人脸表情识别关键技术研究
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摘要
人脸表情含有丰富的人体行为信息,是人们进行非语言交流的一种重要方式。人脸表情识别在人机交互、智能机器人、安全驾驶、临床医学等领域有着非常广泛的应用前景。本论文主要研究内容如下:
1.阐述了人脸表情识别的研究背景和意义,介绍了表情识别的国内外发展现状,并从特征提取和分类两方面对人脸表情识别领域的若干关键技术方法做了总结和概括。
2.本文提出基于Adaboost和多掩模的人眼精确定位算法,该算法先通过Adaboost算法定位人眼大概区域,再利用矩形掩模和椭圆形掩模进一步缩小眼睛所在区域,最后计算像素点的分布中心得到眼睛坐标。
3.针对局部二元模式(Local Binary Pattern, LBP)算法的三大不足,本文采用中心化二元模式(Center-based Binary Pattern, CBP)算法提取人脸表情特征。该方法与LBP相比,不但降低了特征向量的维数,而且将中心像素点的作用考虑在内,同时改写了符号函数提高了抗噪性。实验表明,CBP算法在提取人脸表情特征方面有较快的速度和较强的抗噪性。
4.在表情分类阶段,本文尝试分别采用中心最近邻分类器(Center Nearest Neighbor, CNN)和二对二多分类支持向量机方法(SVM)对表情进行分类,并将二对二多分类SVM方法、CNN和其它方法进行比较实验,实验表明二对二多分类SVM方法不但识别速度快,而且有着较好的识别性能。
Facial expression recognition is widely used in the fields of Man-Machine Interaction, intelligent robot, safe driving and clinical medicine. The main research contents in this paper are as follows:
1. The research background and the current research situation of the facial expression recognition are illustrated. The main technologies of facial expression feature extraction and classification in the facial expression recognition are generally described.
2. A new method of eye location is proposed. It detects a roughly eye area with Adaboost in the first. Then it reduces the area with rectangular mask and elliptical mask. At last eye locations are obtained by computing the distribution centre of pixels.
3. Considering the three deficiencies of local binary pattern (LBP), Center-based binary pattern (CBP) is adopted at the stage of feature extraction. Compared with LBP, CBP, which considers the center pixel, reduces the feature dimensions and changes the sign function. Experiment results show that the method has a fast speed and a good ability to overcome the noise.
4. Center nearest neighbor (CNN) classifier and two-versus-two multi-class SVM classifier are adopted to classify the expressions. Compared with the CNN and the other methods, the result of experimentation shows that two-versus-two multi-class SVM classifier has a fast classification speed and a high performance.
1.阐述了人脸表情识别的研究背景和意义,介绍了表情识别的国内外发展现状,并从特征提取和分类两方面对人脸表情识别领域的若干关键技术方法做了总结和概括。
2.本文提出基于Adaboost和多掩模的人眼精确定位算法,该算法先通过Adaboost算法定位人眼大概区域,再利用矩形掩模和椭圆形掩模进一步缩小眼睛所在区域,最后计算像素点的分布中心得到眼睛坐标。
3.针对局部二元模式(Local Binary Pattern, LBP)算法的三大不足,本文采用中心化二元模式(Center-based Binary Pattern, CBP)算法提取人脸表情特征。该方法与LBP相比,不但降低了特征向量的维数,而且将中心像素点的作用考虑在内,同时改写了符号函数提高了抗噪性。实验表明,CBP算法在提取人脸表情特征方面有较快的速度和较强的抗噪性。
4.在表情分类阶段,本文尝试分别采用中心最近邻分类器(Center Nearest Neighbor, CNN)和二对二多分类支持向量机方法(SVM)对表情进行分类,并将二对二多分类SVM方法、CNN和其它方法进行比较实验,实验表明二对二多分类SVM方法不但识别速度快,而且有着较好的识别性能。
Facial expression recognition is widely used in the fields of Man-Machine Interaction, intelligent robot, safe driving and clinical medicine. The main research contents in this paper are as follows:
1. The research background and the current research situation of the facial expression recognition are illustrated. The main technologies of facial expression feature extraction and classification in the facial expression recognition are generally described.
2. A new method of eye location is proposed. It detects a roughly eye area with Adaboost in the first. Then it reduces the area with rectangular mask and elliptical mask. At last eye locations are obtained by computing the distribution centre of pixels.
3. Considering the three deficiencies of local binary pattern (LBP), Center-based binary pattern (CBP) is adopted at the stage of feature extraction. Compared with LBP, CBP, which considers the center pixel, reduces the feature dimensions and changes the sign function. Experiment results show that the method has a fast speed and a good ability to overcome the noise.
4. Center nearest neighbor (CNN) classifier and two-versus-two multi-class SVM classifier are adopted to classify the expressions. Compared with the CNN and the other methods, the result of experimentation shows that two-versus-two multi-class SVM classifier has a fast classification speed and a high performance.
引文
[1] Pieard R. W.. Affective computing. MIT Press. 1997
[2] Suwa M., Sugie N., and Fujimora K.. A preliminary note on pattern recognition of human emotional expression. Proceedings of the Fourth International Joint Conference on Pattern Recognition. 1978. 408-410
[3] Fasel B., Luettin J.. Automatic facial expression analysis: a survey. Pattern Recognition. 2003. 36(1): 259-275
[4] Ekman P., Friesen W. V.. UNMASKING THE FACE: A guide to recognizing emotions from facial clues. Prentice-Hall, Inc. 1975
[5] Jin Hui, Gao Wen. The human facial combined expression recognition system. Chinese Journal of Computer, 2000. 23(6): 602—608
[6] Gianluca Donato, Marian Stewart Bartlett, Joseph C. Hager, et al. Classifying facial actions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1999. 21(10): 974-989
[7] Pantic M., Rothkrantz L. J. M.. Automatic analysis of facial expression: the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000. 22(12): 1424-1446
[8]薛雨丽,毛峡,郭叶等.人机交互中的人脸表情识别研究进展.计算机工程与应用.2009.14(5):764-772
[9] Andrew J., Calder A., Burton M., et al. A principal component analysis of facial expression. Vision research. 2001. 1(9): 1179-1208
[10] Bartlett M. S., hades H. M., Sejnowski T. J.. Independent component representations for face recognition. Human Vision and Electronic Imaging. 1998. 528-539
[11] Ahonen T, Hadid A, Pietik?inen M. Face description with local binary patterns: Application to face recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2006. 28(12): 2037-2041
[12] Shan C., Gong S., McOwan P. W. Robust facial expression recognition using local binary patters. IEEE International Conference on Image Processing. 2005. 370-373
[13]施兴华.自动人脸表情识别.西安电子科技大学硕士论文.2009.21-44
[14]付晓峰,韦巍.基于多尺度中心化二值模式的人脸表情识别.控制理论与应用.2009.26(6):629-633
[15] Lee T.. Image representation using 2D Gabor wavelets. IEEE Transaction Pattern Analysis and Machine Intelligence. 1996. 18(10): 959-971
[16] Sun Xinghua, Xu Hongxia, Zhao Chunxia. Facial expression recognition based on histogram sequence of local Gabor binary patterns. IEEE International Conference on Computational Intelligence and Security. 2008. 158-163
[17] Cootes T., Edwards G.., Taylor C.. Active appearance models. The 5th European Conference on Computer Vision. 1998. 615-625
[18] Gibson J J., Carmichael L.. The Perception of the Visual World. Science. 1951. 113 (2940): 535
[19] Wildes R. P., Michael J., et al. Recovering estimates fluid flow from image sequence data. Computer Vision and Image Understanding. 2008. 246-266
[20] Béréziat D., Herlin I., Younes L.. A generalized optical flow constraint and its physical interpretation. Proceedings of Computer Vision and Pattern Recognition. 2000. 487-492
[21] Tian Y., Kanade T., Cohn J. F.. Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001. 23(2): 97-115
[22] Ma L., Khorasani K. Facial Expression recognition using constructive feedforward neural networks. IEEE Transaction on Systems Man and Cybernetics: Part B. 2004. 34(3): 1588-1595
[23] Cover T., Hart P.. Nearest neighbor pattern classification. IEEE Transactions in Information Theory. 1967. 13(1): 21-27
[24] Shahdi S. O., Pooyan M., Abu-Bakar S. A. R.. Facial expression recognition using image orientation field in limited regions and MLP neural network. 2010 10th International Conference on Information Sciences, Signal Processing and their Applications. 2010. 85-88
[25]余重基,李际军.一种基于RBF网络生成人脸表情的算法.计算机应用. 2005. 25(7):1611-1615
[26] Corinna Cortes, Vladimir Vapnik. Support vector networks. Machine Learning. 1995. 20(3): 273-297
[27] Tsai, Hung Hsu, Lai Yenshou, Zhang Yicheng. Using SVM to design facial expression recognition for shape and texture features. International Conference on Machine Learning and Cybernetics. 2010. 5: 2697-2704
[28] Gunes T., Polat E.. Feature selection for multi-SVM classifiers in facialexpression classification. International Symposium on Computer and Information Sciences. 2008. 1-5
[29] Zhao Yisu, Shen Xiaojun, Georganas N.D.. Facial expression recognition by applying multi-step integral projection and SVMs. IEEE Instrumentation and Measurement Technology Conference. 2009. 686-691
[30] Baum L. E., Petrie T.. Statistical inference for probabilistic functions of finite state Markov chains. Annals of Mathematical Statistics. 1966. 37: 1554-1563
[31] Baum L. E., Eagon J. A.. An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for recology. Bull Amer. Math. Stat. 1967. 37(2): 360-363
[32] Baum L. E., An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov process. Inequalities. 1972. 3(1): 1-8
[33] Sakaguchi T., Morishima S.. Face feature extraction from spatial frequency for dynamic expression recognition. The International Conference on Pattern Recognition. 1996: 451-455
[34]杨国亮,于仲安.基于改进光流算法和HMM的面部表情分析.微计算机信息.2008.1:260,284-285
[35] https://support.bioid.com/downloads/facedb/index.php. Available: 2011.01.10
[36] Viola P., Jones M.. Rapid object detection using a boosted cascade of simple features. IEEE Conference on Computer Vision and Pattern Recognition. 2001: 511-518
[37]章毓晋.图像工程(上册)-图像处理和分析.清华大学出版社.1999.65-66
[38] Pageorgiou C., Oren M., Poggio T.. A general framework for object detection. International Conference on Computer Vision. 1998. 555-562
[39] Oren M., Pageorgiou C., Ppggio T.. Example-based object detection in images by components. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2001. 23(4): 349-361
[40] Freund Y., Schapire R. E.. A decision-theoretic generalization of online learning and an application to boosting. The Second European Conference on Computational Learning Theory. 1995: 23-37
[41] Viola P., Jones M.. Robust real-time object detection. The Second International Workshop on Statistical and Computational Theories of Vision: Modeling, Learning, Computing, and Sampling. 2001
[42] Reinders M. J. T., Koch R. W. C., Gerbrands J. J.. Location facial features inimage sequences using neural networks. The Second International Conference on Automatic Face and Gesture Recognition. 1996. 16: 230-235
[43] Huang J., Harry.. Eye location using genetic algoriths. The Second International Conference on Audio and Video-Based Biometric Person Authentication. 1996. 16: 248-252
[44] Feng G. C., Yuen P. C.. Variance projection function and its application to eye detection for human face recognition. Pattern Recognition Letters. 1998. 19(9): 899-906
[45]戴景文,刘丹,苏剑波.基于投影峰的眼睛快速定位方法.模式识别与人工智能.2009.22(4):605-609
[46] Kith V., El-Sharkawy M., Bergeson-Dana T., et al. A feature and appearance based method for eye detection on gray intensity face images. International Conference on Computer Engineering and Systems. 2008. 25: 41-47
[47] Cheng Ziguo, Liu Yuncai. Efficient technique for ellipse detection using restricted randomized Hough transform. Proceedings of the International Conference on Information Technology: Coding and Computing. 2004. 2: 714-718
[48]陈海峰,雷华,孔燕波等.基于最小二乘法的改进的随机椭圆检测算法.浙江大学学报(工学报).2008.42(8):1360-1364
[49] Andrew Fitzgibbon, Maurizio Pilu, Robert B. Fisher.. Direct least square fitting of ellipses. Tran Analysis and Machine Intelligence. 1999. 21(5): 476-480
[50]彭辉,张长水,荣钢等.基于K-L变换的人脸自动识别方法.清华大学学报(自然科学报).1997.37(6): 67-70
[51]边肇祺,张学工等.模式识别(第二版).北京:清华大学出版社. 2000
[52]王磊,邹北骥,彭小宁等.一种改进的提取人脸面部特征点的AAM拟合算法.电子学报.2006.34(8):1424-1427
[53] Ojala T., Pietiki?nen T.. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. 24(7): 971-987
[54] Ahonen T., Hadid A., Pietiki?nen M. Face description with local binary pattern: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006. 28(12): 2037-2041
[55] Fu Xiaofeng, Wei wei. Centralized binary patterns embedded with image Euclidean distance for facial expression recognition. The Fourth International Conference on Natural Computation. 2008. 4: 115-119
[56] Li S. Z., Lu J.. Face recognition using the nearest feature line method. IEEETransaction on Neural Networks 1999. 10: 439–443
[57] Gao Qingbin, Wang Zhengzhi. Center-based nearest neighbor classifier. Pattern Recognition. 2007. 40(1): 346-349
[58] Hu Zhonghui, Cai Yunze, He Xing, et al. Fusion of multi-class support vector machines for fault diagnosis. The American Control Conference. 2005: 1941-1945
[59] Krebel U.. Pairwise classification and support vector machines. Advances in Kernel Methods: Support Vector Learning. 1999: 255-268
[60] Platt J. C., Cristianini N., Shawe-Taylor J.. Large margin DAGs for multi-class classification. Advances in Neural Information Processing Systems. 2000. 12: 541-553
[61]孟媛媛,刘希玉.一种新的基于二叉树的SVM多分类方法.计算应用.2005.25(11): 2653-2657
[62]于清,赵晖.一种2_a_2支持向量机多类分类新方法.计算机工程与应用.2008.44(25):186-188
[63] Chang C. C.. Lin C. J.. LIBSVM--A Library for Support Vector Machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/ .Available at 2010-12-24
[64] Shinohara Y, Otsu N. Facial expression recognition using fisher weight maps. Proceedings of IEEE Conference on Automatic Face and Gesture Recognition. 2004. 499-504
[65] Feng Xiaoyi. Facial Expression Recognition Based on Local Binary Patterns and Coarse-to-Fine Classification. International Conference on Computer and Information Technology (CIT’04). 2004
[66] He Lianghua, Zou Cairong. An Enhanced LBP Feature Based on Facial Expression Recognition. The 27th International Conference of the IEEE Engineer in Medicine and Biology Society. 2006.3300-3303
[67]张一鸣.人脸表情识别.大连理工大学硕士论文.2005.56-77
[2] Suwa M., Sugie N., and Fujimora K.. A preliminary note on pattern recognition of human emotional expression. Proceedings of the Fourth International Joint Conference on Pattern Recognition. 1978. 408-410
[3] Fasel B., Luettin J.. Automatic facial expression analysis: a survey. Pattern Recognition. 2003. 36(1): 259-275
[4] Ekman P., Friesen W. V.. UNMASKING THE FACE: A guide to recognizing emotions from facial clues. Prentice-Hall, Inc. 1975
[5] Jin Hui, Gao Wen. The human facial combined expression recognition system. Chinese Journal of Computer, 2000. 23(6): 602—608
[6] Gianluca Donato, Marian Stewart Bartlett, Joseph C. Hager, et al. Classifying facial actions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1999. 21(10): 974-989
[7] Pantic M., Rothkrantz L. J. M.. Automatic analysis of facial expression: the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000. 22(12): 1424-1446
[8]薛雨丽,毛峡,郭叶等.人机交互中的人脸表情识别研究进展.计算机工程与应用.2009.14(5):764-772
[9] Andrew J., Calder A., Burton M., et al. A principal component analysis of facial expression. Vision research. 2001. 1(9): 1179-1208
[10] Bartlett M. S., hades H. M., Sejnowski T. J.. Independent component representations for face recognition. Human Vision and Electronic Imaging. 1998. 528-539
[11] Ahonen T, Hadid A, Pietik?inen M. Face description with local binary patterns: Application to face recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2006. 28(12): 2037-2041
[12] Shan C., Gong S., McOwan P. W. Robust facial expression recognition using local binary patters. IEEE International Conference on Image Processing. 2005. 370-373
[13]施兴华.自动人脸表情识别.西安电子科技大学硕士论文.2009.21-44
[14]付晓峰,韦巍.基于多尺度中心化二值模式的人脸表情识别.控制理论与应用.2009.26(6):629-633
[15] Lee T.. Image representation using 2D Gabor wavelets. IEEE Transaction Pattern Analysis and Machine Intelligence. 1996. 18(10): 959-971
[16] Sun Xinghua, Xu Hongxia, Zhao Chunxia. Facial expression recognition based on histogram sequence of local Gabor binary patterns. IEEE International Conference on Computational Intelligence and Security. 2008. 158-163
[17] Cootes T., Edwards G.., Taylor C.. Active appearance models. The 5th European Conference on Computer Vision. 1998. 615-625
[18] Gibson J J., Carmichael L.. The Perception of the Visual World. Science. 1951. 113 (2940): 535
[19] Wildes R. P., Michael J., et al. Recovering estimates fluid flow from image sequence data. Computer Vision and Image Understanding. 2008. 246-266
[20] Béréziat D., Herlin I., Younes L.. A generalized optical flow constraint and its physical interpretation. Proceedings of Computer Vision and Pattern Recognition. 2000. 487-492
[21] Tian Y., Kanade T., Cohn J. F.. Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001. 23(2): 97-115
[22] Ma L., Khorasani K. Facial Expression recognition using constructive feedforward neural networks. IEEE Transaction on Systems Man and Cybernetics: Part B. 2004. 34(3): 1588-1595
[23] Cover T., Hart P.. Nearest neighbor pattern classification. IEEE Transactions in Information Theory. 1967. 13(1): 21-27
[24] Shahdi S. O., Pooyan M., Abu-Bakar S. A. R.. Facial expression recognition using image orientation field in limited regions and MLP neural network. 2010 10th International Conference on Information Sciences, Signal Processing and their Applications. 2010. 85-88
[25]余重基,李际军.一种基于RBF网络生成人脸表情的算法.计算机应用. 2005. 25(7):1611-1615
[26] Corinna Cortes, Vladimir Vapnik. Support vector networks. Machine Learning. 1995. 20(3): 273-297
[27] Tsai, Hung Hsu, Lai Yenshou, Zhang Yicheng. Using SVM to design facial expression recognition for shape and texture features. International Conference on Machine Learning and Cybernetics. 2010. 5: 2697-2704
[28] Gunes T., Polat E.. Feature selection for multi-SVM classifiers in facialexpression classification. International Symposium on Computer and Information Sciences. 2008. 1-5
[29] Zhao Yisu, Shen Xiaojun, Georganas N.D.. Facial expression recognition by applying multi-step integral projection and SVMs. IEEE Instrumentation and Measurement Technology Conference. 2009. 686-691
[30] Baum L. E., Petrie T.. Statistical inference for probabilistic functions of finite state Markov chains. Annals of Mathematical Statistics. 1966. 37: 1554-1563
[31] Baum L. E., Eagon J. A.. An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for recology. Bull Amer. Math. Stat. 1967. 37(2): 360-363
[32] Baum L. E., An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov process. Inequalities. 1972. 3(1): 1-8
[33] Sakaguchi T., Morishima S.. Face feature extraction from spatial frequency for dynamic expression recognition. The International Conference on Pattern Recognition. 1996: 451-455
[34]杨国亮,于仲安.基于改进光流算法和HMM的面部表情分析.微计算机信息.2008.1:260,284-285
[35] https://support.bioid.com/downloads/facedb/index.php. Available: 2011.01.10
[36] Viola P., Jones M.. Rapid object detection using a boosted cascade of simple features. IEEE Conference on Computer Vision and Pattern Recognition. 2001: 511-518
[37]章毓晋.图像工程(上册)-图像处理和分析.清华大学出版社.1999.65-66
[38] Pageorgiou C., Oren M., Poggio T.. A general framework for object detection. International Conference on Computer Vision. 1998. 555-562
[39] Oren M., Pageorgiou C., Ppggio T.. Example-based object detection in images by components. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2001. 23(4): 349-361
[40] Freund Y., Schapire R. E.. A decision-theoretic generalization of online learning and an application to boosting. The Second European Conference on Computational Learning Theory. 1995: 23-37
[41] Viola P., Jones M.. Robust real-time object detection. The Second International Workshop on Statistical and Computational Theories of Vision: Modeling, Learning, Computing, and Sampling. 2001
[42] Reinders M. J. T., Koch R. W. C., Gerbrands J. J.. Location facial features inimage sequences using neural networks. The Second International Conference on Automatic Face and Gesture Recognition. 1996. 16: 230-235
[43] Huang J., Harry.. Eye location using genetic algoriths. The Second International Conference on Audio and Video-Based Biometric Person Authentication. 1996. 16: 248-252
[44] Feng G. C., Yuen P. C.. Variance projection function and its application to eye detection for human face recognition. Pattern Recognition Letters. 1998. 19(9): 899-906
[45]戴景文,刘丹,苏剑波.基于投影峰的眼睛快速定位方法.模式识别与人工智能.2009.22(4):605-609
[46] Kith V., El-Sharkawy M., Bergeson-Dana T., et al. A feature and appearance based method for eye detection on gray intensity face images. International Conference on Computer Engineering and Systems. 2008. 25: 41-47
[47] Cheng Ziguo, Liu Yuncai. Efficient technique for ellipse detection using restricted randomized Hough transform. Proceedings of the International Conference on Information Technology: Coding and Computing. 2004. 2: 714-718
[48]陈海峰,雷华,孔燕波等.基于最小二乘法的改进的随机椭圆检测算法.浙江大学学报(工学报).2008.42(8):1360-1364
[49] Andrew Fitzgibbon, Maurizio Pilu, Robert B. Fisher.. Direct least square fitting of ellipses. Tran Analysis and Machine Intelligence. 1999. 21(5): 476-480
[50]彭辉,张长水,荣钢等.基于K-L变换的人脸自动识别方法.清华大学学报(自然科学报).1997.37(6): 67-70
[51]边肇祺,张学工等.模式识别(第二版).北京:清华大学出版社. 2000
[52]王磊,邹北骥,彭小宁等.一种改进的提取人脸面部特征点的AAM拟合算法.电子学报.2006.34(8):1424-1427
[53] Ojala T., Pietiki?nen T.. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. 24(7): 971-987
[54] Ahonen T., Hadid A., Pietiki?nen M. Face description with local binary pattern: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006. 28(12): 2037-2041
[55] Fu Xiaofeng, Wei wei. Centralized binary patterns embedded with image Euclidean distance for facial expression recognition. The Fourth International Conference on Natural Computation. 2008. 4: 115-119
[56] Li S. Z., Lu J.. Face recognition using the nearest feature line method. IEEETransaction on Neural Networks 1999. 10: 439–443
[57] Gao Qingbin, Wang Zhengzhi. Center-based nearest neighbor classifier. Pattern Recognition. 2007. 40(1): 346-349
[58] Hu Zhonghui, Cai Yunze, He Xing, et al. Fusion of multi-class support vector machines for fault diagnosis. The American Control Conference. 2005: 1941-1945
[59] Krebel U.. Pairwise classification and support vector machines. Advances in Kernel Methods: Support Vector Learning. 1999: 255-268
[60] Platt J. C., Cristianini N., Shawe-Taylor J.. Large margin DAGs for multi-class classification. Advances in Neural Information Processing Systems. 2000. 12: 541-553
[61]孟媛媛,刘希玉.一种新的基于二叉树的SVM多分类方法.计算应用.2005.25(11): 2653-2657
[62]于清,赵晖.一种2_a_2支持向量机多类分类新方法.计算机工程与应用.2008.44(25):186-188
[63] Chang C. C.. Lin C. J.. LIBSVM--A Library for Support Vector Machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/ .Available at 2010-12-24
[64] Shinohara Y, Otsu N. Facial expression recognition using fisher weight maps. Proceedings of IEEE Conference on Automatic Face and Gesture Recognition. 2004. 499-504
[65] Feng Xiaoyi. Facial Expression Recognition Based on Local Binary Patterns and Coarse-to-Fine Classification. International Conference on Computer and Information Technology (CIT’04). 2004
[66] He Lianghua, Zou Cairong. An Enhanced LBP Feature Based on Facial Expression Recognition. The 27th International Conference of the IEEE Engineer in Medicine and Biology Society. 2006.3300-3303
[67]张一鸣.人脸表情识别.大连理工大学硕士论文.2005.56-77