基于复小波变换的人脸表情识别研究
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摘要
人脸表情含有丰富的人体行为信息,在人类非语言方式的交流中起到了主导作用,近几十年来在人机交互领域也受到越来越多的关注。若能使计算机拥有更强的识别和理解人脸表情的能力,将会极大的改变计算机与人的关系,从而使计算机更好的为人类服务。
本文对国内外关于人脸表情识别的文献进行了深入研究和分析,针对表情识别的若干问题进行了探讨,并且对在识别过程中占据着重要地位的特征提取的各种算法做了深入的研究,在此基础上提出了几种改进的算法。大量的实验证明本文提出的算法的高效性。主要工作如下：
第一,改进了现有的有监督的谱特征分析算法。在原有算法的基础上,引入了另外两种Laplacian矩阵,并通过实验验证了算法的有效性。
第二,提出了基于双元树复小波变换的有监督的谱特征分析算法。引入双元树复小波变换并进行了改进,利用其平移不变性、方向选择性、完全重构性和高效计算能力等特性,对图像进行4层分解来提取表情特征。每层分解得到6幅指向不同方向的带通子图,体现了其多尺度多方向的分辨能力,使得表情中细微的局部特征更好的体现出来,并使得识别率有了大幅提高。然后结合有监督的谱特征分析算法进行表情识别,在JAFFE库和CK库上通过大量的实验来验证本文算法的有效性。
第三,引入单元树复小波变换与双元树复小波变换进行比较,同时提出基于单元树复小波的有监督的谱特征分析算法。对比得知两种复小波变换虽然表征图像特征的能力很相似,但单元树复小波变换计算相对复杂。
第四,搭建了人脸表情识别演示系统,将本文研究的算法的识别结果更直观的显示出来,同时为后续研究提供了一个实验平台。
Facial expression holds rich human behavior information and plays a leading part in human non-verbal communication. In the field of Human-Computer Interaction, more attentions have been paid on it in recent decades. If computer is able to recognize and understand human emotions better, human and computer interaction will be greatly improved and serves human better.
In this paper, the literatures home and abroad about facial expression recognition are in deep research and analysis, and it shows that feature extraction gets much importance in recognition system. We focus a number of issues about facial expression recognition and make research on facial expression feature extraction algorithms. Furthermore, some improvements of feature extraction methods are proposed. Experiments prove the efficiency of the proposed algorithms. The main contributions of this paper are:
Firstly, we improve the existing method of supervised spectral analysis. Another two Laplacian matrixes are introduced to get better performance, and experiments are done to verify its effectiveness.
Secondly, supervised spectral analysis based on dual-tree complex wavelet transform is proposed. We introduce the dual-tree complex wavelet transform and bring forward some improvement. In virtue of the attractive properties such as shift-invariance, direction selectivity, perfect reconstruction and efficient computing, we decompose the image to 4 scales to extract expression feature.6 sub-images oriented at 6 different directions will be obtained at each scale, and it shows its multi-scale and multi-direction resolution ability. Meanwhile subtle and local characteristics of image will be presented better. Combined with supervised spectral analysis, the dual-tree complex wavelet transform enhances the recognition performance remarkably. Experiments on the JAFFE database and CK database show its effectiveness.
Thirdly, the dual-tree complex wavelet transform and single-tree complex wavelet transform are compared with each other. Furthermore, we propose the method of supervised spectral analysis based on the dual-tree complex wavelet transform. By comparison of these two complex wavelet transforms, we learned that though the ability to present image features is very similar, the single-tree complex wavelet transform is more complex to compute.
Fourthly, a demonstration system for facial expression recognition is built to show our achievement, and an experimental platform is provided for the follow-up study at the same time.
本文对国内外关于人脸表情识别的文献进行了深入研究和分析,针对表情识别的若干问题进行了探讨,并且对在识别过程中占据着重要地位的特征提取的各种算法做了深入的研究,在此基础上提出了几种改进的算法。大量的实验证明本文提出的算法的高效性。主要工作如下：
第一,改进了现有的有监督的谱特征分析算法。在原有算法的基础上,引入了另外两种Laplacian矩阵,并通过实验验证了算法的有效性。
第二,提出了基于双元树复小波变换的有监督的谱特征分析算法。引入双元树复小波变换并进行了改进,利用其平移不变性、方向选择性、完全重构性和高效计算能力等特性,对图像进行4层分解来提取表情特征。每层分解得到6幅指向不同方向的带通子图,体现了其多尺度多方向的分辨能力,使得表情中细微的局部特征更好的体现出来,并使得识别率有了大幅提高。然后结合有监督的谱特征分析算法进行表情识别,在JAFFE库和CK库上通过大量的实验来验证本文算法的有效性。
第三,引入单元树复小波变换与双元树复小波变换进行比较,同时提出基于单元树复小波的有监督的谱特征分析算法。对比得知两种复小波变换虽然表征图像特征的能力很相似,但单元树复小波变换计算相对复杂。
第四,搭建了人脸表情识别演示系统,将本文研究的算法的识别结果更直观的显示出来,同时为后续研究提供了一个实验平台。
Facial expression holds rich human behavior information and plays a leading part in human non-verbal communication. In the field of Human-Computer Interaction, more attentions have been paid on it in recent decades. If computer is able to recognize and understand human emotions better, human and computer interaction will be greatly improved and serves human better.
In this paper, the literatures home and abroad about facial expression recognition are in deep research and analysis, and it shows that feature extraction gets much importance in recognition system. We focus a number of issues about facial expression recognition and make research on facial expression feature extraction algorithms. Furthermore, some improvements of feature extraction methods are proposed. Experiments prove the efficiency of the proposed algorithms. The main contributions of this paper are:
Firstly, we improve the existing method of supervised spectral analysis. Another two Laplacian matrixes are introduced to get better performance, and experiments are done to verify its effectiveness.
Secondly, supervised spectral analysis based on dual-tree complex wavelet transform is proposed. We introduce the dual-tree complex wavelet transform and bring forward some improvement. In virtue of the attractive properties such as shift-invariance, direction selectivity, perfect reconstruction and efficient computing, we decompose the image to 4 scales to extract expression feature.6 sub-images oriented at 6 different directions will be obtained at each scale, and it shows its multi-scale and multi-direction resolution ability. Meanwhile subtle and local characteristics of image will be presented better. Combined with supervised spectral analysis, the dual-tree complex wavelet transform enhances the recognition performance remarkably. Experiments on the JAFFE database and CK database show its effectiveness.
Thirdly, the dual-tree complex wavelet transform and single-tree complex wavelet transform are compared with each other. Furthermore, we propose the method of supervised spectral analysis based on the dual-tree complex wavelet transform. By comparison of these two complex wavelet transforms, we learned that though the ability to present image features is very similar, the single-tree complex wavelet transform is more complex to compute.
Fourthly, a demonstration system for facial expression recognition is built to show our achievement, and an experimental platform is provided for the follow-up study at the same time.
引文
[1]丁锡节.人脸表情自动识别系统的研究[学位论文].浙江理工大学.2007年3月
[2]刘伟峰.人脸表情识别研究[学位论文].中国科学技术大学.2007年6月
[3]周晓彦.而部表情识别研究[学位论文].东南大学.2005年3月
[4]P. Ekman, W.V.Friesen, Facial action coding system:A technique for the measurement of facial movement, Consulting Psychologists Press,1978
[5]何良华.人脸表情识别中的若干关键技术的研究[学位论文].东南大学.2005年9月
[6]孙雯玉.人脸表情识别算法研究[学位论文].北京交通大学 2006年12月
[7]罗昊,孟传良.基于特征脸和LDA的人脸识别.贵州工业大学学报.2005年2月,第32卷,第1期,72-75
[8]李俊华,彭力.基于特征块主成分分析的人脸表情识别.计算机工程与设计.2008年6月.第29卷,第12期.3151-3153
[9]李丽,易从琴,孟传良.利用主元分析识别人脸.贵州工业大学学报.2003年8月,第32卷,第4期.69-72
[10]唐京海,张有为.基于FLD特征提取的SVM人脸表情识别方法.计算机工程与应用.2006年11月.10-12
[11]B.E.Boser, I.M.Guyon, V.N.Vapnik. A training algoritm for optimal margin classifier. Proc.of the 5th ACM Workshop on Comptuational Learning Theory. Pittsbrugh.1992.USA:IEEE Computer Society Press,1992.144-152
[12]A.V.Nefian, M.H.Hayes. Face detection and recognition using hidden Markov models. Porc. IEEE Conference on Image Processing. Chicago.1998.USA:IEEE Computer Society Press 1998.141-145
[13]A.V.Nefian, M.H.Hayes. An embedded HMM based approach for face detection and recognition. IEEE Conefernce on Acouistcs, Speech and Signal Processing.Phoenix Aiizona. 1999, USA, IEEE Computer Society Press,1999.
[14]Bartlett Marian Stewart, Lades H Martin, Sejnowski Terrence J. Independent component representations for face recognition Proceedings of the SPIE Symposium on Electronic Imaging[A].Science and Technology conference on Human Vision and Electronic Imaging Ⅲ [C]. California San Jose,1998-01.
[15]C. Havran. Independent Analysis for face authenti cation [A]. KES'2002 Proceedings-Knowledge-Based Intelligent Information and Engineering Systems[C]. Crema(Italy),2002, vol.09.1207-1211.
[16]Ruicong Zni, Qiuqi Ruan. Feature Extraction Using Supervised Spectral Analysis.ICSP2008 Proceedings.1536-1539
[17]Jianbo Shi, Jitendra Malik. Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine intelligence, vol 22,no.8, August 2000,888-905
[18]Fei Wang, Jingdong Wang, Changshui Zhang, James Kwok. Face recognition using spectral features. Pattern Recognition 2007. vol.40.2786-2797
[19]Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, Hong-Jiang Zhang. Face Recognition Using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, Mar. 2005, vol.27, no.3,328-340
[20]董李燕,刘艺蕾,王晓峰.基于人脸局部特征和SVM的表情识别.合肥学院学报,2009年2月,第19卷第1期,24-31
[21]方宁,李景治,贺贵明.基于DCT和神经网络的人脸识别.计算机工程.2004年8月.第30卷.第16期.53-56
[22]赵黎.基于隐马尔可夫模型的人脸表情识别系统的设计与实现[学位论文].天津师范大学.2007年3月
[23]罗昊,孟传良.基于特征脸和LDA的人脸识别.贵州工业大学学报.2005年2月,第32卷,第1期,72-75
[24]Ruicong Zhi, Qiuqi Ruan. Feature Extraction Using Supervised Spectral Analysis. ICSP 2008 Proceedings.
[25]司文武,钱沄涛.一种基于谱聚类的半监督聚类方法.计算机应用.2005年6月.第25卷第6期.1347-1349
[26]金惠珍,赵辽英,刘博.一种基于谱聚类的灰度图像分割法.计算机系统应用.2009年第4期,74-77
[27]李春贵,王萌.谱聚类方法研究及其在Weka中的实现.计算机工程与设计.2008年7月.第29卷第13期.3384-3386
[28]蔡晓妍,戴冠中,杨黎斌.谱聚类算法综述.计算机科学.2008年.第35卷第七期.14-18
[29]孔敏,汤进,罗斌.基于拉普拉斯图的谱特征的图像聚类研究.中国科学技术大学学报.2007年9月.第37卷第9期.1125-1129
[30]C.H.Q.D, X.F.H, H.Y.Z, M.G, H.D.S, A Min-max Cut Algorithm for Graph Partitioning and Data Clustering.
[31]沈亚田,沈夏炯,张磊.基于图划分的谱聚类算法在文本挖掘中应用.计算机技术与发展.2009年5月.第19卷第5期.96-98
[32]曾祥媛.基于谱特征的人脸表情识别[学位论文].北京交通大学.2009年5月.
[33]Z. Wu and R. Leahy, An Optimal Graph Theoretic Approach to Data Clustering:Theory and Its Application to Image Segmentation, IEEE Trans. Pattern Analysis and Machine Intelligence, Nov.1993, vol.15, no.11,1101-1113
[34]Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000, vol.22, no.8,888-905
[35]Hagen L, Kahng A B. New spectral methods for ratio cut partitioning and clustering. IEEE Trans. Computer-Aided Design,1992, vol 11, no.9,1074-1085
[36]Sarkar S, Soundararajan P. Supervised learning of large perceptual organization:Graph spectral partitioning and learning automata. IEEE Transaction on Pattern Analysis and Machine Intelligence,2000,vol.22, no.5,504-525
[37]Ding C, He X, Zha H, et al. Spectral Min-Max cut for Graph Partitioning and Data Clustering[C] //Proc. Of the IEEE Intl Conf. on Data Mining.2001,107-114
[38]M.Meila, L.Xu. Multiway cuts and spectral clustering. U. Washington Tech Report.2003
[39]M. Lyons, S. Akamatsu, M. Kamachi, J.Gyoba. Coding Facial Expressions with Gabor Wavelets, Third IEEE Conference on Face and Gesture Recognition. Japan.1998.200--205.
[40]T. Kanade, J. Cohn, Y. Tian, Comprehensive Database for Facial Expression Analysis, IEEE 4th International Conference on FGR. France.2000
[41]C.J. Liu, and H. Wechsler, Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition, IEEE Transactions on Image Processing, Apr. 2002,vol.11,no.4
[42]罗飞,王国胤,杨勇.一种基于Gabor小波特征的人脸表情识别新方法.计算机科学.2009年1月.第36卷第1期.181-184
[43]GuoYun Zhang, ShiYu Peng, HongMin Li, Combination of dual-tree complex wavelet and svm for face recognition, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, July 2008,12-15
[44]T.K, H.O, H.D, Facial feature extraction using complex dual-tree wavelet transform, Computer Vision and Image Understanding,2008,vol.111,229-246
[45]T.C, T.T, Multiscale texture classification using dual-tree complex wavelet transform, Pattern Recognition Letters,2009,vol.30,331-339
[46]N.D.K, S.U, Texture classification Using Rotated Wavelet Filters, IEEE Transactions on systems, man, and cybernetics-part A:Systems and Humans, Nov.2000,vol.30, no.6
[47]M.K, P.K.B, B.N.C, Texture Image Retrieval Using New Rotated Complex Wavelet Filters, IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics, Dec.2005,vol.35, no.6
[48]阮秋琦.数字图像处理学.北京.电子工业出版社.2004年7月.127-151
[49]I.W. Selesnick, R.G Baraniuk, and N.G Kingsbury, The Dual-Tree Complex Wavelet Transform, IEEE Signal Procession Magazine, Nov.2005,123-151
[50]石宏理,胡波.双树复小波变换及其应用综述.信息与电子工程.2007年6月.第5卷第3期.229-235
[51]Manesh Kokare, P.K.Biswas, B.N.Chatterji. Texture Image Retrieval Using New Rotated Complex Wavelet Filters. IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS-PART B:CYBERNETICS, Dec.2005. Vol.35, no.6
[52]唐良瑞,蔡安妮,孙景鳌.二元树复小波变换及其在图像方向滤波中的应用.中国图像图形学报.2003年4月.第8卷 第4期.435-442
[53]Alaa Eleyan, Huseyin Ozkaramanli, Hasan Demirel. Complex Wavelet Transform-Based Face Recognition. Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing. vol.2008
[54]Chaochun Liu, Daoqing Dai. Face Recognition Using Dual-Tree Complex Wavelet Features. IEEE TRANSACTIONS ON IMAGE PROCESSING Nov.2009, vol.18, no.11,2593-2599
[55]P.R.Hill, D.R.Bull, C.N.Canagarajah, Image fusion using a new framework for complex wavelet transforms. Image Processing,2005. ICIP 2005. IEEE International Conference on. 2005,1338-1341
[56]Dogu Baran Aydogan, Markus Hannula, Tuukka Arola, Prasun Dastidar, Jari Hyttinen.2D texture based classification, segmentation and 3D orientation estimation of tissues using DT-CWT feature extraction methods. Data and Knowledge Engineering.2009, vol.68, 1283-1397
[57]M.Lohakan, P.Nantivatana, W.Narkbuakaew, C.Pintavirooj and M.Sangworasil. Multiresolution Image Alignment based on Discrete Wavelet Transform. TENCON 2005.2005 IEEE Region 10. 1-4
[58]B.Fasel, Juergen Luettin. Automatic facial expression analysis:a survey. Pattern Recognition. 2003. vol.36.259-275
[59]N.Vretos, N.Nikolaidis, I.Pitas, A Model-Based Facial Expression Recognition Algorithm using Principal Components Analysis. Image Processing,2009 16th IEEE International Conference on. 2009.3301-3304
[60]Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, Hongjiang Zhang. Face Recognition Using Laplacianfaces. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Mar.2005, vol.27, no.3,328-340
[61]M. Bartlett, Face image analysis by unsupervised learning and redundancy reduction, Ph.D. Thesis, University of California, San Diego,1998.
[62]Y.T. Wu, T. Kanade, J.F. Cohn, Optical Flow Estimation Using Wavelet MotionModel, IEEE International Conference on Computer Vision, Bombay, India,January 1998,992-998
[63]Maja Pantic, J.M. Leon, Rothkrantz, Automatic Analysis of Facial Expressions:The State of the Art, IEEE Trans. On PAMI,2000, vol.22,no.12,1424-1446
[64]GA. Khuwaja, M.S. Lahari, A parameter-based combined classifier for invariant facial expression and fender recognition, International Journal of Pattern Recognition and Artificial Intelligence,2002, vol.16,no.1,27-51
[65]M. Dailey, G Cottrell, PCA Gabor for expression recognition, Technical Report, Institution UCSD, Number CS-629,1999
[66]Michael J.Lyons, Julien Budynek, Shigeru Akamatsu, Automatic Classification of Single Facial Images, IEEE Trans. on PAMI.1999, vol.21,no.12,1357-1362
[2]刘伟峰.人脸表情识别研究[学位论文].中国科学技术大学.2007年6月
[3]周晓彦.而部表情识别研究[学位论文].东南大学.2005年3月
[4]P. Ekman, W.V.Friesen, Facial action coding system:A technique for the measurement of facial movement, Consulting Psychologists Press,1978
[5]何良华.人脸表情识别中的若干关键技术的研究[学位论文].东南大学.2005年9月
[6]孙雯玉.人脸表情识别算法研究[学位论文].北京交通大学 2006年12月
[7]罗昊,孟传良.基于特征脸和LDA的人脸识别.贵州工业大学学报.2005年2月,第32卷,第1期,72-75
[8]李俊华,彭力.基于特征块主成分分析的人脸表情识别.计算机工程与设计.2008年6月.第29卷,第12期.3151-3153
[9]李丽,易从琴,孟传良.利用主元分析识别人脸.贵州工业大学学报.2003年8月,第32卷,第4期.69-72
[10]唐京海,张有为.基于FLD特征提取的SVM人脸表情识别方法.计算机工程与应用.2006年11月.10-12
[11]B.E.Boser, I.M.Guyon, V.N.Vapnik. A training algoritm for optimal margin classifier. Proc.of the 5th ACM Workshop on Comptuational Learning Theory. Pittsbrugh.1992.USA:IEEE Computer Society Press,1992.144-152
[12]A.V.Nefian, M.H.Hayes. Face detection and recognition using hidden Markov models. Porc. IEEE Conference on Image Processing. Chicago.1998.USA:IEEE Computer Society Press 1998.141-145
[13]A.V.Nefian, M.H.Hayes. An embedded HMM based approach for face detection and recognition. IEEE Conefernce on Acouistcs, Speech and Signal Processing.Phoenix Aiizona. 1999, USA, IEEE Computer Society Press,1999.
[14]Bartlett Marian Stewart, Lades H Martin, Sejnowski Terrence J. Independent component representations for face recognition Proceedings of the SPIE Symposium on Electronic Imaging[A].Science and Technology conference on Human Vision and Electronic Imaging Ⅲ [C]. California San Jose,1998-01.
[15]C. Havran. Independent Analysis for face authenti cation [A]. KES'2002 Proceedings-Knowledge-Based Intelligent Information and Engineering Systems[C]. Crema(Italy),2002, vol.09.1207-1211.
[16]Ruicong Zni, Qiuqi Ruan. Feature Extraction Using Supervised Spectral Analysis.ICSP2008 Proceedings.1536-1539
[17]Jianbo Shi, Jitendra Malik. Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine intelligence, vol 22,no.8, August 2000,888-905
[18]Fei Wang, Jingdong Wang, Changshui Zhang, James Kwok. Face recognition using spectral features. Pattern Recognition 2007. vol.40.2786-2797
[19]Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, Hong-Jiang Zhang. Face Recognition Using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, Mar. 2005, vol.27, no.3,328-340
[20]董李燕,刘艺蕾,王晓峰.基于人脸局部特征和SVM的表情识别.合肥学院学报,2009年2月,第19卷第1期,24-31
[21]方宁,李景治,贺贵明.基于DCT和神经网络的人脸识别.计算机工程.2004年8月.第30卷.第16期.53-56
[22]赵黎.基于隐马尔可夫模型的人脸表情识别系统的设计与实现[学位论文].天津师范大学.2007年3月
[23]罗昊,孟传良.基于特征脸和LDA的人脸识别.贵州工业大学学报.2005年2月,第32卷,第1期,72-75
[24]Ruicong Zhi, Qiuqi Ruan. Feature Extraction Using Supervised Spectral Analysis. ICSP 2008 Proceedings.
[25]司文武,钱沄涛.一种基于谱聚类的半监督聚类方法.计算机应用.2005年6月.第25卷第6期.1347-1349
[26]金惠珍,赵辽英,刘博.一种基于谱聚类的灰度图像分割法.计算机系统应用.2009年第4期,74-77
[27]李春贵,王萌.谱聚类方法研究及其在Weka中的实现.计算机工程与设计.2008年7月.第29卷第13期.3384-3386
[28]蔡晓妍,戴冠中,杨黎斌.谱聚类算法综述.计算机科学.2008年.第35卷第七期.14-18
[29]孔敏,汤进,罗斌.基于拉普拉斯图的谱特征的图像聚类研究.中国科学技术大学学报.2007年9月.第37卷第9期.1125-1129
[30]C.H.Q.D, X.F.H, H.Y.Z, M.G, H.D.S, A Min-max Cut Algorithm for Graph Partitioning and Data Clustering.
[31]沈亚田,沈夏炯,张磊.基于图划分的谱聚类算法在文本挖掘中应用.计算机技术与发展.2009年5月.第19卷第5期.96-98
[32]曾祥媛.基于谱特征的人脸表情识别[学位论文].北京交通大学.2009年5月.
[33]Z. Wu and R. Leahy, An Optimal Graph Theoretic Approach to Data Clustering:Theory and Its Application to Image Segmentation, IEEE Trans. Pattern Analysis and Machine Intelligence, Nov.1993, vol.15, no.11,1101-1113
[34]Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000, vol.22, no.8,888-905
[35]Hagen L, Kahng A B. New spectral methods for ratio cut partitioning and clustering. IEEE Trans. Computer-Aided Design,1992, vol 11, no.9,1074-1085
[36]Sarkar S, Soundararajan P. Supervised learning of large perceptual organization:Graph spectral partitioning and learning automata. IEEE Transaction on Pattern Analysis and Machine Intelligence,2000,vol.22, no.5,504-525
[37]Ding C, He X, Zha H, et al. Spectral Min-Max cut for Graph Partitioning and Data Clustering[C] //Proc. Of the IEEE Intl Conf. on Data Mining.2001,107-114
[38]M.Meila, L.Xu. Multiway cuts and spectral clustering. U. Washington Tech Report.2003
[39]M. Lyons, S. Akamatsu, M. Kamachi, J.Gyoba. Coding Facial Expressions with Gabor Wavelets, Third IEEE Conference on Face and Gesture Recognition. Japan.1998.200--205.
[40]T. Kanade, J. Cohn, Y. Tian, Comprehensive Database for Facial Expression Analysis, IEEE 4th International Conference on FGR. France.2000
[41]C.J. Liu, and H. Wechsler, Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition, IEEE Transactions on Image Processing, Apr. 2002,vol.11,no.4
[42]罗飞,王国胤,杨勇.一种基于Gabor小波特征的人脸表情识别新方法.计算机科学.2009年1月.第36卷第1期.181-184
[43]GuoYun Zhang, ShiYu Peng, HongMin Li, Combination of dual-tree complex wavelet and svm for face recognition, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, July 2008,12-15
[44]T.K, H.O, H.D, Facial feature extraction using complex dual-tree wavelet transform, Computer Vision and Image Understanding,2008,vol.111,229-246
[45]T.C, T.T, Multiscale texture classification using dual-tree complex wavelet transform, Pattern Recognition Letters,2009,vol.30,331-339
[46]N.D.K, S.U, Texture classification Using Rotated Wavelet Filters, IEEE Transactions on systems, man, and cybernetics-part A:Systems and Humans, Nov.2000,vol.30, no.6
[47]M.K, P.K.B, B.N.C, Texture Image Retrieval Using New Rotated Complex Wavelet Filters, IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics, Dec.2005,vol.35, no.6
[48]阮秋琦.数字图像处理学.北京.电子工业出版社.2004年7月.127-151
[49]I.W. Selesnick, R.G Baraniuk, and N.G Kingsbury, The Dual-Tree Complex Wavelet Transform, IEEE Signal Procession Magazine, Nov.2005,123-151
[50]石宏理,胡波.双树复小波变换及其应用综述.信息与电子工程.2007年6月.第5卷第3期.229-235
[51]Manesh Kokare, P.K.Biswas, B.N.Chatterji. Texture Image Retrieval Using New Rotated Complex Wavelet Filters. IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS-PART B:CYBERNETICS, Dec.2005. Vol.35, no.6
[52]唐良瑞,蔡安妮,孙景鳌.二元树复小波变换及其在图像方向滤波中的应用.中国图像图形学报.2003年4月.第8卷 第4期.435-442
[53]Alaa Eleyan, Huseyin Ozkaramanli, Hasan Demirel. Complex Wavelet Transform-Based Face Recognition. Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing. vol.2008
[54]Chaochun Liu, Daoqing Dai. Face Recognition Using Dual-Tree Complex Wavelet Features. IEEE TRANSACTIONS ON IMAGE PROCESSING Nov.2009, vol.18, no.11,2593-2599
[55]P.R.Hill, D.R.Bull, C.N.Canagarajah, Image fusion using a new framework for complex wavelet transforms. Image Processing,2005. ICIP 2005. IEEE International Conference on. 2005,1338-1341
[56]Dogu Baran Aydogan, Markus Hannula, Tuukka Arola, Prasun Dastidar, Jari Hyttinen.2D texture based classification, segmentation and 3D orientation estimation of tissues using DT-CWT feature extraction methods. Data and Knowledge Engineering.2009, vol.68, 1283-1397
[57]M.Lohakan, P.Nantivatana, W.Narkbuakaew, C.Pintavirooj and M.Sangworasil. Multiresolution Image Alignment based on Discrete Wavelet Transform. TENCON 2005.2005 IEEE Region 10. 1-4
[58]B.Fasel, Juergen Luettin. Automatic facial expression analysis:a survey. Pattern Recognition. 2003. vol.36.259-275
[59]N.Vretos, N.Nikolaidis, I.Pitas, A Model-Based Facial Expression Recognition Algorithm using Principal Components Analysis. Image Processing,2009 16th IEEE International Conference on. 2009.3301-3304
[60]Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, Hongjiang Zhang. Face Recognition Using Laplacianfaces. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Mar.2005, vol.27, no.3,328-340
[61]M. Bartlett, Face image analysis by unsupervised learning and redundancy reduction, Ph.D. Thesis, University of California, San Diego,1998.
[62]Y.T. Wu, T. Kanade, J.F. Cohn, Optical Flow Estimation Using Wavelet MotionModel, IEEE International Conference on Computer Vision, Bombay, India,January 1998,992-998
[63]Maja Pantic, J.M. Leon, Rothkrantz, Automatic Analysis of Facial Expressions:The State of the Art, IEEE Trans. On PAMI,2000, vol.22,no.12,1424-1446
[64]GA. Khuwaja, M.S. Lahari, A parameter-based combined classifier for invariant facial expression and fender recognition, International Journal of Pattern Recognition and Artificial Intelligence,2002, vol.16,no.1,27-51
[65]M. Dailey, G Cottrell, PCA Gabor for expression recognition, Technical Report, Institution UCSD, Number CS-629,1999
[66]Michael J.Lyons, Julien Budynek, Shigeru Akamatsu, Automatic Classification of Single Facial Images, IEEE Trans. on PAMI.1999, vol.21,no.12,1357-1362