adaboost, SVM(Support Vector Machine), ANN(Artificial Neural Network) etc. Then appropriate classifiers for face detection and recognition are selected. After that, the method of detecting poses of faces by RBF and PCA is proposed. The classifier with ability of feature selection is studied to prepare for face cascade representation and to make it possible to detect and recognize face fast and accurately. Finally, construction of an array of classifiers is researched, and an effective method to design classifiers of fast face detection and recognition with complex background is presented, which is able to radically discard redundant areas and realize a robust real-time face detection designed for complex background and recognition system with large face database.
Finally, a fast face detection and recognition system for images with complex background is proposed and implemented by combining face cascade representation and classifier design. The model system accomplishes face detection and recognition from images with complex background robustly and accurately. When applied, the system runs at 6 frames per second in P4
1.6G.
引文
[1] William A.Barrett," A Survey of Face Recognition Algorithms and Testing Results", National Biometrics Test Center San Jose State University One Washington Square San Jose, CA 95192-0180 IEEE. 1998
[2] Grudin M A., "Compact multilevel representation of human faces for recognition", Ph. D dissertation, Livepool John Moores University Nov. 1997
[3] Ming-Hsuan Yang, David J.Kriegman and Narendra Ahuja, "Detecting Faces in Images: A Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, No.1, January 2002
[4] 梁路宏，艾海舟等，“人脸检测研究综述”．计算机学报，Vol．25 No5 May 2002
[5] 舟激流，张晔．“人脸识别理论研究进展”，计算机辅助设计与图形学学报，Vol．11，No．2 Mar．1999
[6] D.Anifantis, E.Dermatas and G.Kokkinakis, "A Neural Network Method For Accurate Face Detection On Arbitrary Images" Electronics, Circuits and Systems, 1999. Proceedings of ICECS'99. The 6th IEEE International Conference on, Volume: 1, 1999
[7] Nikos Sarris, Nikos Grammalidis and Michael G..Strintzis "A Novel Neural Network Technique for the Detection of Human Faces in Visual Scenes" 5th Seminar on Neural Network Applications in Electrical Engineering NEUREL-2000 Facùlty of Electrical Engineering, University of Belgrade, Yugoslavia, September 25-27,2000
[8] Anagnostopoulos C., Anagnostopoulos Ⅰ. Etc. "A Probalilistic Neural Network for face detection on segmented skin areas based on fuzzy rules" IEEE Melecon 2002, May 7-9, 2002, Cairo, EGYPT
[9] Qian Gu and Stan Z.Li "Combining Feature Optimization Into Neural Network Based Face Detection" Pattern Recognition, 2000. Proceedings. 15th International Conference on, Volume: 2, 2000
[10] Osuna, E.; Freund, R.; Girosit, F., "Training Support Vector Machines: an Application to Face Detection", Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, 17-19 Jun 1997
[11] Richman, M.S.; Parks, T.W.; Hsien-Che Lee, "A Novel Support Vector Machine-Based Face Detection Method" Signals, Systems, and Computers, 1999. Conference Record of the Thirty-Third Asilomar Conference on, Volume: 1, 1999.
[12] Haizhou Ai, Lihang Ying and Guangyou Xu, "A Subspace Approach to Face Detection with Support Vector Machines" Pattern Recognition, 2002. Proceedings. 16th International Conference on, Volume: 1, 2002
[13] Htichem Sahbi and Nozha Boujemaa, "Coarse-to-Fine Support Vector Classifiers for Face Detection" Pattern Recognition, 2002. Proceedings. 16th International Conference on, Volume:3, 2002
[14] Xiaofeng LU and Nanning Zheng, "Support Vector Domain Description Based Face Detection in Gray-Scale Images" International Symposium on Information Theory and Its Applations Xi'an, PRC, October 7-11, 2002
[15] Tax, D., Duin, R, "Datadomain description by support Vectors", Verleysen, M.(Ed.), Proc, ESANN, pp.251-256,199
[16] Xipan Xiao, Haizhou Ai etc., "Improved Training Algorithms to Reduced Set Vector Machine and Adaboost Cascade Classifier for Face Detection" in Second International Conf. on Image and Graphics, Wei Sui, Editor, SPIE Vol.4875, pp.656-662, 2002.
[17] 赵荣椿等，“数字图像处理导论”．西北工业大学出版社，2000年
[18] 边肇祺，“模式识别”，清华大学出版社，2000年
[19] B.Menser and F.Muller, Face Detection in Color Images Using Principal Components Analysis, Image Processing and Its Applications, Conference Publication No.465 IEEE 1999.
[20] T.Sakai. M.Nagao, and S.Fujibayashi,"Line Extraction and Pattern Detection in a Photograph", Pattern Recognition, Vol. 1, pp.233-248,1969
[21] Haizhou Ai, Luhong Liang and Guangyou Xu, "Face Detection Based on Template Matching and Support Vector Machines" IEEE, 2001.
[22] Young Ho K won and Niels da Vitoria Lobo,"Face Detection Using Templates", IEEE, 1994
[23] A.Yuille,P.Hallinan, and D.Cohen,"Feature Extraction from Faces Using Deformable Templates," Int'l J.Computer Vision, Vol.8,No.2,pp.-111, 1992
[24] K,-K.Sung, "Learning and Example Selection for Object and Pattern Detection",PhD thesis, Massachusetts Inst. of Technology, 1996
[25] K.-K. Sung and T.Poggio, "Example-Based Learning for View-Based Human Face Detection," Technical Report AI Memo 1521, Massachusetts Inst.of Technology AI Lab, 1994
[26] H.Sehneiderman and T.Kanade,"Probabilistic Modeling of Local Apperance and Spatial Relationships for Object Recognition," Proc.IEEE Conf. Comptuer Vision and Pattern Recognition, PP.45-51, 1998
[27] Ara V.Nefian and Monson H.Hayes, "An Embedded HMM-Based Approach for Face Detection And Recognition" IEEE 1999
[28] Ara V.Nefian and Monson H.Hayes, "Face detection and recognition using hidden Markov models for face recognition", in ICASSP98, pp.2721-2724, 1998.
[29] G..Yang and T.S.Huang, "Human Face Detection in Complex Background," Pattern Recognition, vol.27, no.1, pp.53-63, 1994
[30] C.Kotropoulos and I.Pitas,"Rule-Based Face Detection in Frontal Views," Proc.Int'l Conf.Acoustics, Speech and Signal Processing, vol.4,pp.2537-2540, 1997
[31] H.P.Graf, E.Petajan, and E.Cosatto, "Locating Faces and Facial Parts", Proc.First Int'l Workshop Automatic Face and Gesture Recognition, pp.41-46, 1995
[32] T.K.Leung, M.C.Burl, and P.Prona, "Finding Faces in Cluttered Scenes Using Random Labeled Graph Matching, "Proc.Fifth IEEE Int'l Conf.Computer Vision, pp.637-677, 1995
[33] Y.Amit, D.Geman, and B.Jedynak,"Efficient Focusing and Face Detection," Face Recognition: From Theory to Applications, H.Weehsler, P.J. Phillips, V.Bruce, F. Fogelman-Soulie, and T.S.Huang, eds., Vol. 163, pp.124-156, 1998
[34] K.C.Yow and R.Cipolla, "Feature-Based Human Face Detection," Image and Vision Computing, vol.15, no.9, pp.713-735, 1997
[35] M,C. Burl, T.K.Leung, and P.Perona, "Face Localization via Shape Statistics," Proc. First Int'l Workshop Automatic Face and Gesture Recognition, pp.154-159, 1995
[36] S.Satoh, Y.Nakamura, and T.Kanade, "Name-It:Naming and Detecting Faces in News Videos", IEEE Multimedia, vol.6, no.1, pp.22-35, 1999
[37] Y.Miyake, H.Saitoh,H.Yaguchi,and N.Tsukada,"Facial Pattern Detection and Color Correction from Television Picture for Newspaper Printing," J.Imaging Technology, vol.16,
no.5,pp. 165-169,1990.
[38] R.Kjeldsen and J.Kender, "Finding Skin in Color Images," Proc. Second Int'l Conf.Automatic Face and Gesture Recognition, pp, 312-317, 1996
[39] R.Kjeldsen and J.Kender, "Finding Skin in Color Images," Proc.Second Int'l Conf. Automatic Face and Gesture Recognition, pp.312-317, 1996
[40] Y.Dai and Y.Nakano, "Extraction for Facial Images from Complex Background Using Color Information and SGLD Matrices," Proc.First Int'l Workshop Automatic Face and Gesture Reeogntion, Vol.29, no.6, pp.1007-1017
[41] E,Saber and A.M. Tekalp, "Frontal-View Face Detection and Facial Feature Extraction Using Color, Shape and Symmetry Based Cost Functions," Pattern Recognition Letters, vol17, no.8, pp.669-680,1998.
[42] Q.Chen, H.Wu, and M.Yachida, "Face Detection by Fuzzy Matching," Proc.Fifth lEEE Int'l Conf. Computer Vision, pp.591-596, 1995
[43] M.-H.Yang and N.Ahuja, "Detecting Human Faces in Color Images," Proc.IEEE Int'l Conf.Image Processing, vol. 1, pp.127-130, 1998
[44] M.F. Augusteijn and T.L. Skujca, "Identification of Human Faces through Texture-Based Feature Recognition and Neural Network Technology," Proc IEEE Conf. Neural Networks, pp.392-398, 1993
[45] Y.Dai and Y.Nakano, "Face-Texture Model Based on SGLD and Its Application in Face Detection in a Color Scene," Pattern Recognition, vol.29, no.6, pp. 1007-1017, 1996.
[46] K.Hotta, T.Kurita, and T.Mishima, "Scale Invariant Face Detection Method Using Higher-Order Local Autocorrelation Features Extracted from Log-Polar Image." Proc.Third Int'l Conf. Automatic Face and Gesture Recognition, pp.70-75, 1998.
[47] H.Wu, Q.Chen, and M.Yachida, "Face Detection from Color Images Using a Fuzzy Pattern Matching Method," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.21, no.6, pp.557-563, June 1999.
[48] Turk, M. and Pentland, A.Eigenfaces for Recognition. Journal of Cognitive Neurosciences. 3(1):71-96
[49] Watanapongse, P.& Szu, H.Application of the Principal wavelet Component in pattern classification, SPIE Proc., Wavelet Appl.v, Vol.3991, pp.194-205, Orlando April 14-16,1998.
[50] Belhumeur P.N., Hespanha J.P.,Kriegman, D.J. "Eigenfaces vs.Fisherfaces: Recognition Using Class Specific Linear Projection", Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume: 19, pp 711-720, Issue:7, July 1997
[51] Chengjun Liu and Harry Wechsler, "Enhanced Fisher Linear Discriminant Models for Face Recognition" IEEE Transactions on Image Processing v11 n 4 April 2002 Institute of Electrical and Electronics Engineers Inc. p 467-476 1057-7149
[52] D.J.Burr, "Elastic matching of line drawings," IEEE Trans.Patt.Anal. Machine Intell, Vol.3,no.t, pp.708-713, 1981
[53] A.L.Yuille, "Deformable templates for face recognition," J.Cognitive Neurosci, Vol. 3, no.1, pp59-70, 1991
[54] Szu, H.Progrss in Unsupervised Learning Neural Networks and Applications, IEEE Trans.Ind.Elcc,Mag.Sept. 1999.
[55] J.Gunning and N.Murphy, "Neural network based classification using automatically selected features sets," IEEE Conf.Series 359,pp.29-33,1992.
[56] G, Guodong, S.Li, and C.Kapluk.,"Face recognition by support vector machines",In Proc.IEEE International Conference on Automatic Face and Gesture Recognition, pages 196-201, 2000
[57] K.Jonsson, J.Matas, J.Kittler, and Y.Li. Learning support vectors for face verification and recognition. In Proc.IEEE International Conference on Automatic Face and Gesture Recognition, pages 208-213, 2000.
[58] Guo-Dong Guo and Hong-Jiang Zhang,"Boosting for Fast Face Recognition", 2001
[59] S.Y.Lee,Y.K.Ham and R.H.Park, "Recognition of Human Front Faces Using Konowledge-based Feature Extraction and Neuro-fuzzy Algorithm", Pattern Recognition, 1996, Vol.29.No.11, pp.1863-1876.
[60] Ziquan Hong, "Algebric Feature Extraction of Image for Recognition," Pattern Recognition, 1991, Vol.24, No.3, pp.211-219
[61] I.Craw, "Recognizing Face Features and Faces", IEEE Colloquium on Machine Storage and Recognition of Faces, London, Jan. 1992
[62] R.Chellapa,C.Wilson, and S.Sirohey.Human and machine recognition of faces:a survey.Proceeings of the IEEE, 83(5):705～741. 1995.
[63] A.Samal and P.A.lyengar,"Automatic recognition and analysis of human faces and facial express:Asurvey, "Pattern Recognition, 25:65-77, 1992.
[64] Phillips P.J, Moon H, Rauss Petal, "The FERET evaluation methodology for face-recognition algorithms. "In IEEE Computer Society Conference on CVPR, June 17-19 1997:137_143.
[65] 张翠平和苏光大，“人脸识别技术综述”，中国图象图形学报第5卷(A版)第11期，2000年11月。
[66] H.P.Graf, E.Cosatto etc. "Multimodal System for Locating Heads and Faces," Proc.Second Int'l Conf. Automatic Face and Gesture Recognitin, pp.88-93,1996
[67] J.Yang and A, Waibel, "A Real-time Face Tracker," Proc. Third Workshop Applications of Computer Vision, pp. 142-147
[68] Alonso G, Agrawal D, et al, "Functionality and Limitationas of Current Workflow Management Systems", Technical Report IBM Almaden Research Center, 1997.
[69] 徐之海，冯华君．“基于KL变换的人脸识别”，西安科技学院学报，2001，(04)。
[70] 周宗恒．韩懿、高艳芳．“基于KL肤色的人脸初定位”，计算机工程，第27卷第4期，2001年。
[71] G.Yang, R.Stiefelhagen, U.Meier, and A.Waibel,"Visual Tracking for Multimodal Human Computer Interaction," Proc.ACM Human Factors in Computing Systems Conf.(CHI98), pp142-1467, 1996.
[72] S.A.Sirohey, "Human Face Segmentation and Identification," Technical Report CS-TR-3176, Univ.of Maryland, 1993.
[73] T.K.Leung, M.C.Burl, and P.Perona, "Finding Faces in Cluttered Scenes Using Random Labeled Graph Matching," Proc.Fifth IEEE Int'l Conf. Computer Vision, pp.637-644,1995
[74] K.Sobottka and I.Pitas, "Face Localization and Feature Extraction Based on Shape and Color Information," Proc.IEEE Int'l Conf. Image Processing, pp.483-486,1996.
[75] S.-H.Kim, N.-K. Kim,S.C.Ahn, and H.-G. Kim, "Object Oriented Face Detection Using Ranse and Color Information," Proc. Third Int'l Conf. Automatic Face and Gesture Recognition, pp.76-81, 1998.
[76] L.Sirovich and M.Kirby, "Low-dimensional Procedure for the characterization of human faces," in Journal of the Optical Society of Am. A, vol.4, pp519-524,1987
[88] M.Turk and A.Pentland, "Eigenfaees for Recognition," Journal of Cognitive Neuroscience, vol.3, no.1, pp71-86, 1991
[77] M. A. Turk and A. P. Pentland, "Face Recognition Using Eigenfaces", Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586-591, June, 1991.
[78] Tang Ho Man, Sunny, "Face Recognition Algorithms Review ", Term paper of the Chinese university of Hong Kong, Dec, 2001.
[79] 郭立、陆大虎、朱俊株，“基于Gabor多通道滤波和Hopfield神经网络的纹理图像分割”，计算机科学与应用。
[80] Gabor D., "Theory of communication", Journal of IEEE, 1946, 93: 429-457.
[81] J. G Daugman, "Two-dimensional spectral analysis of cortical receptive field profiles", Vis. Res., vol. 20, pp. 847-856, 1980.
[82] C.Papageorgiou, M.Oren, and T.Poggio, "A general framework for object detection", In International Conference on Computer Vision, 1998.
[83] Gokberk, B., Akarun, L. and Alpaydin, E., "Feature selection for pose invariant face recognition," Pattern Recognition, 2002. Proceedings. 16th International Conference on, Volume: 4,2002 Page(s): 306-309 vol.4。
[84] Guangpeng Zhang, Yanning Zhang, Fuyuan Hu, "Fast and Accurate Face Recognition Based on Selected Gabor Filters and FLD",第四界中国生物识别学术会议论文, 2003。
[85] P.SomoI,P.Pudil, etc."Adaptive floating search methods in feature selection". Pattern Recognition Letters 20(1999)1157-1163
[86] ZhenQiu Zhang; MingJing Li, etc., "Multi-View Face Detection with FloatBoost", Applications of Computer Vision, 2002. (WACV 2002). Proceedings. Sixth IEEE Workshop on, 3-4 Dec. 2002 Pages: 184-188
[87] A.Colmenarez, B.Frey, and T.Huang, "Detection and tracking of faces and facial features", In Proceedings of IEEE International Conference on Image Processing, 1999
[88] Rui Liao and Stan Z.Li. "Face recognition based on multiple facial features", In Proceedings of 1th IEEE International Conference on Automatic Face and Gesture Recogntion, Pages 239-244, Grenoble, France, March 2000
[89] Robert M Haralick et al. "Analysis and solutions of the three point perspective pose estimation problem", In proceedings of the International Conference on Computer Vision, Pages 592-598, 1991
[90] Ricardo Lopez and Thomas S.Huang, "Head Pose Computation for very low bit-rate video coding", In 6th International Conference on Computer Analysis of Images and Patterns, Pages 440-447. Springer-verlag Berlin Heidelberg, 1995.
[91] Rowley, H.A.; Baluja, S.; Kanade, T." Neural network-based face detection", PatternAnalysis and Machine Intelligence, IEEE Transactions on, Volume: 20 , Issue: 1, Jan. 1998pages:23-38
[92] G.Burel and D.Carel, "Detection and Localization of faces on Digital Images",Pattern Recognition Letters, vol. 15, no.10, pp.963-967,1994.
[93] S.-H.Lin, S.Y."Face Recognition/Detection by Probabilistic Decision-Based Neural Network", IEEE Trans. Neural Networks, vol 8, no.1, pp114-132, 1997.
[94] Rajagopalan, A.N.; Kumar, K.S.; Karlekar, J.etc.,"Finding faces in photographs",Computer
Vision, 1998. Sixth International Conference on, 4-7 Jan. 1998,Pages:640-645
[95] Nefian, A.V.; Hayes, M.H., Ⅲ,"Hidden Markov models for face recognition",Acoustics, Speech, and Signal Processing, 1998. ICASSP '98. Proceedings of the 1998 IEEE International Conference on, Volume: 5,12-15 May 1998,Pages:2721-2724 vol.5
[96] R.O.Duda and P.E.Hart, "Pattern classification and Scene Analysis", New York:John Wiley &Sons, 1973
[97] McClelland J.,Rumelhart, D E, "A distributed model of human learning and memory",.Parallel Distributed Processing (Vol. 2, pp. 170-215). Cambridge, MA: MIT Press,1986.
[98] 阎平凡等编著，“人工神经网络与模拟进化计算”．清华大学出版社，1999。
[99] Sergios Theodoridis, Konstantinos Koutroumbas,"Pattern Recognition",china machine press,2003.
[100] Churing Y. "Backpropagation, Theory, Architecture and Application", New York:Lawrence Erbaum Publishers, 1995.
[101] 龚元明，施卫东，BP神经网络振荡和平台问题的解决”，北京理工大学学报，vol．15，No．5，Nov．1995。
[102] 黄建国、王建华和卢国强，“提高人工神经网络BP算法收敛速度的几种方法”，华东船舶工业学院学报，vol．8，No．2．Jun．1994．
[103] Xin Yao,"Evolving Artificial Neural Networks", Proceedings of the IEEE, Volume:87, Issue: 9, Sept. 1999,Pages: 1423-1447.
[104] J. Moody and C. Darken, "Learning with localized receptive fields" In T. Sejnowski, D. Touretzky, and G. Hinton, editors, Connectionist Models Summer School, Carnegie Mellon University,Morgan Kaufmann Publishers, 1988.
[105] Moody J and Darken C, "Fast Learning in Networks of Locally-tuned Processing Units",Neural Computation,pp281-294,1989
[106] Tom M.M. "Mathine Learning",China Machine Press(英文版)
[107] Chapelle O, Haffner P, Vapnik V N, "Support Vector Machines for Histgram-based Image Classification",IEEE Trans. On Neural Networks, 1999,10(5): 1055-1064.
[108] Vapnik V N. "The Nature of Statistical Learning Theory", NY:Springer-verlag, 1995.
[109] 张艳宁等，“基于SOM和SVM的遥感图像目标识别”，系统工程与电子技术，第24卷第7期，2002。
[110] 张艳宁，“基于支撑矢量机的目标识别方法研究”，西北工业大学博士后出站报告，2001年。
[111] J. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods—Support Vector Learning (B. Scholkopf, CJC Burges, and AJ Smola, eds.), MIT Press, 185-208, 1999.
[112] Y.Freund and R.Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting",ln Proc. Of the Second European Conference on Computational Learning Theory, LNCS,March, 1995.
[113] 万亮．“基于先验模型、无简单伪造训练样本签名签定”，西北工业大学硕士论文，2003年。
[114] Paul Viola,Michael Jones, "Robust Real-time Object Detection",second international workshop on statistical and computational theories of vision-modeling, learning,computing,and sampling,Vaneouver, Canada,fuly 13,2001.
[115] 韩宏、杨静宁、娄震，“基于层次的分类器组合”，南京理工大学学报，第26卷第1期，2002年2月。
[2] Grudin M A., "Compact multilevel representation of human faces for recognition", Ph. D dissertation, Livepool John Moores University Nov. 1997
[3] Ming-Hsuan Yang, David J.Kriegman and Narendra Ahuja, "Detecting Faces in Images: A Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, No.1, January 2002
[4] 梁路宏，艾海舟等，“人脸检测研究综述”．计算机学报，Vol．25 No5 May 2002
[5] 舟激流，张晔．“人脸识别理论研究进展”，计算机辅助设计与图形学学报，Vol．11，No．2 Mar．1999
[6] D.Anifantis, E.Dermatas and G.Kokkinakis, "A Neural Network Method For Accurate Face Detection On Arbitrary Images" Electronics, Circuits and Systems, 1999. Proceedings of ICECS'99. The 6th IEEE International Conference on, Volume: 1, 1999
[7] Nikos Sarris, Nikos Grammalidis and Michael G..Strintzis "A Novel Neural Network Technique for the Detection of Human Faces in Visual Scenes" 5th Seminar on Neural Network Applications in Electrical Engineering NEUREL-2000 Facùlty of Electrical Engineering, University of Belgrade, Yugoslavia, September 25-27,2000
[8] Anagnostopoulos C., Anagnostopoulos Ⅰ. Etc. "A Probalilistic Neural Network for face detection on segmented skin areas based on fuzzy rules" IEEE Melecon 2002, May 7-9, 2002, Cairo, EGYPT
[9] Qian Gu and Stan Z.Li "Combining Feature Optimization Into Neural Network Based Face Detection" Pattern Recognition, 2000. Proceedings. 15th International Conference on, Volume: 2, 2000
[10] Osuna, E.; Freund, R.; Girosit, F., "Training Support Vector Machines: an Application to Face Detection", Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, 17-19 Jun 1997
[11] Richman, M.S.; Parks, T.W.; Hsien-Che Lee, "A Novel Support Vector Machine-Based Face Detection Method" Signals, Systems, and Computers, 1999. Conference Record of the Thirty-Third Asilomar Conference on, Volume: 1, 1999.
[12] Haizhou Ai, Lihang Ying and Guangyou Xu, "A Subspace Approach to Face Detection with Support Vector Machines" Pattern Recognition, 2002. Proceedings. 16th International Conference on, Volume: 1, 2002
[13] Htichem Sahbi and Nozha Boujemaa, "Coarse-to-Fine Support Vector Classifiers for Face Detection" Pattern Recognition, 2002. Proceedings. 16th International Conference on, Volume:3, 2002
[14] Xiaofeng LU and Nanning Zheng, "Support Vector Domain Description Based Face Detection in Gray-Scale Images" International Symposium on Information Theory and Its Applations Xi'an, PRC, October 7-11, 2002
[15] Tax, D., Duin, R, "Datadomain description by support Vectors", Verleysen, M.(Ed.), Proc, ESANN, pp.251-256,199
[16] Xipan Xiao, Haizhou Ai etc., "Improved Training Algorithms to Reduced Set Vector Machine and Adaboost Cascade Classifier for Face Detection" in Second International Conf. on Image and Graphics, Wei Sui, Editor, SPIE Vol.4875, pp.656-662, 2002.
[17] 赵荣椿等，“数字图像处理导论”．西北工业大学出版社，2000年
[18] 边肇祺，“模式识别”，清华大学出版社，2000年
[19] B.Menser and F.Muller, Face Detection in Color Images Using Principal Components Analysis, Image Processing and Its Applications, Conference Publication No.465 IEEE 1999.
[20] T.Sakai. M.Nagao, and S.Fujibayashi,"Line Extraction and Pattern Detection in a Photograph", Pattern Recognition, Vol. 1, pp.233-248,1969
[21] Haizhou Ai, Luhong Liang and Guangyou Xu, "Face Detection Based on Template Matching and Support Vector Machines" IEEE, 2001.
[22] Young Ho K won and Niels da Vitoria Lobo,"Face Detection Using Templates", IEEE, 1994
[23] A.Yuille,P.Hallinan, and D.Cohen,"Feature Extraction from Faces Using Deformable Templates," Int'l J.Computer Vision, Vol.8,No.2,pp.-111, 1992
[24] K,-K.Sung, "Learning and Example Selection for Object and Pattern Detection",PhD thesis, Massachusetts Inst. of Technology, 1996
[25] K.-K. Sung and T.Poggio, "Example-Based Learning for View-Based Human Face Detection," Technical Report AI Memo 1521, Massachusetts Inst.of Technology AI Lab, 1994
[26] H.Sehneiderman and T.Kanade,"Probabilistic Modeling of Local Apperance and Spatial Relationships for Object Recognition," Proc.IEEE Conf. Comptuer Vision and Pattern Recognition, PP.45-51, 1998
[27] Ara V.Nefian and Monson H.Hayes, "An Embedded HMM-Based Approach for Face Detection And Recognition" IEEE 1999
[28] Ara V.Nefian and Monson H.Hayes, "Face detection and recognition using hidden Markov models for face recognition", in ICASSP98, pp.2721-2724, 1998.
[29] G..Yang and T.S.Huang, "Human Face Detection in Complex Background," Pattern Recognition, vol.27, no.1, pp.53-63, 1994
[30] C.Kotropoulos and I.Pitas,"Rule-Based Face Detection in Frontal Views," Proc.Int'l Conf.Acoustics, Speech and Signal Processing, vol.4,pp.2537-2540, 1997
[31] H.P.Graf, E.Petajan, and E.Cosatto, "Locating Faces and Facial Parts", Proc.First Int'l Workshop Automatic Face and Gesture Recognition, pp.41-46, 1995
[32] T.K.Leung, M.C.Burl, and P.Prona, "Finding Faces in Cluttered Scenes Using Random Labeled Graph Matching, "Proc.Fifth IEEE Int'l Conf.Computer Vision, pp.637-677, 1995
[33] Y.Amit, D.Geman, and B.Jedynak,"Efficient Focusing and Face Detection," Face Recognition: From Theory to Applications, H.Weehsler, P.J. Phillips, V.Bruce, F. Fogelman-Soulie, and T.S.Huang, eds., Vol. 163, pp.124-156, 1998
[34] K.C.Yow and R.Cipolla, "Feature-Based Human Face Detection," Image and Vision Computing, vol.15, no.9, pp.713-735, 1997
[35] M,C. Burl, T.K.Leung, and P.Perona, "Face Localization via Shape Statistics," Proc. First Int'l Workshop Automatic Face and Gesture Recognition, pp.154-159, 1995
[36] S.Satoh, Y.Nakamura, and T.Kanade, "Name-It:Naming and Detecting Faces in News Videos", IEEE Multimedia, vol.6, no.1, pp.22-35, 1999
[37] Y.Miyake, H.Saitoh,H.Yaguchi,and N.Tsukada,"Facial Pattern Detection and Color Correction from Television Picture for Newspaper Printing," J.Imaging Technology, vol.16,
no.5,pp. 165-169,1990.
[38] R.Kjeldsen and J.Kender, "Finding Skin in Color Images," Proc. Second Int'l Conf.Automatic Face and Gesture Recognition, pp, 312-317, 1996
[39] R.Kjeldsen and J.Kender, "Finding Skin in Color Images," Proc.Second Int'l Conf. Automatic Face and Gesture Recognition, pp.312-317, 1996
[40] Y.Dai and Y.Nakano, "Extraction for Facial Images from Complex Background Using Color Information and SGLD Matrices," Proc.First Int'l Workshop Automatic Face and Gesture Reeogntion, Vol.29, no.6, pp.1007-1017
[41] E,Saber and A.M. Tekalp, "Frontal-View Face Detection and Facial Feature Extraction Using Color, Shape and Symmetry Based Cost Functions," Pattern Recognition Letters, vol17, no.8, pp.669-680,1998.
[42] Q.Chen, H.Wu, and M.Yachida, "Face Detection by Fuzzy Matching," Proc.Fifth lEEE Int'l Conf. Computer Vision, pp.591-596, 1995
[43] M.-H.Yang and N.Ahuja, "Detecting Human Faces in Color Images," Proc.IEEE Int'l Conf.Image Processing, vol. 1, pp.127-130, 1998
[44] M.F. Augusteijn and T.L. Skujca, "Identification of Human Faces through Texture-Based Feature Recognition and Neural Network Technology," Proc IEEE Conf. Neural Networks, pp.392-398, 1993
[45] Y.Dai and Y.Nakano, "Face-Texture Model Based on SGLD and Its Application in Face Detection in a Color Scene," Pattern Recognition, vol.29, no.6, pp. 1007-1017, 1996.
[46] K.Hotta, T.Kurita, and T.Mishima, "Scale Invariant Face Detection Method Using Higher-Order Local Autocorrelation Features Extracted from Log-Polar Image." Proc.Third Int'l Conf. Automatic Face and Gesture Recognition, pp.70-75, 1998.
[47] H.Wu, Q.Chen, and M.Yachida, "Face Detection from Color Images Using a Fuzzy Pattern Matching Method," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.21, no.6, pp.557-563, June 1999.
[48] Turk, M. and Pentland, A.Eigenfaces for Recognition. Journal of Cognitive Neurosciences. 3(1):71-96
[49] Watanapongse, P.& Szu, H.Application of the Principal wavelet Component in pattern classification, SPIE Proc., Wavelet Appl.v, Vol.3991, pp.194-205, Orlando April 14-16,1998.
[50] Belhumeur P.N., Hespanha J.P.,Kriegman, D.J. "Eigenfaces vs.Fisherfaces: Recognition Using Class Specific Linear Projection", Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume: 19, pp 711-720, Issue:7, July 1997
[51] Chengjun Liu and Harry Wechsler, "Enhanced Fisher Linear Discriminant Models for Face Recognition" IEEE Transactions on Image Processing v11 n 4 April 2002 Institute of Electrical and Electronics Engineers Inc. p 467-476 1057-7149
[52] D.J.Burr, "Elastic matching of line drawings," IEEE Trans.Patt.Anal. Machine Intell, Vol.3,no.t, pp.708-713, 1981
[53] A.L.Yuille, "Deformable templates for face recognition," J.Cognitive Neurosci, Vol. 3, no.1, pp59-70, 1991
[54] Szu, H.Progrss in Unsupervised Learning Neural Networks and Applications, IEEE Trans.Ind.Elcc,Mag.Sept. 1999.
[55] J.Gunning and N.Murphy, "Neural network based classification using automatically selected features sets," IEEE Conf.Series 359,pp.29-33,1992.
[56] G, Guodong, S.Li, and C.Kapluk.,"Face recognition by support vector machines",In Proc.IEEE International Conference on Automatic Face and Gesture Recognition, pages 196-201, 2000
[57] K.Jonsson, J.Matas, J.Kittler, and Y.Li. Learning support vectors for face verification and recognition. In Proc.IEEE International Conference on Automatic Face and Gesture Recognition, pages 208-213, 2000.
[58] Guo-Dong Guo and Hong-Jiang Zhang,"Boosting for Fast Face Recognition", 2001
[59] S.Y.Lee,Y.K.Ham and R.H.Park, "Recognition of Human Front Faces Using Konowledge-based Feature Extraction and Neuro-fuzzy Algorithm", Pattern Recognition, 1996, Vol.29.No.11, pp.1863-1876.
[60] Ziquan Hong, "Algebric Feature Extraction of Image for Recognition," Pattern Recognition, 1991, Vol.24, No.3, pp.211-219
[61] I.Craw, "Recognizing Face Features and Faces", IEEE Colloquium on Machine Storage and Recognition of Faces, London, Jan. 1992
[62] R.Chellapa,C.Wilson, and S.Sirohey.Human and machine recognition of faces:a survey.Proceeings of the IEEE, 83(5):705～741. 1995.
[63] A.Samal and P.A.lyengar,"Automatic recognition and analysis of human faces and facial express:Asurvey, "Pattern Recognition, 25:65-77, 1992.
[64] Phillips P.J, Moon H, Rauss Petal, "The FERET evaluation methodology for face-recognition algorithms. "In IEEE Computer Society Conference on CVPR, June 17-19 1997:137_143.
[65] 张翠平和苏光大，“人脸识别技术综述”，中国图象图形学报第5卷(A版)第11期，2000年11月。
[66] H.P.Graf, E.Cosatto etc. "Multimodal System for Locating Heads and Faces," Proc.Second Int'l Conf. Automatic Face and Gesture Recognitin, pp.88-93,1996
[67] J.Yang and A, Waibel, "A Real-time Face Tracker," Proc. Third Workshop Applications of Computer Vision, pp. 142-147
[68] Alonso G, Agrawal D, et al, "Functionality and Limitationas of Current Workflow Management Systems", Technical Report IBM Almaden Research Center, 1997.
[69] 徐之海，冯华君．“基于KL变换的人脸识别”，西安科技学院学报，2001，(04)。
[70] 周宗恒．韩懿、高艳芳．“基于KL肤色的人脸初定位”，计算机工程，第27卷第4期，2001年。
[71] G.Yang, R.Stiefelhagen, U.Meier, and A.Waibel,"Visual Tracking for Multimodal Human Computer Interaction," Proc.ACM Human Factors in Computing Systems Conf.(CHI98), pp142-1467, 1996.
[72] S.A.Sirohey, "Human Face Segmentation and Identification," Technical Report CS-TR-3176, Univ.of Maryland, 1993.
[73] T.K.Leung, M.C.Burl, and P.Perona, "Finding Faces in Cluttered Scenes Using Random Labeled Graph Matching," Proc.Fifth IEEE Int'l Conf. Computer Vision, pp.637-644,1995
[74] K.Sobottka and I.Pitas, "Face Localization and Feature Extraction Based on Shape and Color Information," Proc.IEEE Int'l Conf. Image Processing, pp.483-486,1996.
[75] S.-H.Kim, N.-K. Kim,S.C.Ahn, and H.-G. Kim, "Object Oriented Face Detection Using Ranse and Color Information," Proc. Third Int'l Conf. Automatic Face and Gesture Recognition, pp.76-81, 1998.
[76] L.Sirovich and M.Kirby, "Low-dimensional Procedure for the characterization of human faces," in Journal of the Optical Society of Am. A, vol.4, pp519-524,1987
[88] M.Turk and A.Pentland, "Eigenfaees for Recognition," Journal of Cognitive Neuroscience, vol.3, no.1, pp71-86, 1991
[77] M. A. Turk and A. P. Pentland, "Face Recognition Using Eigenfaces", Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586-591, June, 1991.
[78] Tang Ho Man, Sunny, "Face Recognition Algorithms Review ", Term paper of the Chinese university of Hong Kong, Dec, 2001.
[79] 郭立、陆大虎、朱俊株，“基于Gabor多通道滤波和Hopfield神经网络的纹理图像分割”，计算机科学与应用。
[80] Gabor D., "Theory of communication", Journal of IEEE, 1946, 93: 429-457.
[81] J. G Daugman, "Two-dimensional spectral analysis of cortical receptive field profiles", Vis. Res., vol. 20, pp. 847-856, 1980.
[82] C.Papageorgiou, M.Oren, and T.Poggio, "A general framework for object detection", In International Conference on Computer Vision, 1998.
[83] Gokberk, B., Akarun, L. and Alpaydin, E., "Feature selection for pose invariant face recognition," Pattern Recognition, 2002. Proceedings. 16th International Conference on, Volume: 4,2002 Page(s): 306-309 vol.4。
[84] Guangpeng Zhang, Yanning Zhang, Fuyuan Hu, "Fast and Accurate Face Recognition Based on Selected Gabor Filters and FLD",第四界中国生物识别学术会议论文, 2003。
[85] P.SomoI,P.Pudil, etc."Adaptive floating search methods in feature selection". Pattern Recognition Letters 20(1999)1157-1163
[86] ZhenQiu Zhang; MingJing Li, etc., "Multi-View Face Detection with FloatBoost", Applications of Computer Vision, 2002. (WACV 2002). Proceedings. Sixth IEEE Workshop on, 3-4 Dec. 2002 Pages: 184-188
[87] A.Colmenarez, B.Frey, and T.Huang, "Detection and tracking of faces and facial features", In Proceedings of IEEE International Conference on Image Processing, 1999
[88] Rui Liao and Stan Z.Li. "Face recognition based on multiple facial features", In Proceedings of 1th IEEE International Conference on Automatic Face and Gesture Recogntion, Pages 239-244, Grenoble, France, March 2000
[89] Robert M Haralick et al. "Analysis and solutions of the three point perspective pose estimation problem", In proceedings of the International Conference on Computer Vision, Pages 592-598, 1991
[90] Ricardo Lopez and Thomas S.Huang, "Head Pose Computation for very low bit-rate video coding", In 6th International Conference on Computer Analysis of Images and Patterns, Pages 440-447. Springer-verlag Berlin Heidelberg, 1995.
[91] Rowley, H.A.; Baluja, S.; Kanade, T." Neural network-based face detection", PatternAnalysis and Machine Intelligence, IEEE Transactions on, Volume: 20 , Issue: 1, Jan. 1998pages:23-38
[92] G.Burel and D.Carel, "Detection and Localization of faces on Digital Images",Pattern Recognition Letters, vol. 15, no.10, pp.963-967,1994.
[93] S.-H.Lin, S.Y."Face Recognition/Detection by Probabilistic Decision-Based Neural Network", IEEE Trans. Neural Networks, vol 8, no.1, pp114-132, 1997.
[94] Rajagopalan, A.N.; Kumar, K.S.; Karlekar, J.etc.,"Finding faces in photographs",Computer
Vision, 1998. Sixth International Conference on, 4-7 Jan. 1998,Pages:640-645
[95] Nefian, A.V.; Hayes, M.H., Ⅲ,"Hidden Markov models for face recognition",Acoustics, Speech, and Signal Processing, 1998. ICASSP '98. Proceedings of the 1998 IEEE International Conference on, Volume: 5,12-15 May 1998,Pages:2721-2724 vol.5
[96] R.O.Duda and P.E.Hart, "Pattern classification and Scene Analysis", New York:John Wiley &Sons, 1973
[97] McClelland J.,Rumelhart, D E, "A distributed model of human learning and memory",.Parallel Distributed Processing (Vol. 2, pp. 170-215). Cambridge, MA: MIT Press,1986.
[98] 阎平凡等编著，“人工神经网络与模拟进化计算”．清华大学出版社，1999。
[99] Sergios Theodoridis, Konstantinos Koutroumbas,"Pattern Recognition",china machine press,2003.
[100] Churing Y. "Backpropagation, Theory, Architecture and Application", New York:Lawrence Erbaum Publishers, 1995.
[101] 龚元明，施卫东，BP神经网络振荡和平台问题的解决”，北京理工大学学报，vol．15，No．5，Nov．1995。
[102] 黄建国、王建华和卢国强，“提高人工神经网络BP算法收敛速度的几种方法”，华东船舶工业学院学报，vol．8，No．2．Jun．1994．
[103] Xin Yao,"Evolving Artificial Neural Networks", Proceedings of the IEEE, Volume:87, Issue: 9, Sept. 1999,Pages: 1423-1447.
[104] J. Moody and C. Darken, "Learning with localized receptive fields" In T. Sejnowski, D. Touretzky, and G. Hinton, editors, Connectionist Models Summer School, Carnegie Mellon University,Morgan Kaufmann Publishers, 1988.
[105] Moody J and Darken C, "Fast Learning in Networks of Locally-tuned Processing Units",Neural Computation,pp281-294,1989
[106] Tom M.M. "Mathine Learning",China Machine Press(英文版)
[107] Chapelle O, Haffner P, Vapnik V N, "Support Vector Machines for Histgram-based Image Classification",IEEE Trans. On Neural Networks, 1999,10(5): 1055-1064.
[108] Vapnik V N. "The Nature of Statistical Learning Theory", NY:Springer-verlag, 1995.
[109] 张艳宁等，“基于SOM和SVM的遥感图像目标识别”，系统工程与电子技术，第24卷第7期，2002。
[110] 张艳宁，“基于支撑矢量机的目标识别方法研究”，西北工业大学博士后出站报告，2001年。
[111] J. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods—Support Vector Learning (B. Scholkopf, CJC Burges, and AJ Smola, eds.), MIT Press, 185-208, 1999.
[112] Y.Freund and R.Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting",ln Proc. Of the Second European Conference on Computational Learning Theory, LNCS,March, 1995.
[113] 万亮．“基于先验模型、无简单伪造训练样本签名签定”，西北工业大学硕士论文，2003年。
[114] Paul Viola,Michael Jones, "Robust Real-time Object Detection",second international workshop on statistical and computational theories of vision-modeling, learning,computing,and sampling,Vaneouver, Canada,fuly 13,2001.
[115] 韩宏、杨静宁、娄震，“基于层次的分类器组合”，南京理工大学学报，第26卷第1期，2002年2月。