基于二元模式的人脸识别与表情识别研究
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摘要
人们的工作和生活越来越多地和计算机联系在一起,使得人类与计算机之间的关系越来越紧密。另外,各种各样的机器人也相继问世并且会越来越多地出现在我们的周围。人们渴望自然和谐的人机交互——计算机首先要识别主人的身份,然后判别主人的表情以做出相应的动作。因此,本文以快速准确的人机交互为目标,旨在研究如何提高基于人脸的身份识别与表情识别的识别率及识别速度。论文的主要研究工作与成果包括以下几个方面的内容:
1)提出一种基于局部二元模式直方图映射(LBPHP)的快速人脸识别方法。此方法将局部二元模式直方图映射到保局投影(LPP)空间获得低维的LBPHP特征,在此低维特征空间判别新样本大大提高了识别速度,由于LPP强大的鉴别特性,此方法的识别率也很高,相比于传统的基于局部二元模式(LBP)的人脸识别方法,此方法不仅识别速度更快、识别率更高,尤其在大型人脸库上凸显其优势,适于此类人脸库上的实际应用如身份识别等。
2)在表情特征提取方面,传统LBP算子存在不足:产生的直方图维数过长、鉴别力不高、对噪声反应敏感.针对此问题,提出中心化二元模式(CBP)算子。CBP算子相对于LBP算子具有三大优势:(1)CBP算子通过比较“近邻点对”之间的差异捕捉到梯度信息,不仅增强其鉴别能力而且大大降低特征维数。(2)CBP算子充分考虑中心像素点的作用并给它分配最高权重,此举大大提高其鉴别能力。(3)从图像中提取到的CBP特征在有噪声情形下更加鲁棒、更加稳定。此外,为提高识别结果,首次将中心最近邻分类器引入表情识别中,它的分类效能优于目前常用的最近邻分类器。
3)为进一步提高人脸表情识别率,对中心化二元模式(CBP)做拓展:(1)将梯度信息融入CBP;(2)提出多尺度CBP(简称MCBP);(3)为增强算法对表情图像中细小变形的鲁棒性,首次引入图像欧式距离(IMED)并将其嵌入MCBP方法.嵌有IMED的MCBP(简称MCBP-IMED)方法提取出的特征具有优点:维数大大降低、很强的鉴别能力、对噪声不敏感、不易受细小变形的干扰。
4)提出结合了CBP与Gabor变换的中心化Gabor(简称CGBP)直方图,并将梯度信息融入其中。为更好地反映表情流形的内在结构,提出融合了局部方法和有监督方法思想的有监督Laplacianfaces(简称SLAP)。另外,注意到人脸面部表情与人们情感表达的密切关系,提出一种融合了连续性与离散性的表情空间模型。基于此模型,将SLAP应用于融入梯度信息的CGBP直方图进行表情识别及表情成分分析。
Human's life and work is related increasingly to computer,so the relationship between human and computer becomes closer and closer.Furthermore,many kinds of robots come out and will appear increasingly around us.Human long for the natural and harmonious human-computer interaction(HCI),i.e.,computers firstly distinguish the mastership and then act according to recognizing host's expression.Hence,the thesis sets the fast and accurate HCI as the goal and aims at improving the accuracy and speed of the face recognition and expression recognition.The major research works and contributions of the thesis include:
1) It proposed a fast face recognition approach called local binary pattern histogram projection(LBPHP).The method projects local binary pattern(LBP) histogram onto locality preserving projection(LPP) space and obtains the low dimensional LBPHP feature.It is fast to recognize new sample in the low dimensional space.The method has good accuracy in the light of LPP's powerful discriminative property.LBPHP method is superior to conventional method based on LBP not only on recognition speed but also on accuracy.It is prominent especially on large-scale face database and suitable for practical application e.g.,identity authentication.
2) In the aspect of expression feature extraction,the conventional LBP operators have several disadvantages such as rather long histograms produced by them,lower discrimination and sensitivity to noise.Aimed at the problems,it proposed the centralized binary pattern(CBP) operator.CBP operator has several advantages:(1) CBP operator captures the gradient information by comparing pairs of neighbors, which not only improves its discrimination but also reduces significantly the feature's dimensionality.(2) Its discrimination is strengthened due to considering the center pixel point and giving it the largest weight.(3) The CBP feature extracted from image is more robust and more stable in the noisy situation.Moreover,for the purpose of increasing recognition accuracy,it introduced the center-based nearest neighbor classifier into expression recognition.This kind of classifier is superior to the nearest neighbor classifer.
3) In order to increase the accuracy of expression recognition,it expanded CBP from the following aspects:(1) The gradient information was integrated into CBP.(2) Multi-scale CBP(MCBP) was proposed.(3) For the purpose of improving the robustness to small deformation of expressional images,it introduced image Euclidean distance(IMED) and embedded it in MCBP.MCBP-IMED is short for the approach of MCBP embedded with IMED.The feature extracted by using this method has advantages:lower dimension,very powerful discrimination,insensitivity to noise, robustness to small deformation.
4) It proposed the centralized Gabor binary pattern(CGBP) histogram,which combined CBP and Gabor transform,and integrated the gradient information into CGBP histogram.In order to reflect accurately the underlying structure of expressional manifold,it put forward the supervised Laplacianfaces(SLAP) which combined the ideas of local method and supervised method.Furthermore,because of the close relationship between facial expression and feeling,it proposed the expressional space model which combined the continuity and scatter of expressional space.Baesd on the model,SLAP was applied to CGBP histogram with gradient information to recognize expression and analyse expression components.
1)提出一种基于局部二元模式直方图映射(LBPHP)的快速人脸识别方法。此方法将局部二元模式直方图映射到保局投影(LPP)空间获得低维的LBPHP特征,在此低维特征空间判别新样本大大提高了识别速度,由于LPP强大的鉴别特性,此方法的识别率也很高,相比于传统的基于局部二元模式(LBP)的人脸识别方法,此方法不仅识别速度更快、识别率更高,尤其在大型人脸库上凸显其优势,适于此类人脸库上的实际应用如身份识别等。
2)在表情特征提取方面,传统LBP算子存在不足:产生的直方图维数过长、鉴别力不高、对噪声反应敏感.针对此问题,提出中心化二元模式(CBP)算子。CBP算子相对于LBP算子具有三大优势:(1)CBP算子通过比较“近邻点对”之间的差异捕捉到梯度信息,不仅增强其鉴别能力而且大大降低特征维数。(2)CBP算子充分考虑中心像素点的作用并给它分配最高权重,此举大大提高其鉴别能力。(3)从图像中提取到的CBP特征在有噪声情形下更加鲁棒、更加稳定。此外,为提高识别结果,首次将中心最近邻分类器引入表情识别中,它的分类效能优于目前常用的最近邻分类器。
3)为进一步提高人脸表情识别率,对中心化二元模式(CBP)做拓展:(1)将梯度信息融入CBP;(2)提出多尺度CBP(简称MCBP);(3)为增强算法对表情图像中细小变形的鲁棒性,首次引入图像欧式距离(IMED)并将其嵌入MCBP方法.嵌有IMED的MCBP(简称MCBP-IMED)方法提取出的特征具有优点:维数大大降低、很强的鉴别能力、对噪声不敏感、不易受细小变形的干扰。
4)提出结合了CBP与Gabor变换的中心化Gabor(简称CGBP)直方图,并将梯度信息融入其中。为更好地反映表情流形的内在结构,提出融合了局部方法和有监督方法思想的有监督Laplacianfaces(简称SLAP)。另外,注意到人脸面部表情与人们情感表达的密切关系,提出一种融合了连续性与离散性的表情空间模型。基于此模型,将SLAP应用于融入梯度信息的CGBP直方图进行表情识别及表情成分分析。
Human's life and work is related increasingly to computer,so the relationship between human and computer becomes closer and closer.Furthermore,many kinds of robots come out and will appear increasingly around us.Human long for the natural and harmonious human-computer interaction(HCI),i.e.,computers firstly distinguish the mastership and then act according to recognizing host's expression.Hence,the thesis sets the fast and accurate HCI as the goal and aims at improving the accuracy and speed of the face recognition and expression recognition.The major research works and contributions of the thesis include:
1) It proposed a fast face recognition approach called local binary pattern histogram projection(LBPHP).The method projects local binary pattern(LBP) histogram onto locality preserving projection(LPP) space and obtains the low dimensional LBPHP feature.It is fast to recognize new sample in the low dimensional space.The method has good accuracy in the light of LPP's powerful discriminative property.LBPHP method is superior to conventional method based on LBP not only on recognition speed but also on accuracy.It is prominent especially on large-scale face database and suitable for practical application e.g.,identity authentication.
2) In the aspect of expression feature extraction,the conventional LBP operators have several disadvantages such as rather long histograms produced by them,lower discrimination and sensitivity to noise.Aimed at the problems,it proposed the centralized binary pattern(CBP) operator.CBP operator has several advantages:(1) CBP operator captures the gradient information by comparing pairs of neighbors, which not only improves its discrimination but also reduces significantly the feature's dimensionality.(2) Its discrimination is strengthened due to considering the center pixel point and giving it the largest weight.(3) The CBP feature extracted from image is more robust and more stable in the noisy situation.Moreover,for the purpose of increasing recognition accuracy,it introduced the center-based nearest neighbor classifier into expression recognition.This kind of classifier is superior to the nearest neighbor classifer.
3) In order to increase the accuracy of expression recognition,it expanded CBP from the following aspects:(1) The gradient information was integrated into CBP.(2) Multi-scale CBP(MCBP) was proposed.(3) For the purpose of improving the robustness to small deformation of expressional images,it introduced image Euclidean distance(IMED) and embedded it in MCBP.MCBP-IMED is short for the approach of MCBP embedded with IMED.The feature extracted by using this method has advantages:lower dimension,very powerful discrimination,insensitivity to noise, robustness to small deformation.
4) It proposed the centralized Gabor binary pattern(CGBP) histogram,which combined CBP and Gabor transform,and integrated the gradient information into CGBP histogram.In order to reflect accurately the underlying structure of expressional manifold,it put forward the supervised Laplacianfaces(SLAP) which combined the ideas of local method and supervised method.Furthermore,because of the close relationship between facial expression and feeling,it proposed the expressional space model which combined the continuity and scatter of expressional space.Baesd on the model,SLAP was applied to CGBP histogram with gradient information to recognize expression and analyse expression components.
引文
[1]章毓晋.中国图像工程及当前的几个研究热点.计算机辅助设计与图形学学报,2002,14(6):489-500.
[2]杨涛,李子青,潘泉,等.基于在线特征选择的实时多姿态人脸跟踪.自动化学报,2008,34(1):14-20.
[3]刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法.自动化学报,2003,29(6):900-911.
[4]山世光,高文,唱轶钲,等.人脸识别中的“误配准灾难”问题研究.计算机学报,2005,28(5):782-791.
[5]卿来云,山世光,陈熙霖,等.基于球面谐波基图像的任意光照下的人脸识别.计算机学报,2006,29(5):760-768.
[6]宋刚,艾海舟,徐光祜.纹理约束下的人脸特征点跟踪.软件学报,2004,15(11):1607-1615.
[7]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(自然科学版),1997,37(3):67-70.
[8]周杰,卢春雨,张长水,等.人脸自动识别方法综述.电子学报,2000,28(4):102-106.
[9]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.电子学报,2002,30(9):1292-1294.
[10]薛峰,丁晓青.基于形状匹配变形模型的三维人脸重构.电子学报,2006,34(10):1896-1899.
[11]Yan S,Zhang H,Hu Y,et al.Discriminant analysis on embedded manifold.European Conference on Computer Vision,2004:121-132.
[12]宋枫溪,杨静宇,刘树海,等.基于多类最大散度差的人脸表示方法.自动化学报,2006,32(3):378-385.
[13]高秀梅,杨静宇,金忠,等.基于核的Foley-Sammon鉴别分析与人脸识别.计算机辅助设计与图形学学报,2004,16(7):962-967.
[14]杨健,杨静宇.具有统计不相关性的图像投影鉴别分析及人脸识别.计算机研究与发展,2003,40(3):447-452.
[15]何力,张军平,周志华.基于放大因子和延伸方向研究流形学习算法.计算机学报,2005,28(12):2000-2009.
[16]耿新,周志华,陈世福.基于混合投影函数的眼睛定位.软件学报,2003,14(8):1394-1400.
[17]韩松,潘纲,王跃明,等.三维鼻形:一种新的生物特征识别模式.计算机辅助设计与图形学学报,2008,20(1):38-42.
[18]林维训,潘纲,吴朝晖,等.脸部特征定位方法.中国图象图形学报,2003,8A(8):849-859.
[19]徐琳,袁保宗,高文.真实感人脸建模研究的进展与展望.软件学报,2003,14(4):804-810.
[20]陈家大,赖剑煌,冯国灿.一种人脸姿势判别与正脸合成的新方法.计算机研究与发展,2006,43(8):1477-1484.
[21]刘党辉,沈兰荪,Kin-Man Lam.人脸识别研究进展.电路与系统学报,2004,9(1):85-94.
[22]王成章,尹宝才,孙艳丰,等.改进的基于形变模型的三维人脸建模方法.自动化学报,2007,33(3):232-239.
[23]赵向阳,杜利民.人脸合成中模型的平滑调整和逼真的纹理映射.计算机辅助设计与图形学学报,2004,16(11):1517-1522.
[24]刘治,严京旗,施鹏飞,等.三维生物特征识别进展.计算机工程与应用,2006,17:1-3.
[25]裴玉茹,查红彬.真实感人脸的形状与表情空间.计算机辅助设计与图形学学报,2006,18(5):613-619.
[26]邢延超,谈正,林健文.具有最佳类间可分离性的弹性图匹配人脸验证新方 法.西安交通大学学报,2003,37(6):565-568.
[27]关鹏,张立明.基于贝赛尔曲面的三维人脸特征点定位方法.复旦学报(自然科学版),2008,47(1):117-123.
[28]龙飞,庄连生,庄镇泉.基于小波变换和Fisher判别分析的人脸识别方法.模式识别与人工智能,2005,18(2):223-227.
[29]Schapire R E.A brief introduction to boosting.In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence,September 1999,14(5):771-780.
[30]Schapire R E,Freund Y,Bartlett P,et al.Boosting the margin:A new explanation for the effectiveness of voting methods.The Annals of Statistics,1998,26(5):1651-1686.
[31]Schapire R E,Singer Y.Improved boosting algorithms using confidence-rated predictions.Machine Learning,1999,37(3):297-336.
[32]Schapire R E,Singer Y.BoostTexer:A system for mulitclass multi-label text categorization.Machine Learning,1998.
[33]Paul V,Michael J J.Rapid object detection using a boosted cascade of simple features.IEEE CVPR,2001,1:511-518.
[34]Zhang Z,Zhu L,Li C,et al.Real-time multi-view face detection.5th International Conference on Automatic Face and Gesture Recognition,May 2002:142-147.
[35]Fr(o｜¨)ba B,Kiiblbeck C.Real-time face detection using edge-orientation matching.AVBPA,2001:78-83.
[36]Fr(o｜¨)ba B,Ernst A.Fast frontal-view face detection using a multi-path decision tree.AVBPA,2003:921-928.
[37]Rowley H A,Baluja S,Kanade T.Neural network-based face detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1):23-28.
[38]Bledsoe W W.Man-machine facial recognition.Panoramic Research Inc,1966.
[39]Brunelli R,Poggio T.Face recognition:Feature versus templates.IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(10):1042-1052.
[40] Jain A, Huang J. Integrating independent components and linear discriminant analysis for gender classification. Automatic Face and Gesture Recognition, 2004, 159-163.
[41] Kirby M, Sirovich L. Application of the Karhunen2Loeve procedure for the charaterization of human faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990,12(1): 103-108.
[42] Turk M A, Pentland A P. Face recognition using eigenfaces. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1991: 586-591.
[43] Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290: 2323-2326.
[44] Tenenbaum J B, Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science, 2000, 290: 2319-2323.
[45] Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Dietterich T G, Becker S, Ghahramani Z. Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2002: 788-795.
[46] He X, Niyogi P. Locality preserving projections. Proc.Conf. Advances in Neural Information Processing Systems, 2003.
[47] He X F, Yan S C, Hu Y X, et al. Face recognition using laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340.
[48] Biederman I. Recognition by components: A theory of human images understanding. Psychol, 1987, 94(2): 115-147.
[49] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization. Nature, 1999,401(21): 788-791.
[50] Lee D D, Seung H S. Algorithms for non2negative matrix factorization. Proceedings of Neural Information Processing Systems, Denver, USA. 2000, 556-562.
[51] Li S Z, Hou X W, Zhang H J, et al. Learning spatially localized, parts-based representation. Proceedings of Computer Vision and Pattern Recognition, Hawaii, USA. 2001.
[52] Yang J, Zhang D, Frangi a F, 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.
[53] Deever a T, Hemami S S. Efficient sign coding and estimation of zero-quantized coefficients in embedded wavelet image codecs. IEEE Transactions on Image Processing, 2003,12(4): 420-430.
[54] Grgic S, Kers K, Grgic M. Image compression using wavelets. IEEE International Symposium on Industrial Electronics, 1999,1: 99-104.
[55] Lazar D, Averbuch A. Wavelet-based video coder via bit allocation. IEEE Transactions on Circuits and Systems for Video Technology, 2001,11(7): 815-832.
[56] Graps A. An introduction to wavelets. Computational Science and Engineering, 1995, 2(2): 50-61.
[57] Lades M, Vorbruggen J, Buhmann J, et al. Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers, 1993, 42(3): 300-311.
[58] Wiskott L, Fellous J M. Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7): 775-779.
[59] Liu C J. 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.
[60] Kohonen T. Self-organization and associative memory. 1988: Springer-Verlag.
[61] Lin S H, Kung S Y, Lin L J. Face recognition/detection by probabilistic decision-based nerual network. IEEE Transactions on Neural Networks, 1997, 8(1): 114-132.
[62] Meng J E, Shiqian W, Juwei L, et al. Face recognition with radial basis function(RBF) neural networks. IEEE Transactions on Neural Networks, 2002, 13(3): 697-710.
[63] Cootes T F, Edwards G J, Taylor C J. Active appearance models. In Proc. European Conference on Computer Vision, 1998, 2: 484-498.
[64] Cootes T F, Taylor C J, Cooper D, et al. Active shape models-Their training and application. Computer Vision and Image Understanding, 1995, 61(1): 38-59.
[65]史东承,谢玉鹏,吴莉,等.基于主动表观模型的人脸图像描述与编码.长春工业大学学报(自然科学版),2006,27(4):324-328.
[66]赵歆波,邹晓春.基于正交投影的主动外观模型匹配算法.西北工业大学学报,2008,26(2):168-172.
[67]Ojala T,Pietik(a|¨)inen M,M(a|¨)enp(a|¨)(a|¨) 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.
[68]Ahonen T,Hadid A,Pietik(a|¨)inen M.Face description with local binary patterns:Application to face recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(12):2037-2041.
[69]Bruce V,Hancock P J B,Burton A M.Human face perception and identification.In Face Recognition:From Theory to Applications,Wechsler H,Phillips P J,Bruce V,Soulie F F,and Huang T S,Eds.Springer-Verlag,Berlin,Germany,1998:51-72.
[70]Knight B,Johnston A.The role of movement in face recognition.Vis.Cog.4,1997:265-274.
[71]Zhao W,Chellappa R,Phillips P,et al.Face recognition:A literature survey.ACM Compution Survey,December Issue,2003:399-458.
[72]Choudhury T,Clarkson B,Jebara T,et al.Multimodal person recognition using unconstrained audio and video.Inernational conference on audio- and video-based person authentication,1999:176-181.
[73]Bigun J,Duc B,Smeraldi F,et al.Multi-modal person authentication.In Face Recognition:From Theory to Applications,Wechsler H,Phillips P J,Bruce V,Soulie F F,and Huang T S,Eds.Springer-Verlag,Berlin,Germany,1998:26-50.
[74]Zhou S,Krueger V,Chellappa R.Probabilistic recognition of human faces from video.Computer Vision and Image Understanding,2003.
[75]Li S Z,Jain A K.Handbook of face recognition.Editor by Li.S.Z,Jain,A.K.Springer Verlag,New York,2005.
[76]Sim T,Baker S,Bsat M.The CMU Pose,Illumination,and Expression(PIE)Database.Processiong of the IEEE International conference on automatic face and gesture recognition,May 2002.
[77]Chellappa R,Wilson C L,Sirohey S.Human and machine recognition of faces:A survey.In:Proceedings of the IEEE,1995,83(5):705-740.
[78]Phillips P J,Wechsler H,Huang J,et al.The FERET database and evaluation procedure for face recognition algorithms.Image and Vision Computing J,1998,16(5):295-306.
[79]Phillips P J,Moon H,Rizvi S A,et al.The FERET evaluation methodology for face recognition algorithms.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22:1090-1104.
[80]Gao W,Cao B,Shan S,et al.The CAS-PEAL large-scale chinese face database and evaluation protocols.Technical Report No.JDL_TR 04 FR_001,Joint Research & Development Laboratory,CAS,2004.
[81]高文,金辉.面部表情图像的分析与识别.计算机学报,1997,20(9):782-789.
[82]金辉,高文.人脸面部混合表情识别系统.计算机学报,2000,23(6):602-608.
[83]金辉,高文.基于HMM的面部表情图像序列的分析与识别.自动化学报,2002,28(4):646-650.
[84]金辉,高文.基于特征流的面部表情运动分析及应用.软件学报,2003,14(12):2098-2105.
[85]余冰,金连甫.基于特征运动的表情人脸识别.中国图象图形学报:A辑,2002,7(11):1139-1143.
[86]周川,林学闷.基于核函数因素分解模型的表情合成与识别.清华大学学报(自然科学版),2006,46(10):1751-1754.
[87]朱健翔,苏光大,李迎春.结合Gabor特征与Adaboost的人脸表情识别.光电子·激光,2006,17(8):993-998.
[88]刘晓曼,章毓晋.基于Gabor直方图特征和MVBoost的人脸表情识别.计算机研究与发展,2007,44(7):1089-1096.
[89]武宇文,刘宏,查红彬.基于特征分组加权聚类的表情识别.计算机辅助设计与图形学学报,2005,17(11):2394-2401.
[90]陈国社,张青,李凡.基于多姿态多状态面部情绪模型的表情识别.华中科技大学学报(自然科学版),2004,32(8):60-62.
[91]杨国亮,王志良,刘冀伟,等.一种改进的HMM训练算法及其在面部表情识别中的应用.计算机科学,2006,33(11):200-204.
[92]姜璐,章品正,舒华忠.矩在面部表情识别中的应用.东南大学学报:自然科学版,2004,34(4):557-560.
[93]孙宁,冀贞海,邹采荣,等.基于2维偏最小二乘法的图像局部特征提取及其在面部表情识别中的应用.中国图象图形学报,2007,12(5):847-853.
[94]左坤隆,刘文耀.基于活动外观模型的人脸表情分析与识别.光电子·激光,2004,15(7):853-857.
[95]刘伟锋,汪增福.基于Gabor特征和混合高斯分布模型的人脸表情分析.计算机工程与应用,2008,44(26):191-195.
[96]张颖,罗森林.情感建模与情感识别.计算机工程与应用,2003,39(33):98-102.
[97]Feng X,Pietik(a|¨)inen M,Hadid A.Facial expression recognition with local binary patterns and linear programming.Pattern Recognition and Image Analysis,2005,15(2):546-548.
[98]崔洁,冯晓毅.基于粗略到精细分类的面部表情识别方法.计算机工程,2007,33(5):168-169.
[99]邓洪波,金连文.一种基于局部Gabor滤波器组及PCA+LDA的人脸表情识别方法.中国图象图形学报,2007,12(2):322-329.
[100]第一届中国情感计算及智能交互学术会议论文集,北京,2003.
[101]王志良.人工心理学--关于更接近人脑工作模式的科学.北京科技大学学报,2000,10:478-481.
[102]Yang M,Kriegman D J,Ahuja N.Detecting faces in images:A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(1):34-58.
[103]Pantic M,Rothkrantz L.Facial action recognition for facial expression analysis from static face images.IEEE Transactions on Systems,Man and Cybernetics-Part B,2004,34(3):1449-1461.
[104]Donato G,Barlett S,Hager C,et al.Classifying facial actions.IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(10):974-989.
[105]Wen Z,Huang T.Capturing subtle facial motions in 3d face tracking.In:Proceedings of IEEE International Conference on Computer Vision,Nice,France,2003,2:1343-1350.
[106]姚伟,孙正兴,张岩.面向脸部表情识别的Gabor特征选择方法.计算机辅助设计与图形学学报,2008,20(1):79-84.
[107]Wang H,Ahuja N.Facial expression decomposition.In:Proceedings of IEEE International Conference on Computer Vision,Nice,France,2003,2:958-965.
[108]Zalewski L,Gong S.Synthesis and recognition of facial expressions in virtual 3D views.In:Proceedings of IEEE 6~(th) International Conference on Automatic Face and Gesture Recognition,Seoul,Korea,2004:49-498.
[109]Shan C,Gong S,McOwan P W.Robust facial expression recognition using local binary patterns.IEEE International Conference on Image Processing,2005,2:Ⅱ-370-3.
[110]Seung H S,Lee D D.The manifold ways of perception.Science,2000,290:2268-2269.
[111]Dubuisson S,Devoine F,Masson M.A solution for facial expression representation and recognition.Signal Processing:Image Communicationg,2002,17(9):657-673.
[112]李睿凡,朱强生,郭燕慧,等.鲁棒局部保持投影的表情识别.北京邮电大学学报,2006,29(增刊):178-206.
[113]Bourel F,Chibelushi C C,Low A A.Robust facial expression recognition using a state-based model of spatially-localized facial dynamics.In:Proceeding of IEEE International Conference on Automatic Face and Gesture Recognition,Washington,DC,USA,2002:106-111.
[114]Zhang Y,Ji Q.Active and dynamic information fusion for facial expression understanding from image sequences.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(5):699-714.
[115]武宇文.基于脸部二维形状与结构特征的表情识别研究.北京大学博士学位论文,2005.
[116]Fellenz W,Taylor J,Tsapatsoulis N,et al.Comparing template-based,feature-based and supervised classification of facial expression from static images.Proceedings of Circuits,Systems,Communication and Computers(CSCC'99),Nugata,Japan,1999:5331-5336.
[117]Lisetti C,Rumelhart D.Facial expression recognition using a neural network.Proceedings of the 11th International Flairs Conferenc,AAAI Press,New York,1998.
[118]Dailey M,Cottrel G.PCA Gabor for expression recognition.Institution UCSD,Number CS-629,1999.
[119]Cottrel G W,Metcalfe J.EMPATH:face,emotion,gender recognition using holons.Advances in Neural Information Processing Systems 3,R.P.Lippman,ed.,1991:564-571.
[120]Kaiser S,Wehrle T.Automated coding of facial behavior in human-computer international with FACS.J.Nonverbal Behavior,1992,16(2):67-83.
[121]Calder A J,Burton A M,Miller P,et al.A principal component analysis of facial expressions.Vision Research,2001,41:1179-1208.
[122]Michel P,Kaliouby R E.Real time facial expression recognition in video using support vector machines.Proceediings of the 5th International Conference on Multimodal Interfaces,Vancouver,British Columbia,Canada,2003:258-264.
[123]Chen X W,Huang T.Facial expression recognition:a clustering-based approach.Journal of Pattern Recognition Letter,2003,24:1295-1302.
[124]Bsrtlett M S,Littlewort G,Frank M,et al.Recognizing facial expression:machine learning and application to spontaneous behavior.In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Diego,CA,USA,2005,2:568-573.
[125]Wang Y,Ai H Z,Wu B,et al.Real time facial expression recognition with AdaBoost.Proceedings of the 17th International Conference on Pattern Recognition(ICPR'04),2004,3:23-26.
[126]Yacoob Y,Davis L S.Recognizing human facial expression from long image sequences using optical flow.IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(6):636-642.
[127]Muller S,Wllhoff F,Hulsken F,et al.Facial expression recognition using pseudo 3-D hidden Markov models.In:Proceedings of International Conference on Pattern Recognition,Quebec City,Canada,2002,2:32-35.
[128]Rosenblum M,Yacoob Y,Davis L.Human expression recognition from motion using a radial basis function network architecture.IEEE Transactions on Neural Networks,1996,7(5):1121-1138.
[129]Kobayashi H,Hara E Dynamic recognition of basic facial expression by discrete-time recurrent neural network.Proceedings of the International Joint Conference on Neural Networks,1993:155-158.
[130]Essa I,Pentland A.Coding,analysis interpretation,recognition of facial expression.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):757-763.
[131]Chang Y,Hu C B,Turk M.Probabilistic expression analysis on manifolds.Proc.IEEE Conf.on Computer Vision and Pattern Recognition.2004,2:Ⅱ-520 - Ⅱ-527.
[132]Lyons M J,Akamatsu S,Kamachi M,et al.Coding facial expressions with Gabor wavelets.Proceedings,Third IEEE International Conference on Automatic Face and Gesture Recognition.Nara Japan,IEEE Computer Society,1998,200-205.
[133]Kanade T,Cohn J F,Tian Y.Comprehensive database for facial expression analysis.Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition(FG'00).Grenoble,France,2000,46-53.
[134]曹林,王东峰,刘小军,等.基于二维Gabor小波的人脸识别算法.电子与信息学报,2006,28(3):490-494.
[135]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.
[136]陈伏兵,陈秀宏,张生亮,等.基于模块2DPCA的人脸识别方法.中国图象图形学报,2006,11(4):580-585.
[137]Belhumeur P N,Hespanha J P,Kriegman D J.Eigenfaces vs.Fisherfaces: recognition using class specific linear projection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720.
[138]Yang H,Wang Y.A LBP-based face recognition method with hamming distance constraint.Fourth International Conference on Image and Graphics.Chengdu:IEEE,2007:645-649.
[139]Ahonen T,Pietik(a｜¨)inen M,Hadid A,et al.Face recognition based on the appearance of local regions.Proceedings of the 17th International Conference on Pattern Recognition.Cambridge:IEEE,2004,3:153-156.
[140]Ahonen T,Hadid A,Pietik(a｜¨)inen M.Face recognition with local binary patterns.Proc.Eighth European Conf.Computer Vision,2004:469-481.
[141]曹林,王东峰,刘小军,等.基于Gabor小波和HMM的人像鉴别算法.控制与决策,2005,20(9):1073-1076.
[142]朱健翔,苏光大,李迎春.结合Gabor特征与Adaboost的人脸表情识别.光电子·激光,2006,17(8):993-998.
[143]左坤隆,刘文耀.基于活动外观模型的人脸表情分析与识别.光电子·激光,2004,15(7):853-857.
[144]Chen P J,Wang G Y,Yang Y,et al.Facial expression recognition based on rough set theory and SVM.Proceedings of First International Conference on Rough Sets and Knowledge Technology,2006,4062:772-777.
[145]崔洁,冯晓毅.一种新的人面部表情识别方法.计算机工程与应用,2006,42(29):78-80.
[146]Duda R O,Hart P E,Stork.D G.Pattern Classification(Second Edition).2004,Beijing:China Machine Press.
[147]Li Stan Z,Lu Juwei.Face recognition using the nearest feature line method.IEEE Transactions on Neural Networks,1999,10(2):439-443.
[148]Gao Q,Wang Z.Center-based nearest neighbor classifier.Pattern Recognition,2007,40:346-349.
[149]Wang L,Zhang Y,Feng J.On the Euclidean distance of image.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(8):1334-1339.
[150] Simard P, Cun Y L, Dender J. Efficient pattern recognition using a new transformation distance. Advances in Neural Information Processing Systems, S.Hanson, J.Cowan, and C.Giles eds., pp. 50-58. San Mateo, Calif.: Morgan Kaufman, 1993.
[151] Huttenlocher D P, Klanderman G A, Rucklidge W J. Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993,15(9): 850-863.
[152] Bajcsy R, Lovacic S. Multiresolution elastic mathing. Computer Vision, Graphics, and Image Processing, 1989, 46:1-21.
[153] Li J, Chen G., Chi Z. A fuzzy image metric with application to fractal coding. IEEE Transactions on Image Processing, 2002,11(6): 636-643.
[154] Lin Daw-Tung. Facial expression classification using PCA and hierarchical radial basis function network. Journal of Information Science and Engineering, 2006, 22(5): 1033-1046.
[155] Sun W, Ruan Q. Two-dimension PCA for facial expression recognition. The 8th International Conference on Signal Processing. 2006, 3:16-20.
[156] Fisher R A. The use of multiple measures in taxonomic problems. Ann. Eugenics, 1936, 7:179-188.
[157] Saul L K, Roweis S T. Think globally, fit locally: unsupervised learning of low dimensional manifolds. Machine Learning Research, 2003, 4:119-155.
[158] Cox T, Cox M. Multidimensional Scaling. 1994, London: Chapman&Hall.
[159] Zhang Z, Zha H. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM Journal of Scientific Computing, 2004, 26(1): 313-338.
[160] Porat M, Zeevi Y. The generalized Gabor scheme of image representation in biological and machine vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988,10(4): 452- 468.
[2]杨涛,李子青,潘泉,等.基于在线特征选择的实时多姿态人脸跟踪.自动化学报,2008,34(1):14-20.
[3]刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法.自动化学报,2003,29(6):900-911.
[4]山世光,高文,唱轶钲,等.人脸识别中的“误配准灾难”问题研究.计算机学报,2005,28(5):782-791.
[5]卿来云,山世光,陈熙霖,等.基于球面谐波基图像的任意光照下的人脸识别.计算机学报,2006,29(5):760-768.
[6]宋刚,艾海舟,徐光祜.纹理约束下的人脸特征点跟踪.软件学报,2004,15(11):1607-1615.
[7]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(自然科学版),1997,37(3):67-70.
[8]周杰,卢春雨,张长水,等.人脸自动识别方法综述.电子学报,2000,28(4):102-106.
[9]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.电子学报,2002,30(9):1292-1294.
[10]薛峰,丁晓青.基于形状匹配变形模型的三维人脸重构.电子学报,2006,34(10):1896-1899.
[11]Yan S,Zhang H,Hu Y,et al.Discriminant analysis on embedded manifold.European Conference on Computer Vision,2004:121-132.
[12]宋枫溪,杨静宇,刘树海,等.基于多类最大散度差的人脸表示方法.自动化学报,2006,32(3):378-385.
[13]高秀梅,杨静宇,金忠,等.基于核的Foley-Sammon鉴别分析与人脸识别.计算机辅助设计与图形学学报,2004,16(7):962-967.
[14]杨健,杨静宇.具有统计不相关性的图像投影鉴别分析及人脸识别.计算机研究与发展,2003,40(3):447-452.
[15]何力,张军平,周志华.基于放大因子和延伸方向研究流形学习算法.计算机学报,2005,28(12):2000-2009.
[16]耿新,周志华,陈世福.基于混合投影函数的眼睛定位.软件学报,2003,14(8):1394-1400.
[17]韩松,潘纲,王跃明,等.三维鼻形:一种新的生物特征识别模式.计算机辅助设计与图形学学报,2008,20(1):38-42.
[18]林维训,潘纲,吴朝晖,等.脸部特征定位方法.中国图象图形学报,2003,8A(8):849-859.
[19]徐琳,袁保宗,高文.真实感人脸建模研究的进展与展望.软件学报,2003,14(4):804-810.
[20]陈家大,赖剑煌,冯国灿.一种人脸姿势判别与正脸合成的新方法.计算机研究与发展,2006,43(8):1477-1484.
[21]刘党辉,沈兰荪,Kin-Man Lam.人脸识别研究进展.电路与系统学报,2004,9(1):85-94.
[22]王成章,尹宝才,孙艳丰,等.改进的基于形变模型的三维人脸建模方法.自动化学报,2007,33(3):232-239.
[23]赵向阳,杜利民.人脸合成中模型的平滑调整和逼真的纹理映射.计算机辅助设计与图形学学报,2004,16(11):1517-1522.
[24]刘治,严京旗,施鹏飞,等.三维生物特征识别进展.计算机工程与应用,2006,17:1-3.
[25]裴玉茹,查红彬.真实感人脸的形状与表情空间.计算机辅助设计与图形学学报,2006,18(5):613-619.
[26]邢延超,谈正,林健文.具有最佳类间可分离性的弹性图匹配人脸验证新方 法.西安交通大学学报,2003,37(6):565-568.
[27]关鹏,张立明.基于贝赛尔曲面的三维人脸特征点定位方法.复旦学报(自然科学版),2008,47(1):117-123.
[28]龙飞,庄连生,庄镇泉.基于小波变换和Fisher判别分析的人脸识别方法.模式识别与人工智能,2005,18(2):223-227.
[29]Schapire R E.A brief introduction to boosting.In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence,September 1999,14(5):771-780.
[30]Schapire R E,Freund Y,Bartlett P,et al.Boosting the margin:A new explanation for the effectiveness of voting methods.The Annals of Statistics,1998,26(5):1651-1686.
[31]Schapire R E,Singer Y.Improved boosting algorithms using confidence-rated predictions.Machine Learning,1999,37(3):297-336.
[32]Schapire R E,Singer Y.BoostTexer:A system for mulitclass multi-label text categorization.Machine Learning,1998.
[33]Paul V,Michael J J.Rapid object detection using a boosted cascade of simple features.IEEE CVPR,2001,1:511-518.
[34]Zhang Z,Zhu L,Li C,et al.Real-time multi-view face detection.5th International Conference on Automatic Face and Gesture Recognition,May 2002:142-147.
[35]Fr(o｜¨)ba B,Kiiblbeck C.Real-time face detection using edge-orientation matching.AVBPA,2001:78-83.
[36]Fr(o｜¨)ba B,Ernst A.Fast frontal-view face detection using a multi-path decision tree.AVBPA,2003:921-928.
[37]Rowley H A,Baluja S,Kanade T.Neural network-based face detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1):23-28.
[38]Bledsoe W W.Man-machine facial recognition.Panoramic Research Inc,1966.
[39]Brunelli R,Poggio T.Face recognition:Feature versus templates.IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(10):1042-1052.
[40] Jain A, Huang J. Integrating independent components and linear discriminant analysis for gender classification. Automatic Face and Gesture Recognition, 2004, 159-163.
[41] Kirby M, Sirovich L. Application of the Karhunen2Loeve procedure for the charaterization of human faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990,12(1): 103-108.
[42] Turk M A, Pentland A P. Face recognition using eigenfaces. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1991: 586-591.
[43] Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290: 2323-2326.
[44] Tenenbaum J B, Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science, 2000, 290: 2319-2323.
[45] Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Dietterich T G, Becker S, Ghahramani Z. Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2002: 788-795.
[46] He X, Niyogi P. Locality preserving projections. Proc.Conf. Advances in Neural Information Processing Systems, 2003.
[47] He X F, Yan S C, Hu Y X, et al. Face recognition using laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340.
[48] Biederman I. Recognition by components: A theory of human images understanding. Psychol, 1987, 94(2): 115-147.
[49] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization. Nature, 1999,401(21): 788-791.
[50] Lee D D, Seung H S. Algorithms for non2negative matrix factorization. Proceedings of Neural Information Processing Systems, Denver, USA. 2000, 556-562.
[51] Li S Z, Hou X W, Zhang H J, et al. Learning spatially localized, parts-based representation. Proceedings of Computer Vision and Pattern Recognition, Hawaii, USA. 2001.
[52] Yang J, Zhang D, Frangi a F, 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.
[53] Deever a T, Hemami S S. Efficient sign coding and estimation of zero-quantized coefficients in embedded wavelet image codecs. IEEE Transactions on Image Processing, 2003,12(4): 420-430.
[54] Grgic S, Kers K, Grgic M. Image compression using wavelets. IEEE International Symposium on Industrial Electronics, 1999,1: 99-104.
[55] Lazar D, Averbuch A. Wavelet-based video coder via bit allocation. IEEE Transactions on Circuits and Systems for Video Technology, 2001,11(7): 815-832.
[56] Graps A. An introduction to wavelets. Computational Science and Engineering, 1995, 2(2): 50-61.
[57] Lades M, Vorbruggen J, Buhmann J, et al. Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers, 1993, 42(3): 300-311.
[58] Wiskott L, Fellous J M. Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7): 775-779.
[59] Liu C J. 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.
[60] Kohonen T. Self-organization and associative memory. 1988: Springer-Verlag.
[61] Lin S H, Kung S Y, Lin L J. Face recognition/detection by probabilistic decision-based nerual network. IEEE Transactions on Neural Networks, 1997, 8(1): 114-132.
[62] Meng J E, Shiqian W, Juwei L, et al. Face recognition with radial basis function(RBF) neural networks. IEEE Transactions on Neural Networks, 2002, 13(3): 697-710.
[63] Cootes T F, Edwards G J, Taylor C J. Active appearance models. In Proc. European Conference on Computer Vision, 1998, 2: 484-498.
[64] Cootes T F, Taylor C J, Cooper D, et al. Active shape models-Their training and application. Computer Vision and Image Understanding, 1995, 61(1): 38-59.
[65]史东承,谢玉鹏,吴莉,等.基于主动表观模型的人脸图像描述与编码.长春工业大学学报(自然科学版),2006,27(4):324-328.
[66]赵歆波,邹晓春.基于正交投影的主动外观模型匹配算法.西北工业大学学报,2008,26(2):168-172.
[67]Ojala T,Pietik(a|¨)inen M,M(a|¨)enp(a|¨)(a|¨) 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.
[68]Ahonen T,Hadid A,Pietik(a|¨)inen M.Face description with local binary patterns:Application to face recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(12):2037-2041.
[69]Bruce V,Hancock P J B,Burton A M.Human face perception and identification.In Face Recognition:From Theory to Applications,Wechsler H,Phillips P J,Bruce V,Soulie F F,and Huang T S,Eds.Springer-Verlag,Berlin,Germany,1998:51-72.
[70]Knight B,Johnston A.The role of movement in face recognition.Vis.Cog.4,1997:265-274.
[71]Zhao W,Chellappa R,Phillips P,et al.Face recognition:A literature survey.ACM Compution Survey,December Issue,2003:399-458.
[72]Choudhury T,Clarkson B,Jebara T,et al.Multimodal person recognition using unconstrained audio and video.Inernational conference on audio- and video-based person authentication,1999:176-181.
[73]Bigun J,Duc B,Smeraldi F,et al.Multi-modal person authentication.In Face Recognition:From Theory to Applications,Wechsler H,Phillips P J,Bruce V,Soulie F F,and Huang T S,Eds.Springer-Verlag,Berlin,Germany,1998:26-50.
[74]Zhou S,Krueger V,Chellappa R.Probabilistic recognition of human faces from video.Computer Vision and Image Understanding,2003.
[75]Li S Z,Jain A K.Handbook of face recognition.Editor by Li.S.Z,Jain,A.K.Springer Verlag,New York,2005.
[76]Sim T,Baker S,Bsat M.The CMU Pose,Illumination,and Expression(PIE)Database.Processiong of the IEEE International conference on automatic face and gesture recognition,May 2002.
[77]Chellappa R,Wilson C L,Sirohey S.Human and machine recognition of faces:A survey.In:Proceedings of the IEEE,1995,83(5):705-740.
[78]Phillips P J,Wechsler H,Huang J,et al.The FERET database and evaluation procedure for face recognition algorithms.Image and Vision Computing J,1998,16(5):295-306.
[79]Phillips P J,Moon H,Rizvi S A,et al.The FERET evaluation methodology for face recognition algorithms.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22:1090-1104.
[80]Gao W,Cao B,Shan S,et al.The CAS-PEAL large-scale chinese face database and evaluation protocols.Technical Report No.JDL_TR 04 FR_001,Joint Research & Development Laboratory,CAS,2004.
[81]高文,金辉.面部表情图像的分析与识别.计算机学报,1997,20(9):782-789.
[82]金辉,高文.人脸面部混合表情识别系统.计算机学报,2000,23(6):602-608.
[83]金辉,高文.基于HMM的面部表情图像序列的分析与识别.自动化学报,2002,28(4):646-650.
[84]金辉,高文.基于特征流的面部表情运动分析及应用.软件学报,2003,14(12):2098-2105.
[85]余冰,金连甫.基于特征运动的表情人脸识别.中国图象图形学报:A辑,2002,7(11):1139-1143.
[86]周川,林学闷.基于核函数因素分解模型的表情合成与识别.清华大学学报(自然科学版),2006,46(10):1751-1754.
[87]朱健翔,苏光大,李迎春.结合Gabor特征与Adaboost的人脸表情识别.光电子·激光,2006,17(8):993-998.
[88]刘晓曼,章毓晋.基于Gabor直方图特征和MVBoost的人脸表情识别.计算机研究与发展,2007,44(7):1089-1096.
[89]武宇文,刘宏,查红彬.基于特征分组加权聚类的表情识别.计算机辅助设计与图形学学报,2005,17(11):2394-2401.
[90]陈国社,张青,李凡.基于多姿态多状态面部情绪模型的表情识别.华中科技大学学报(自然科学版),2004,32(8):60-62.
[91]杨国亮,王志良,刘冀伟,等.一种改进的HMM训练算法及其在面部表情识别中的应用.计算机科学,2006,33(11):200-204.
[92]姜璐,章品正,舒华忠.矩在面部表情识别中的应用.东南大学学报:自然科学版,2004,34(4):557-560.
[93]孙宁,冀贞海,邹采荣,等.基于2维偏最小二乘法的图像局部特征提取及其在面部表情识别中的应用.中国图象图形学报,2007,12(5):847-853.
[94]左坤隆,刘文耀.基于活动外观模型的人脸表情分析与识别.光电子·激光,2004,15(7):853-857.
[95]刘伟锋,汪增福.基于Gabor特征和混合高斯分布模型的人脸表情分析.计算机工程与应用,2008,44(26):191-195.
[96]张颖,罗森林.情感建模与情感识别.计算机工程与应用,2003,39(33):98-102.
[97]Feng X,Pietik(a|¨)inen M,Hadid A.Facial expression recognition with local binary patterns and linear programming.Pattern Recognition and Image Analysis,2005,15(2):546-548.
[98]崔洁,冯晓毅.基于粗略到精细分类的面部表情识别方法.计算机工程,2007,33(5):168-169.
[99]邓洪波,金连文.一种基于局部Gabor滤波器组及PCA+LDA的人脸表情识别方法.中国图象图形学报,2007,12(2):322-329.
[100]第一届中国情感计算及智能交互学术会议论文集,北京,2003.
[101]王志良.人工心理学--关于更接近人脑工作模式的科学.北京科技大学学报,2000,10:478-481.
[102]Yang M,Kriegman D J,Ahuja N.Detecting faces in images:A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(1):34-58.
[103]Pantic M,Rothkrantz L.Facial action recognition for facial expression analysis from static face images.IEEE Transactions on Systems,Man and Cybernetics-Part B,2004,34(3):1449-1461.
[104]Donato G,Barlett S,Hager C,et al.Classifying facial actions.IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(10):974-989.
[105]Wen Z,Huang T.Capturing subtle facial motions in 3d face tracking.In:Proceedings of IEEE International Conference on Computer Vision,Nice,France,2003,2:1343-1350.
[106]姚伟,孙正兴,张岩.面向脸部表情识别的Gabor特征选择方法.计算机辅助设计与图形学学报,2008,20(1):79-84.
[107]Wang H,Ahuja N.Facial expression decomposition.In:Proceedings of IEEE International Conference on Computer Vision,Nice,France,2003,2:958-965.
[108]Zalewski L,Gong S.Synthesis and recognition of facial expressions in virtual 3D views.In:Proceedings of IEEE 6~(th) International Conference on Automatic Face and Gesture Recognition,Seoul,Korea,2004:49-498.
[109]Shan C,Gong S,McOwan P W.Robust facial expression recognition using local binary patterns.IEEE International Conference on Image Processing,2005,2:Ⅱ-370-3.
[110]Seung H S,Lee D D.The manifold ways of perception.Science,2000,290:2268-2269.
[111]Dubuisson S,Devoine F,Masson M.A solution for facial expression representation and recognition.Signal Processing:Image Communicationg,2002,17(9):657-673.
[112]李睿凡,朱强生,郭燕慧,等.鲁棒局部保持投影的表情识别.北京邮电大学学报,2006,29(增刊):178-206.
[113]Bourel F,Chibelushi C C,Low A A.Robust facial expression recognition using a state-based model of spatially-localized facial dynamics.In:Proceeding of IEEE International Conference on Automatic Face and Gesture Recognition,Washington,DC,USA,2002:106-111.
[114]Zhang Y,Ji Q.Active and dynamic information fusion for facial expression understanding from image sequences.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(5):699-714.
[115]武宇文.基于脸部二维形状与结构特征的表情识别研究.北京大学博士学位论文,2005.
[116]Fellenz W,Taylor J,Tsapatsoulis N,et al.Comparing template-based,feature-based and supervised classification of facial expression from static images.Proceedings of Circuits,Systems,Communication and Computers(CSCC'99),Nugata,Japan,1999:5331-5336.
[117]Lisetti C,Rumelhart D.Facial expression recognition using a neural network.Proceedings of the 11th International Flairs Conferenc,AAAI Press,New York,1998.
[118]Dailey M,Cottrel G.PCA Gabor for expression recognition.Institution UCSD,Number CS-629,1999.
[119]Cottrel G W,Metcalfe J.EMPATH:face,emotion,gender recognition using holons.Advances in Neural Information Processing Systems 3,R.P.Lippman,ed.,1991:564-571.
[120]Kaiser S,Wehrle T.Automated coding of facial behavior in human-computer international with FACS.J.Nonverbal Behavior,1992,16(2):67-83.
[121]Calder A J,Burton A M,Miller P,et al.A principal component analysis of facial expressions.Vision Research,2001,41:1179-1208.
[122]Michel P,Kaliouby R E.Real time facial expression recognition in video using support vector machines.Proceediings of the 5th International Conference on Multimodal Interfaces,Vancouver,British Columbia,Canada,2003:258-264.
[123]Chen X W,Huang T.Facial expression recognition:a clustering-based approach.Journal of Pattern Recognition Letter,2003,24:1295-1302.
[124]Bsrtlett M S,Littlewort G,Frank M,et al.Recognizing facial expression:machine learning and application to spontaneous behavior.In:Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,San Diego,CA,USA,2005,2:568-573.
[125]Wang Y,Ai H Z,Wu B,et al.Real time facial expression recognition with AdaBoost.Proceedings of the 17th International Conference on Pattern Recognition(ICPR'04),2004,3:23-26.
[126]Yacoob Y,Davis L S.Recognizing human facial expression from long image sequences using optical flow.IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(6):636-642.
[127]Muller S,Wllhoff F,Hulsken F,et al.Facial expression recognition using pseudo 3-D hidden Markov models.In:Proceedings of International Conference on Pattern Recognition,Quebec City,Canada,2002,2:32-35.
[128]Rosenblum M,Yacoob Y,Davis L.Human expression recognition from motion using a radial basis function network architecture.IEEE Transactions on Neural Networks,1996,7(5):1121-1138.
[129]Kobayashi H,Hara E Dynamic recognition of basic facial expression by discrete-time recurrent neural network.Proceedings of the International Joint Conference on Neural Networks,1993:155-158.
[130]Essa I,Pentland A.Coding,analysis interpretation,recognition of facial expression.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):757-763.
[131]Chang Y,Hu C B,Turk M.Probabilistic expression analysis on manifolds.Proc.IEEE Conf.on Computer Vision and Pattern Recognition.2004,2:Ⅱ-520 - Ⅱ-527.
[132]Lyons M J,Akamatsu S,Kamachi M,et al.Coding facial expressions with Gabor wavelets.Proceedings,Third IEEE International Conference on Automatic Face and Gesture Recognition.Nara Japan,IEEE Computer Society,1998,200-205.
[133]Kanade T,Cohn J F,Tian Y.Comprehensive database for facial expression analysis.Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition(FG'00).Grenoble,France,2000,46-53.
[134]曹林,王东峰,刘小军,等.基于二维Gabor小波的人脸识别算法.电子与信息学报,2006,28(3):490-494.
[135]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.
[136]陈伏兵,陈秀宏,张生亮,等.基于模块2DPCA的人脸识别方法.中国图象图形学报,2006,11(4):580-585.
[137]Belhumeur P N,Hespanha J P,Kriegman D J.Eigenfaces vs.Fisherfaces: recognition using class specific linear projection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720.
[138]Yang H,Wang Y.A LBP-based face recognition method with hamming distance constraint.Fourth International Conference on Image and Graphics.Chengdu:IEEE,2007:645-649.
[139]Ahonen T,Pietik(a｜¨)inen M,Hadid A,et al.Face recognition based on the appearance of local regions.Proceedings of the 17th International Conference on Pattern Recognition.Cambridge:IEEE,2004,3:153-156.
[140]Ahonen T,Hadid A,Pietik(a｜¨)inen M.Face recognition with local binary patterns.Proc.Eighth European Conf.Computer Vision,2004:469-481.
[141]曹林,王东峰,刘小军,等.基于Gabor小波和HMM的人像鉴别算法.控制与决策,2005,20(9):1073-1076.
[142]朱健翔,苏光大,李迎春.结合Gabor特征与Adaboost的人脸表情识别.光电子·激光,2006,17(8):993-998.
[143]左坤隆,刘文耀.基于活动外观模型的人脸表情分析与识别.光电子·激光,2004,15(7):853-857.
[144]Chen P J,Wang G Y,Yang Y,et al.Facial expression recognition based on rough set theory and SVM.Proceedings of First International Conference on Rough Sets and Knowledge Technology,2006,4062:772-777.
[145]崔洁,冯晓毅.一种新的人面部表情识别方法.计算机工程与应用,2006,42(29):78-80.
[146]Duda R O,Hart P E,Stork.D G.Pattern Classification(Second Edition).2004,Beijing:China Machine Press.
[147]Li Stan Z,Lu Juwei.Face recognition using the nearest feature line method.IEEE Transactions on Neural Networks,1999,10(2):439-443.
[148]Gao Q,Wang Z.Center-based nearest neighbor classifier.Pattern Recognition,2007,40:346-349.
[149]Wang L,Zhang Y,Feng J.On the Euclidean distance of image.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(8):1334-1339.
[150] Simard P, Cun Y L, Dender J. Efficient pattern recognition using a new transformation distance. Advances in Neural Information Processing Systems, S.Hanson, J.Cowan, and C.Giles eds., pp. 50-58. San Mateo, Calif.: Morgan Kaufman, 1993.
[151] Huttenlocher D P, Klanderman G A, Rucklidge W J. Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993,15(9): 850-863.
[152] Bajcsy R, Lovacic S. Multiresolution elastic mathing. Computer Vision, Graphics, and Image Processing, 1989, 46:1-21.
[153] Li J, Chen G., Chi Z. A fuzzy image metric with application to fractal coding. IEEE Transactions on Image Processing, 2002,11(6): 636-643.
[154] Lin Daw-Tung. Facial expression classification using PCA and hierarchical radial basis function network. Journal of Information Science and Engineering, 2006, 22(5): 1033-1046.
[155] Sun W, Ruan Q. Two-dimension PCA for facial expression recognition. The 8th International Conference on Signal Processing. 2006, 3:16-20.
[156] Fisher R A. The use of multiple measures in taxonomic problems. Ann. Eugenics, 1936, 7:179-188.
[157] Saul L K, Roweis S T. Think globally, fit locally: unsupervised learning of low dimensional manifolds. Machine Learning Research, 2003, 4:119-155.
[158] Cox T, Cox M. Multidimensional Scaling. 1994, London: Chapman&Hall.
[159] Zhang Z, Zha H. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM Journal of Scientific Computing, 2004, 26(1): 313-338.
[160] Porat M, Zeevi Y. The generalized Gabor scheme of image representation in biological and machine vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988,10(4): 452- 468.
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