笑脸表情分类识别的研究
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摘要
在人脸识别(Face Recognition,FR)研究领域,人脸特征提取作为一种重要的生物技术,成为实现智能人机交互的前提和关键技术之一。人脸表情特征提取和分类作为一个十分活跃的研究方向,具有很好的应用前景。随着人脸识别技术的进步,人脸表情识别技术取得了进步,但仍有很多关键性的问题需要进一步研究,比如光照、姿态、表情变化等。因此,从构建应用系统的角度看来,对人脸表情分类的研究仍处于起步阶段,相应的理论和方法仍有待完善。
从情感理解的角度来看,笑脸表情有着明显的特殊性,比较直接地反映了对象的心理状态,同时笑脸表情分类在电子类消费品当中目前已经成为了独立的一个应用领域。本文以笑脸表情分类方法为主题,研究了用于笑脸表情分类和识别的关键算法。典型的表情系统应该包括人脸检测、人脸特征提取、特征选择以及表情分类。由于特征提取对识别结果起着关键作用,本文将重点放在特征提取关键算法的研究上。作为一种小波变换方法,Gabor特征提取方法在人脸表情识别中得到成功应用。但Gabor特征的缺点是计算复杂度较高,同时Gabor特征用于分类识别的性能仍有提升的空间。我们将近年来在图像分类中应用广泛的PHOG(Pyramid Histogram of Oriented Gradients,PHOG)特征、生物启发特征(Biologically Inspired Model,BIM)特征以及局部二值模式(Locally Binary Patterns,LBP)特征引入笑脸表情识别中。本文还针对笑脸表情分类,融合、降维等问题进行了深入研究,研究内容与主要贡献如下:
1、本文对比了AdaBoost和支持向量机(Support Vector Machine,SVM)分类器进行人脸表情识别的性能,研究表明,AdaBoost的识别速度比SVM快,而准确度略低;SVM光照鲁棒性则比AdaBoost强,在光照变化较大的环境下,宜采用SVM;对于同时需要兼顾准确度和速度的场合,宜采用AdaBoost进行特征选择,再用SVM进行分类识别。实验结果表明,上述办法是有效的。
2、本文分析了基于Gabor小波变换的笑脸表情特征提取方法。对于传统降维方法而言,Gabor特征维数过高。本文提出了对嘴部表情区域进行金字塔分割,在此基础上进行金字塔梯度方向直方图(Pyramid Histogram of Oriented Gradients,PHOG)特征提取。我们假设HOG频谱分布与表情变化剧烈程度呈现正相关,峰值附近像素渲染较大的表情变化,因而对表情分类识别的贡献也较大。根据金字塔分割各区域所提取的梯度方向直方图(Histograms of Oriented Gradients,HOG),在提取Gabor特征的时候,相应地采用间隔采样技术。最终提取到更加有利于笑脸分类的Gabor特征。
3、本文分析了基于生物启发模型(Biologically Inspired Model,BIM)的表情特征提取方法,同时将PHOG特征与BIM特征进行了对比研究。对这两种特征融合后在GENKI数据库上进行笑脸分类的测试和评价,结果表明特征融合能显著提高笑脸分类识别性能。本文研究了BIM特征应用于笑脸分类的实施步骤,结果显示与应用在人脸识别上的已有成果相比具有类似的性能。结合对流形学习理论的研究,本文运用了一种线性流形学习方法,即局部保持投影(Local Preserving Projection,LPP)对BIM特征进行降维,保持了该特征的分类能力,同时提高了识别效率。较好地解决了表情特征提取与降维的问题。
4、提出了一个现实世界环境中的笑脸分类系统。三种类型的基本特征(即Gabor,PLBP和PHOG)分别提取出来并融合以后,馈入组合分类器(即AdaBoost+SVM)进一步分类识别。这种方法是在GENKI数据库上进行测试的,取得了高达86.197%的识别率。然而,以往文献研究显示基本特征加单一分类器的方法并没有取得十分理想的分类结果,部分原因在于这三种特征面临着维数过高的问题。与基本特征加上组合分类器方法的比较研究表明特征融合对性能的改进是显著的。
通过对分类器识别性能的对比研究,尤其是针对以上两种主要表情特征的对比研究,我们获得了笑脸表情图像分类识别的有效的技术路线和实验方法,在包括GENKI数据的在内的典型表情数据库中的实验结果表明,本文两种基本特征提取方法(即PHOG和BIM)在笑脸表情分类中具有良好的性能。得出的结论是:笑脸表情特征提取的性能主要受到数据采集的光照条件、人脸姿态等各种因素的影响,而为了获得较为鲁棒的系统,有必要将第三种类型的基本特征(即PLBP)提取出来并与上述两种基本方法进行特征级别融合,用于分类识别,才能获得较好的分类识别结果。
In the field of face recognition (FR), facial feature extraction as an important biological characteristic is a prerequisite and a key technology of intelligent human-computer interaction, facial expression extraction and facial feature classification has become a very active research direction, with good prospects. With the progress of face recognition technology, Facial Expression Recognition (FER) took advances. However, there are still many critical issues that need further study, such as lighting, head post, facial expression variation, and so on. Viewing from the point of building an effective expression recognition system, research of facial expression classification is still in its infancy, theory and method remains to be improved.
From the perspective of emotional understanding, smile expression has a distinct specificity, it reflects the psychological state of the object more directly, while smile expression classification has become a stand-alone applications at present in the electronic consumer goods. In this paper, we focus on the key classification and recognition algorithms, under the subject of smile expression classification. A typical facial expression classification system should contain face detection, face expression feature extraction, feature selection and expression classification. Deal to the great influence of feature extraction for the smile expression classification system, we focus on the key algorithms for feature extraction. As a wavelet transform, Gabor feature extraction method has been successfully applied in facial expression recognition. However, there is still room for improvement of the high computational complexity as well as the classification and recognition performance of the Gabor features. We introduce PHOG (Pyramid Histogram of Oriented Gradients) feature, BIM (Biologically Inspired Model) feature and LBP (Local Binary Patterns) to the smile expression recognition that are widely used in images classification recently years. Besides, this paper is in-depth study of the facial expression classification, features fusion, dimension reduction, etc. The content and main contributions are as follows:
1. This paper compares the AdaBoost and Support Vector Machine (SVM) classifier for the capability of facial expression classification. Research shown that, AdaBoost is faster than SVM in recognition speed, but the accuracy is slightly lower; while SVM is more lighting-robust than AdaBoost. In an environment with greater lighting changes, it's appropriate to adopt SVM rather than AdaBoost. When both speed and accuracy were needed, it would be appropriate to use them both by a fusing way, i.e., AdaBoost for feature selection, and SVM for classification. Experimental results shown the effectiveness of our methods.
2. This paper analyzes the Gabor wavelet transform based feature extraction method. To the traditional dimension reduction methods, Gabor feature dimension is too large. this paper proposes a Pyramid Histogram of Oriented Gradients (PHOG) expression feature extraction method based on pyramid segmentation in the mouth area. We assume that changes in the expression were positively correlated with HOG spectrum distribution and pixel nearer to peak intensity contributes more to demonstrate facial expression variation. Gabor feature is extracted according to the HOG spectrum distribution with a corresponding non-uniform sampling technique. Finally we got the Gabor feature that is more conducive to smile expression classification.
3. This paper analyzes the facial expression feature extraction method based on the biologically inspired model (BIM). A comparative study of BIM features and PHOG features was presented. Evaluations and tests of fusing the two features for smile expression classification was conducted in the GENKI database, results shown that the fusion features can significantly improve the smile expression classification performance. This paper studies the characteristics of BIM and its implementation applied to smile expression, results shown that similar performance could be achieved comparing with that of the face recognition using BIM features. Thanks to the manifold learning theories, a linear flow pattern learning method, i.e., locality preserving projection (LPP) was adopted to reduce the dimensionality of the BIM features, without losing its classification ability and efficiency. Thus, we solved the expression features extraction and dimensionality reduction problems.
4. This paper presents a smile expression classification system in real-world environment. Three types of features (i.e., Gabor, PLBP and PHOG) are extracted and fused before being fed into the combined classifiers (i.e., AdaBoost plus SVM) for further classification. This method is tested in the GENKI database, and achieved a recognition rate up to 86.197%. However, previous studies have shown that the basic feature with single classifier method did not achieve very good classification results. Part of the reason is that all of the three feature is confronted with the problem of a large feature dimension. Compared study of the basic feature with combined classifiers method shown that the fusion feature method improved the performance significantly.
Finally, it came up with the experimental methods for effectively smile image classification and recognition by the comparative study of the classifier performance mainly using the two features(i.e. PHOG and BIM). The results of the proposed methods are based on some typical expression databases including GENKI data. Experiments shown that the two feature extraction methods above could get good performances. We concluded that performance of the smile expression feature extraction are heavily dependent on data acquisition by light conditions, face poses and other factors. It is necessary to extract the PLBP features for feature-level fusion with the two features above in order to obtain better classification results.
从情感理解的角度来看,笑脸表情有着明显的特殊性,比较直接地反映了对象的心理状态,同时笑脸表情分类在电子类消费品当中目前已经成为了独立的一个应用领域。本文以笑脸表情分类方法为主题,研究了用于笑脸表情分类和识别的关键算法。典型的表情系统应该包括人脸检测、人脸特征提取、特征选择以及表情分类。由于特征提取对识别结果起着关键作用,本文将重点放在特征提取关键算法的研究上。作为一种小波变换方法,Gabor特征提取方法在人脸表情识别中得到成功应用。但Gabor特征的缺点是计算复杂度较高,同时Gabor特征用于分类识别的性能仍有提升的空间。我们将近年来在图像分类中应用广泛的PHOG(Pyramid Histogram of Oriented Gradients,PHOG)特征、生物启发特征(Biologically Inspired Model,BIM)特征以及局部二值模式(Locally Binary Patterns,LBP)特征引入笑脸表情识别中。本文还针对笑脸表情分类,融合、降维等问题进行了深入研究,研究内容与主要贡献如下:
1、本文对比了AdaBoost和支持向量机(Support Vector Machine,SVM)分类器进行人脸表情识别的性能,研究表明,AdaBoost的识别速度比SVM快,而准确度略低;SVM光照鲁棒性则比AdaBoost强,在光照变化较大的环境下,宜采用SVM;对于同时需要兼顾准确度和速度的场合,宜采用AdaBoost进行特征选择,再用SVM进行分类识别。实验结果表明,上述办法是有效的。
2、本文分析了基于Gabor小波变换的笑脸表情特征提取方法。对于传统降维方法而言,Gabor特征维数过高。本文提出了对嘴部表情区域进行金字塔分割,在此基础上进行金字塔梯度方向直方图(Pyramid Histogram of Oriented Gradients,PHOG)特征提取。我们假设HOG频谱分布与表情变化剧烈程度呈现正相关,峰值附近像素渲染较大的表情变化,因而对表情分类识别的贡献也较大。根据金字塔分割各区域所提取的梯度方向直方图(Histograms of Oriented Gradients,HOG),在提取Gabor特征的时候,相应地采用间隔采样技术。最终提取到更加有利于笑脸分类的Gabor特征。
3、本文分析了基于生物启发模型(Biologically Inspired Model,BIM)的表情特征提取方法,同时将PHOG特征与BIM特征进行了对比研究。对这两种特征融合后在GENKI数据库上进行笑脸分类的测试和评价,结果表明特征融合能显著提高笑脸分类识别性能。本文研究了BIM特征应用于笑脸分类的实施步骤,结果显示与应用在人脸识别上的已有成果相比具有类似的性能。结合对流形学习理论的研究,本文运用了一种线性流形学习方法,即局部保持投影(Local Preserving Projection,LPP)对BIM特征进行降维,保持了该特征的分类能力,同时提高了识别效率。较好地解决了表情特征提取与降维的问题。
4、提出了一个现实世界环境中的笑脸分类系统。三种类型的基本特征(即Gabor,PLBP和PHOG)分别提取出来并融合以后,馈入组合分类器(即AdaBoost+SVM)进一步分类识别。这种方法是在GENKI数据库上进行测试的,取得了高达86.197%的识别率。然而,以往文献研究显示基本特征加单一分类器的方法并没有取得十分理想的分类结果,部分原因在于这三种特征面临着维数过高的问题。与基本特征加上组合分类器方法的比较研究表明特征融合对性能的改进是显著的。
通过对分类器识别性能的对比研究,尤其是针对以上两种主要表情特征的对比研究,我们获得了笑脸表情图像分类识别的有效的技术路线和实验方法,在包括GENKI数据的在内的典型表情数据库中的实验结果表明,本文两种基本特征提取方法(即PHOG和BIM)在笑脸表情分类中具有良好的性能。得出的结论是:笑脸表情特征提取的性能主要受到数据采集的光照条件、人脸姿态等各种因素的影响,而为了获得较为鲁棒的系统,有必要将第三种类型的基本特征(即PLBP)提取出来并与上述两种基本方法进行特征级别融合,用于分类识别,才能获得较好的分类识别结果。
In the field of face recognition (FR), facial feature extraction as an important biological characteristic is a prerequisite and a key technology of intelligent human-computer interaction, facial expression extraction and facial feature classification has become a very active research direction, with good prospects. With the progress of face recognition technology, Facial Expression Recognition (FER) took advances. However, there are still many critical issues that need further study, such as lighting, head post, facial expression variation, and so on. Viewing from the point of building an effective expression recognition system, research of facial expression classification is still in its infancy, theory and method remains to be improved.
From the perspective of emotional understanding, smile expression has a distinct specificity, it reflects the psychological state of the object more directly, while smile expression classification has become a stand-alone applications at present in the electronic consumer goods. In this paper, we focus on the key classification and recognition algorithms, under the subject of smile expression classification. A typical facial expression classification system should contain face detection, face expression feature extraction, feature selection and expression classification. Deal to the great influence of feature extraction for the smile expression classification system, we focus on the key algorithms for feature extraction. As a wavelet transform, Gabor feature extraction method has been successfully applied in facial expression recognition. However, there is still room for improvement of the high computational complexity as well as the classification and recognition performance of the Gabor features. We introduce PHOG (Pyramid Histogram of Oriented Gradients) feature, BIM (Biologically Inspired Model) feature and LBP (Local Binary Patterns) to the smile expression recognition that are widely used in images classification recently years. Besides, this paper is in-depth study of the facial expression classification, features fusion, dimension reduction, etc. The content and main contributions are as follows:
1. This paper compares the AdaBoost and Support Vector Machine (SVM) classifier for the capability of facial expression classification. Research shown that, AdaBoost is faster than SVM in recognition speed, but the accuracy is slightly lower; while SVM is more lighting-robust than AdaBoost. In an environment with greater lighting changes, it's appropriate to adopt SVM rather than AdaBoost. When both speed and accuracy were needed, it would be appropriate to use them both by a fusing way, i.e., AdaBoost for feature selection, and SVM for classification. Experimental results shown the effectiveness of our methods.
2. This paper analyzes the Gabor wavelet transform based feature extraction method. To the traditional dimension reduction methods, Gabor feature dimension is too large. this paper proposes a Pyramid Histogram of Oriented Gradients (PHOG) expression feature extraction method based on pyramid segmentation in the mouth area. We assume that changes in the expression were positively correlated with HOG spectrum distribution and pixel nearer to peak intensity contributes more to demonstrate facial expression variation. Gabor feature is extracted according to the HOG spectrum distribution with a corresponding non-uniform sampling technique. Finally we got the Gabor feature that is more conducive to smile expression classification.
3. This paper analyzes the facial expression feature extraction method based on the biologically inspired model (BIM). A comparative study of BIM features and PHOG features was presented. Evaluations and tests of fusing the two features for smile expression classification was conducted in the GENKI database, results shown that the fusion features can significantly improve the smile expression classification performance. This paper studies the characteristics of BIM and its implementation applied to smile expression, results shown that similar performance could be achieved comparing with that of the face recognition using BIM features. Thanks to the manifold learning theories, a linear flow pattern learning method, i.e., locality preserving projection (LPP) was adopted to reduce the dimensionality of the BIM features, without losing its classification ability and efficiency. Thus, we solved the expression features extraction and dimensionality reduction problems.
4. This paper presents a smile expression classification system in real-world environment. Three types of features (i.e., Gabor, PLBP and PHOG) are extracted and fused before being fed into the combined classifiers (i.e., AdaBoost plus SVM) for further classification. This method is tested in the GENKI database, and achieved a recognition rate up to 86.197%. However, previous studies have shown that the basic feature with single classifier method did not achieve very good classification results. Part of the reason is that all of the three feature is confronted with the problem of a large feature dimension. Compared study of the basic feature with combined classifiers method shown that the fusion feature method improved the performance significantly.
Finally, it came up with the experimental methods for effectively smile image classification and recognition by the comparative study of the classifier performance mainly using the two features(i.e. PHOG and BIM). The results of the proposed methods are based on some typical expression databases including GENKI data. Experiments shown that the two feature extraction methods above could get good performances. We concluded that performance of the smile expression feature extraction are heavily dependent on data acquisition by light conditions, face poses and other factors. It is necessary to extract the PLBP features for feature-level fusion with the two features above in order to obtain better classification results.
引文
[1] Y.L. Tian, T. Kanade, J. Cohn. Handbook of face recognition[M]. Berlin, Heidelberg: Springer-Verlag, 2003:1-400.
[2] B. Fasel, J. Luettin. Automatic facial expression analysis: Survey[J]. Pattern Recognition, 2003, 36(1): 259-275.
[3] M. Pantic and L. Rothkrantz. Automatic analysis of facial expressions: the state of the art[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12): 1424-1445.
[4] P. Ekman, W.V. Friesen. Unmasking the face: A guide to recognizing emotions from facial clues[M]. Englewood Cliffs, N.J.: Prentice-Hall, 1975:1-167.
[5] P. Ekman. Emotion In the Human Face[M]. 2nd edition. New York: Pergamon Press, 1982:1-141.
[6] P. Ekman. Facial expression of emotion[J]. American Psychologist, 1993, 48(4): 384-392.
[7] P. Ekman, W.V. Friesen. Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage[M]. 3rd edition. New York, USA: W.W. Norton & Company, 2001:1-167.
[8] Z. Zeng, M. Pantic, G. Roisman, et al. A survey of affect recognition methods: Audio, visual, and spontaneous expressions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(1):39-58.
[9] L. Parr, B. M. Waller. Understanding chimpancee facial expression: insights into the evolution of communication[J]. Social Cognitive and Affective Neuroscience, 2006, 1(3): 221-228.
[10] M. Bartlett, G. Littlewort, M. Frank, et al. Fully automatic facial action recognition in spontaneous behavior[C]. Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG'06): University of Southampton,UK, 2006: 223-228.
[11] Y.B. Wang, H.Z. Ai, B. Wu, et al. Real time facial expression recognition with AdaBoost[C]. 17th International Conference on Pattern Recognition (ICPR'04): Cambridge, England, UK, 2004: 926-929.
[12] M. Pantic, J. Rothkrantz. Facial action recognition for facial expression analysis from static face images[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(3):1449-1461.
[13] P. Ekman, W.V. Friesen. Pictures of facial affect[M]. Palo Alto, CA, USA: Consulting Psychologists Press, 1975: 1-110.
[14] J.C. Hager, P. Ekman. Long distance transmission of facial affect signals[J]. Ethnology and Sociobiology, 1979, 1(1): 77-82.
[15] P. Ekman. W.V. Friesen. Felt, false, and miserable smiles[J]. Journal of Nonverbal Behavior, 1982, 6(4): 238-252.
[16] Y. Tian, T. Kanade, J. Cohn. Recognizing action units for facial expression analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2):97-115.
[17] A. Kapoor, Y. Qi, R.W. Picard. Fully automatic upper facial action recognition[C]. IEEE International Workshop on Analysis and Modeling of Faces and Gestures: Media Lab., MIT, Cambridge, MA, USA, 2003: 195-202.
[18] Kotsia, I. Pitas. Facial expression recognition in image sequences using geometric deformation features and support vector machines[J]. IEEE Transactions on Image Processing, 2007, 16(1):172-187.
[19] Z. Wen, T.H. Huang. Capturing subtle facial motions in 3D face tracking[C]. Ninth IEEE International Conference on Computer Vision (ICCV'03): Nice, France, 2003: 1343-1350.
[20] C. Cortes, M. Mohri. Confidence intervals for the area under the roc curve[C]. Advances in Neural Information Processing Systems: Vancouver, Canada, 2004: 17-28.
[21] J.R. Movellan. Tutorial on gabor filters[R]. MPLab Tutorials, UCSD MPLab, http://mplab.ucsd.edu/tutorials/gabor.pdf, 2005.
[22] M. Lades, J.C. Vorbruggen, J. Buhmann, et al. Distortion invariant object recognition in the dynamic link architecture[J]. IEEE Transactions on Computers, 1993, 42(3): 300-311.
[23] G. Donato, M.S. Bartlett, J.C. Hager, et al. Classifying facial actions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(10): 974-989.
[24] N. Sebe, Y. Sun, E. Bakker, et al. Towards authentic emotion recognition[C]. IEEE International Conference on Systems, Man and Cybernetics: The Hague, Netherlands: 2004: 623-628.
[25] J.F. Cohn, K.L. Schmidt. The timing of facial motion in posed and spontaneous smiles[J]. International Journal of Wavelets, Multiresolution and Information Processing (IJWMIP'04), 2004, 2(2): 121-132.
[26] Cohen, N. Sebe, A. Garg, et al. Facial expression recognition from video sequences: Temporal and static modeling[J]. Computer Vision and Image Understanding, Special Issue on Face Recognition, 2003, 91(1-2): 160-187.
[27] Y. Zhang, Q. Ji. Active and dynamic information fusion for facial expressionunderstanding from image sequences[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5): 699-714.
[28] P. Yang, Q. Liu, D. Metaxas. Boosting coded dynamic features for facial action units and facial expression recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition: Minneapolis, MN, USA: 2007: 688-693.
[29] M. J. McDonnell. Box-filtering techniques[J]. Computer Graphics and Image Processing, 1981, 17(1):65-70.
[30] S. Castan, J. Shen. Fast approximate realization of linear filters by translating cascading sum-box technique[C]. Proceedings of Computer Vision and Pattern Recognition, San Francisco, CA, USA: 1985, 678-680.
[31] Paul Viola, Michael Jones. Robust real-time object detection[J]. International Journal of Computer Vision, 2002,57(2):137-154.
[32] J. Whitehill, C.W. Omlin. Haar features for FACS AU recognition[C]. Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG'06), Los Alamitos: 2006, 97-101.
[33] Y. Tong, W.H. Liao, Q. Ji. Facial action unit recognition by exploiting their dynamic and semantic relationships[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(10):1683-1699.
[34] G.Y. Zhao, M. Pietika¨inen. Dynamic texture recognition using local binary patterns with an application to facial expressions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6):915-928.
[35] Z.W. Zhu, Q. Ji. Robust real-time face pose and facial expression recovery[J]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06): New York, NY: 2006, 1:681-688.
[36] T. Kanade, J. Cohn, Y.-L. Tian. Comprehensive database for facial expression analysis[C]. Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG'00): Grenoble, France, 2000: 46-53.
[37] T. Sim, S. Baker, M. Bsat. The CMU pose, illumination, and expression database[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12): 1615-1618.
[38] M. Pantic, M. Valstar, R. Rademaker, et al. Web-based database for facial expression analysis[C]. IEEE International Conference on Multimedia and Expo: Amsterdam, Netherlands: 2005: 5-5.
[39] A.J. O'Toole, J. Harms, S.L.Snow, et al. A video database of moving faces andpeople[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5): 812-816.
[40] P.J. Phillips, H. Moon, S.A. Rizvi, et al. The FERET evaluation methodology for face-recognition algorithms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(10):1090-1104.
[41] T. Kanade, J.F. Cohn, Y.L. Tian. Comprehensive database for facial expression analysis[C]. Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition: Grenoble, France, 2000: 46-53.
[42] Y. Freund, R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting[C]. Second Annual European Conference on Computational Learning Theory: Barcelona, Spain, 1995: 23-37.
[43] M. Pantic, L.J.M. Rothkranz. Expert system for automatic analysis of facial expression[J]. Image and Vision Computing, 2000, 18(11): 881-905.
[44] A. Mehrabian, N. Epstein. A measure of emotional empathy[J]. Journal of Personality, 1972, 40:525–543.
[45] C. Darwin. The expression of the emotions in man and animals[M]. London, UK: John Murray Press, 1872:1-367.
[46] P. Ekman, W.V. Friesen. Constants across cultures in the Face and emotion[J] .Journal of Personality and Social Psychology, 1971, 17(2) :124-129.
[47] P. Ekman, W.V. Friesen. Facial action coding system: investigator’s guide[M]. Palo Alto, CA, USA: Consulting Psychologists Press, 1978: 1-231.
[48] P. Ekman, W.V. Friesen. The Facial Action Coding System: A Technique for The Measurement of Facial Movement[M]. San Francisco, USA: Consulting Psychologists Press, 1978: 1-225.
[49] M. Suwa, N. Sugie, K. Fujimora. A preliminary note on pattern recognition of human emotional expression[C]. Proceedings of the Fourth International Joint Conference on Pattern Recognition: Kyoto, Japan, 1978: 408-410.
[50] I.A. Essa, A.P. Pentland. Coding, analysis, interpretation, and recognition of facial expressions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 757-763.
[51]金辉,高文.人脸面部混合表情识别系统[J].计算机学报, 2000, 23(6):602-608.
[52] W. Gao, B. Cao, S.G. Shan, et al. The CAS-PEAL Large-Scale Chinese Face Database and Evaluation Protocols[R]. No. JDL_TR_04_FR_001, Joint Research & Development Laboratory, CAS, 2004.
[53] Fasel, B. Fortenberry, J. Movellan. A generative framework for real time object detection and classification[J]. Computer Vision and Image Understanding In Special Issue on Eye Detection and Tracking, 2005, 98(1):182-210.
[54] J.R. Movellan, F. Tanaka, I.R. Fasel, et al. The rubi project: A progress report[C]. Proceedings of the 2007 ACM/IEEE Second International Conference on Human-Robot Interaction: Arlington, USA, 2007: 333-339.
[55] W. Zhao, R. Chellappa, J. Phillips, et al. Face Recognition: A Literature Survey[J]. ACM Computing Survey, 2003, 35(4): 399-458.
[56] P. Campadelli, R. Lanzarotti, G. Lipori. The fundamentals of verbal and non verbal communication and the biometrical issues: Eye localization: a survey[M]. Amsterdam, Netherlands: IOS Press: 2007:234-245.
[57] O. Jesorsky, K.J. Kirchberg, R.W. Frischholz. Robust face detection using the hausdorff distance[C]. Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication: London, UK, 2001: 90-95.
[58] P. Wang, M.B. Green, Q. Ji, et al. Automatic eye detection and its validation[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRW'05)-Workshops: San Diego, California, USA, 2005: 164-164.
[59] I.R. Fasel, M.S. Bartlett, J.R. Movellan. A comparison of Gabor filter methods for automatic detection of facial landmarks[C]. Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FG'02): Washington DC, 2002: 242-246.
[60] Huang, H. Wechsler. Eye detection using optimal wavelet packets and radial basis functions (RBFs)[J]. International Journal of Pattern Recognition and Artificial Intelligence, 1999, 13 (7): 1009-1025.
[61] R. Kothari, J.L. Mitchell. Detection of eye locations in unconstrained visual images[C]. Proceedings of International Conference on Image Processing: Lausanne, Switzerland, 1996: 519-522.
[62] T.K. Leung, M.C. Burl, P. Perona. Finding faces in cluttered scenes using random labeled graph matching[C]. Fifth International Conference on Computer Vision (ICCV'95): Cambridge, MA, 1995: 637-644.
[63] T.M. Mitchell著,曾华军,张银奎等译.机器学习[M].北京:机械工业出版社, 2003: 1-282.
[64] T.M. Mitchell. Does machine learning really work?[J]. AI Magazine, 1997, 18(3): 11-20.
[65]周志华.机器学习及其挑战[PPT].南京大学计算机软件新技术国家重点实验室网站, http://cs.nju.edu.cn/people/zhouzh/,南京,中国, 2003.12.
[66]周志华.机器学习的研究[PDF].国家自然科学基金委员会信息科学部AI战略研讨会报告, http://www.intsci.ac.cn/research/zhouzh05.pdf,北京,中国2005.6.
[67]李闯,丁晓青,吴佑寿.一种改进的AdaBoost算法——AD AdaBoost[J].计算机学报, 2007, 30(1): 103-109.
[68]王珏.机器学习研究回顾与趋势[PPT].中科院自动化研究所模式识别国家重点实验室网站http://www.intsci.ac.cn/research/wangj04.ppt, 2004.
[69] L. Wiskott, J.M. Fellous, N. Kruger, et al. Face recognition by elastic bunch graph matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7):775-779.
[70] D. Cristinacce, T. Cootes. Facial feature detection using AdaBoost with shape constraints[C]. 14th British Machine Vision Conference: Norwich, England, 2003: 231-240.
[71] A.L. Yuille, H.H. Bulthoff. Bayesian decision theory and psychophysics[R] MaxPlanck Institut fur Biologische Kybernetic, Arbeitsgruppe, Bulthoff, 1993.
[72] T. Hastie, R. Tibshirani, J.H. Friedman. The Elements of Statistical Learning[M]. Berlin, Heidelberg: Springer-Verlag: 2009: 1-745.
[73] A. Tefas, C. Kotropoulos, I. Pitas. Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(7):735-746.
[74] J. Qin, Z.S. He. A SVM face recognition method based on Gabor-featured key points[C]. Proceedings of International Conference on Machine Learning and Cybernetics: Guangzhou, China, 2005: 5144-5149.
[75] P. Moreno, A. Bernardino, J.S. Marques. et al. Gabor parameter selection for local feature detection[C]. Pattern Recognition Image Analysis: Berlin, Heidelberg, 2005: 11-19.
[76] D. Gonzalez-Jimenez, J.L. Alba-Castro. Shape-driven Gabor jets for face description and authentication[J]. IEEE Transactions on Information Forensics and Security, 2007, 2 (4): 769-780.
[77] J. Zou, Q. Ji, G. Nagy. A comparative study of local matching approach for face recognition[J]. IEEE Transactions on Image Processing, 2007, 16(10): 2617-2628.
[78] J. Ilonen, J.K. Kamarainen, P. Paalanen, et al. Image feature localization by multiplehypothesis testing of Gabor features[J]. IEEE Transactions on Image Processing, 2008, 17(3): 311-325.
[79] T.S. Lee. Image Representation Using 2D Gabor Wavelets[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(10): 959-971.
[80] C.J. Liu, H. Wechsler. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J]. IEEE Transactions on Image Processing, 2002, 11(4): 467-476.
[81] Y. Su, , S.G. Shan, X.L. Chen, et al. Hierarchical ensemble of Gabor Fisher classifier for face recognition[C]. Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG'06): University of Southampton,UK, 2006: 91-96.
[82] S.G. Shan, P. Yang, X. Chen, et al. AdaBoost Gabor fisher classifier for face recognition[C]. Proceeding of IEEE International Workshop on Analysis and Modelling of Faces and Gestures: Berlin, Heidelberg, 2005: 279-292.
[83] W.C. Zhang, S.G. Shan, W. Gao, et al. Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition[C]. Tenth IEEE International Conference on Computer Vision (ICCV'05): Beijing, China, 2005: 786-791.
[84] C.J. Liu. Gabor-based kernel PCA with fractional power polynomial models for face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(5): 572-581.
[85] X.D. Xie, K.M. Lam. Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image[J]. IEEE Transactions on Image Processing, 2006, 15(9): 2481-2492.
[86] L.L. Shen, L. Bai. Gabor wavelets and kernel direct discriminant analysis for face recognition[C]. 17th International Conference on Pattern Recognition (ICPR'04): Cambridge UK, 2004: 284-287.
[87] J.P. Jones, L.A. Palmer An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex[J]. Journal of Neural physiology, 1987, 58(6): 1233-1258.
[88]á. Serrano, I.M. de Diego, C. Conde, et al. Fusion of support vector classifiers for parallel Gabor methods applied to face verification[C]. Seventh International Workshop on Multiple Classifier Systems: Berlin, Heidelberg, 2007: 141-150.
[89] Y. Jin, Q.Q. Ruan. Gabor-based improved locality preserving projections for face recognition[C]. IEEE International Conference on Image Processing: San Antonio, TX,2007: 153-156.
[90] L. Wang, Y.P. Li, C.B. Wang, et al. Face recognition using Gabor face-based 2DPCA and (2D)2PCA classification with ensemble and multi-channel model[C]. Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications: Honolulu, HI, 2007: 343-349.
[91] L.H. He, D. Hu, C.J. Jiang. Gabor binary codes for face recognition[C]. Fifth Mexican International Conference on Artificial Intelligence (MICAI'06): Apizaco, Mexico, 2006: 53-60.
[92] B.H. Zhang, S.G. Shan, X.L. Chen, et al. Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition[J]. IEEE Transactions on Image Processing, 2007, 16(1): 57-68.
[93]刘正光,叶剑华.基于局部二值模式和级联AdaBoost的多模态人脸识别[J].计算机应用, 2008, 28(11): 2853-2855.
[94]叶剑华.三维及多模态人脸识别研究.[D]天津大学, 2008.
[95] G.C. Littlewort, M.S. Bartlett, I. Fasel, et al. Dynamics of facial expression extracted automatically from video[J]. Image and Vision Computing, 2006, 24(6): 615-625.
[96] M.S. Bartlett, G.C. Littlewort, M.G. Frank, et al. Automatic recognition of facial actions in spontaneous expressions[J]. Journal of Multimedia, 2006, 1(6): 22-35.
[97] F. Gosselin, P.G. Schyns. Bubbles: a technique to reveal the use of information in recognition tasks[J]. Vision Research, 2001, 41(17): 2261-2271.
[98] P. Ekman, W.V. Friesen. The Facial Action Coding System: A Technique For The Measurement of Facial Movement[M]. San Francisco, CA: Consulting Psychologists Press: 1978:1-200.
[99] P. Wang, Q. Ji. Learning Discriminant Features for Multi-View Face and Eye Detection[C]. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'05): San Diego, CA, 2005: 373-379.
[100] A. Pnevmatikakis, A. Stergiou, E. Rentzeperis, et al. Impact of face registration errors on recognition[C]. third IFIP Conference on Artificial Intelligence Applications and Innovations: Boston, USA, 2006: 187-194.
[101]á. Serrano, I.M. de Diego, C. Conde, et al. Recent advances in face biometrics with Gabor wavelets: A review[J]. Pattern Recognition Letters, 2010, 31(5): 372-381.
[102] A.L. Yullie, P.W. Hallinan, D.S. Cohen. Feature extraction from faces using deformable templates[J]. International Journal of Computer Vision, 1992, 8(2): 99-111.
[103] J. Movellan. Tutorial on gabor filters[R]. MPLab Tutorials, UCSD MPLab, 2005.
[104] P. Viola, M.J. Jones. Robust real-time face detection[J]. International Journal of Computer Vision, 2004, 57(2): 137-154.
[105] M. Everingham, A. Zisserman. Regression and classification approaches to eye localization[C]. Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG'06): University of Southampton, UK, 2006: 441-446.
[106] N. Dalal, B. Triggs. Histograms of oriented gradients for human detection[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05): San Diego, California, USA, 2005: 886-893.
[107] M. Kirby, L. Sirovich.Application of the Karhunen-Loe‘ve Procedure for the Characterization of Human Faces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(1):103-108.
[108] M. Turk, A.P. Pentland. Eigenfaces for Recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1):71-86.
[109] Y. Xirouhakis, G. Votsis, A. Delopoulos. Estimation of 3D Motion and Structure of Human Faces[C]. Proceeding of European Robotics, Intelligent Systems and Control Conference (EURISCON'99): Athens, Greece, 1999: 333-344.
[110]叶敬福,詹永照.基于Gabor小波变换的人脸表情特征提取[J].计算机工程, 2005, 31(15):172-174.
[111] D.G. Lowe. Object recognition from local scale-invariant features[C]. Proceedings of the Seventh IEEE International Conference on Computer Vision (ICCV'99): Corfu, Greece, 1999: 1150-1157.
[112] K. Levi, Y. Weiss. Learning object detection from a small number of examples: The importance of good features[C]. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04): Washington, D.C., USA: 2004: 53-60.
[113] T. Ojala, M. Pietikainen, D. Harwood. A comparative study of texture measures with classification based on feature distributions[J]. Pattern Recognition, 1996, 29(1): 51-59.
[114] H.-X. Jia, Y.-J. Zhang. Fast Human Detection by Boosting Histograms of Oriented Gradients[C]. Proceedings of the Fourth International Conference on Image and Graphics (ICIG 2007): Chengdu, Sichuan, China, 2007: 683-688,.
[115] Zhu, M.-C. Yeh, K.-T. Cheng. Fast Human Detection Using a Cascade of Histograms of Oriented Gradients[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition: Washington, D.C., USA, 2006: 1491-1498.
[116]王珏,周志华,周傲英.机器学习及其应用[M].北京:清华大学出版社, 2006:1-324.
[117] T. Sakaguchi, S. Morishima. Facial Expression Recognition from Image Sequences Using Hidden Markov Model[C]. Proceedings of Second Asian Conference on Computer Vision: Singapore, 1995: 713-717.
[118]尹星云,王洵,董兰芳,等.用隐马尔可夫模型设计人脸表情识别系统[J].电子科技大学学报, 2003, 32(6): 725-728.
[119] A. Samaria. Face Recognition Using Hidden Markov Model [D] University of Cambridge, 1994.
[120] H. Othman, T. Aboulnasr. A separable low complexity 2D HMM with application to face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(10):1229-1238.
[121] S. Russell, P. Vorvig著,姜哲,金弈江,张敏等译.人工智能:一种现代方法(第二版)[M].北京:人民邮电出版社, 2010: 1-758.
[122] V.N. Vapnik著.张学工译.统计学习理论的本质[M].北京:清华大学出版社, 2004:1-226.
[123] C.J.C. Burges. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
[124]张学工.关于统计学习理论与支持向量机[J].自动化学报, 2000, 26(1): 32-42.
[125]基于统计学习理论的支持向量机算法研究[PDF]. http://bidm.stat.fju.edu.tw:81/Data%20Mining2009課程/資料採礦相關資訊/SVM/SVM.pdf.
[126] J.W. Han, M. Kamber著,范明,孟晓峰等译.数据挖掘:概念与技术[M].原书第2版.北京:机械工业出版社, 2001: 1-374.
[127] M. Savvides, B.V.K.V. Kumar, P.K. Khosla. Eigenphases vs. Eigenfaces[C]. Proceedings of the 17th International Conference on Pattern Recognition: Pittsburgh, PA, USA, 2004: 810-813.
[128] P.J. Phillips, P. Grother, R.J. Micheals, et al. FRVT 2002: Evaluation Report[R]. http://www.frvt.org/DLs/FRVT_2002_Evaluation_Report.pdf, 2003.
[129] T. Sim, T. Kanade. Combining Models and Exemplars for Face Recognition: An Illuminating Example[C]. Proceeding of Workshop on Models versus Exemplars in Computer Vision(CVPR'01): Kauai, Hawaii, 2001: 283-292.
[130] C.J. Liu, H. Wechsler. Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition[J]. IEEE Transactions on Image Processing, 2002, 11(4): 467-476.
[131] R. Singh, M. Vatsa, A. Noore. Texture Feature Based Face Recognition for Single Training Images[J]. Electronics Letters, 2005, 41(11): 640-641.
[132] J. Daugman. How Iris Recognition Works[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2004, 14(1): 21-30.
[133] B. Moghaddam, T. Jebara, A.P. Pentland. Bayesian Face Recognition[J]. Pattern Recognition, 2000, 33(11): 1771-1782.
[134] H.F. Chen, P.N. Belhumeur, D.W. Jacobs. In Search of Illumination Invariants[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition: Hilton Head Island, SC , USA, 2000: 254-261.
[135] J.G. Daugman. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters[J]. Journal of the Optical Society of America A: Optics, Image Science, and Vision, 1985, 2(7): 1160-1169.
[136] A.S. Georghiades, P.N. Belhumeur, D.J. Kriegman. From Few to Many: Illumination Cone Models for Face Recognition under Differing Pose and Lighting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 643-660.
[137] L. Zhang, D. Samaras. Face Recognition under Variable Lighting using Harmonic Image Exemplars[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03): Madison, Wisconsin, 2003: 19-25.
[138] M. O’Sullivan, P. Ekman, W.V. Friesen. The effect of comparisons on detecting deceit[J]. Journal of Nonverbal Behavior, 1988, 12(2): 203-215.
[139] J.M. Leppa¨nen, J.K. Hietanen. Is there more in a happy face than just a big smile?[J]. Visual Cognition, 2007, 15(4): 468-490.
[140] C. Papageorgiou, T. Poggio. A Trainable System for Object Detection[J]. International Journal of Computer Vision, 2000, 38(1): 15-33.
[141] L. Valiant. A Theory of Learnability[J]. Communication of the Association for Computing Machinery, 1984, 27:1134-1142.
[142] M. Kearns, L.G. Valiant. Cryptographic Limitations on Learning Boolean Formulae and Finite Automata[J]. Journal of the Association for Computing Machinery, 1994, 41(1): 67-95.
[143] R.E. Schapire. A Brief Introduction of Boosting[C]. Proceedings of the 16thinternational joint conference on Artificial intelligence: Stockholm, Sweden, 1999: 1401-1406.
[144] R. Lienhart, J. Maydt. An extended set of Haar-like features for rapid object detection[C]. IEEE Conference on Image Processing: Rochester, New York, USA, 2002: 900-903.
[145] P. Yang, S.G. Shan, W. Gao, et al. Face recognition using AdaBoosted Gabor features[C]. Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FG'04): Seoul, Korea, 2004: 356-361.
[146] P. Viola, M. Jones. Rapid object detection using a boosted cascade of simple features[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01): Kauai, Hawaii, 2001: 511-518.
[147] H.S. Seung, D.D. Lee, The Manifold Ways of Perception[J]. Science, 2000, 290(5500): 2268-2269.
[148] J.B. Tenenbaum, V de Silva, J.C. Langford. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323.
[149] S.T. Roweis, L.K. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding[J]. Science, 2000, 290(5500): 2323-2324.
[150] X. He, P. Niyogi. Locality Preserving Projection[R]. Technical Report TR-2002-09, Department of Computer Science, the University of Chicago, 2002.
[151] X. He, P. Niyogi. Locality Preserving Projections[C]. Advances in Neural Information Processing Systems 16:00 Granada, Spain, 2003: 153-160.
[152] M.A. Turk, A.P. Pentland. Face Recognition Using Eigenfaces[C]. IEEE International Conference on Computer Vision and Pattern Recognition: Maui, HI, USA, 1991: 586-591.
[153] P.N. Belhumeour, J.P. Hespanha, D.J. Kriegman. Eigenfaces vs. fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7):711-720.
[154] M. Belkin, P. Niyogi. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation[J]. Neural Computation, 2003, 15(6):1373-1396.
[155] Y. Bengio, J.F.O. Paiement, P. Vincent, et al, Out-of-Sample Extensions for LLE, ISOMAP, MDS, Eigenmaps, and Spectral Clustering[C]. Advances in Neural Information Processing Systems: Granada, Spain, 2004: 177-184.
[156] J. Ham, D.D. Lee, S. Mika, et al, A Kernel View of the Dimensionality Reduction of Manifolds[C]. Proceedings of the twenty-first international conference on Machinelearning: Banff, Alberta, Canada, 2004: 47-54.
[157] S. Yan, D. Xu, B. Zhang, et al. Graph Embedding: A General Framework for Dimensionality Reduction[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'05): San Diego, CA, USA, 2005: 830-837.
[158] J. Gui, W. Jia, L. Zhu, et al. Locality preserving discriminant projections for face and palmprint recognition[J]. Neurocomputing, 2010, 73(13-15), 2696-2707.
[159] C. Deng, X. He, Y. Hu, et al. Learning a Spatially Smooth Subspace for Face Recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition: Minneapolis, MN, USA, 2007: 650-656.
[160] S.C. Yan, H. Wang, Y. Fu, et al. Synchronized sub-manifold embedding for person-independent pose estimation and beyond[J]. IEEE transactions on Image Processing, 2009, 18(1): 202-210.
[161] X.D. Xie, K.M. Lam. Face recognition using elastic local reconstruction based on a single face image[J]. Pattern Recognition, 2008, 41(1): 406-417.
[162] T.P. Zhang, B. Fang, Y.Y. Tang, et al. Topology preserving non-negative matrix factorization for face recognition[J]. IEEE transactions on image processing, 2008, 17(4): 574-584.
[163] J. Chen, X. Chen, J. Yang, et al. Optimization of a training set for more robust face detection[J]. Pattern Recognition, 2009, 42(11): 2828-2840.
[164] O. Arandjelovic, R. Cipolla. A pose-wise linear illumination manifold model for face recognition using video[J]. Computer Vision and Image Understanding, 2009, 113(1): 113-125.
[165] F. Wang, C. Zhang, J.D. Wang, et al. Semi-Supervised Classification Using Linear Neighborhood Propagation[C]. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'06): New York, NY, USA, 2006: 160-167.
[166] S.C. Yan, D. Xu, B.Y. Zhang, et al. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51.
[167] D. de Ridder, R.P.W. Duin. Locally linear embedding for classification[R]. Pattern Recognition Group, Department of Imaging Science&Technology, Delft University of Technology: Delft, Netherlands: 2002.
[168] O. Kouropteva, O. Okun, M. Pietik¨ainen. Selection of the optimal parameter value for the locally linear embedding algorithm[C]. 1st International Conference on Fuzzy Systems and Knowledge Discovery: Singapore, 2002: 359-363.
[169] D. de Ridder, O. Kouropteva, O. Okun, et al. Supervised locally linear embedding[C]. Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing: Istanbul, Turkey, 2003: 333-341.
[170] H. Choi, S. Choi. Robust kernel Isomap[J]. Pattern Recognition, 2007, 40(3): 853-862.
[171] J. Chen, R.P. Wang, S.G. Shan, et al. Isomap Based on the Image Euclidean Distance[C]. 18th International Conference on Pattern Recognition (ICPR'06): Hong Kong, China, 2006: 1110-1113.
[172] J.B. Li, J.S. Pan, S.C. Chu, et al. Kernel class-wise locality preserving projection[J]. Information Sciences, 2008, 178(7): 1825-1835.
[173]赵继东,鲁珂,吴跃.保局投影算法的优化研究[J].电子科技大学学报, 2008, 37(5): 750-752.
[174] J.D. Zhao, K. Lu, Y. Wu. Research on the Optimization of Locality Preserving Projections[J]. Journal of University of Electronic Science and Technology of China, 2008, 37(5): 37-39.
[175] J.G. Daugman. Two-dimensional spectral analysis of cortical receptive field profiles[J]. Vision Research, 1980, 20(10): 847–856.
[176] D. Gabor. Theory of communication[J]. Journal of institute for electrical engineering, 1946, 93: 429-457.
[177] J.J. Henriksen. 3D surface tracking and approximation using Gabor filters [D]. Odense, Denmark: South Denmark University, 2007.
[178] H.J. Nussbaumer. Fast Fourier transform and convolution algorithms[M]. New York, USA: Springer-Verlag, 1982: 1-287.
[179] J.G. Daugman. Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression[J]. IEEE Transaction on Acoustics, Speech, and Signal Processing, 1988, 36(7): 1169–1179.
[180]朱健翔,苏光大,李迎春.结合Gabor特征与AdaBoost的人脸表情识别[J].光电子·激光, 2006, 17(8): 993-998.
[181] Q. Zhu, M.-C. Yeh, K.-T. Cheng, et al. Fast Human Detection Using a Cascade of Histograms of Oriented Gradients[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition: University of California at Santa Barbara, CA, USA, 2007: 1491-1498.
[182] A. Bosch, A. Zisserman, X. Munoz. Representing shape with a spatial pyramid kernel[C]. Proceedings of the 6th ACM international conference on Image and videoretrieval: Amsterdam, Netherlands: 2007: 401-408.
[183] Y. Bai, L.H. Guo, L.W. Jin. A novel feature extraction method using Pyramid Histogram of Orientation Gradients for smile recognition[C]. 16th IEEE International Conference on Image Processing: Cairo, Egypt, 2009: 3305-3308.
[184] P. Viola, M. Jones. Fast and robust classification using asymmetric AdaBoost and a detector cascade[C]. Advances in Neural Information Processing Systems: Cambridge, British Columbia, Canada, 2002: 1311-1318.
[185] T. Serre, L. Wolf, T. Poggio. Object Recognition with Features Inspired by Visual Cortex[C]. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition: Los Alamitos, CA, USA, 2005: 994-1000.
[186] X.F. He, P. Niyogi. Locality preserving projections[C]. Advances in Neural Information Processing Systems: Cambridge, British Columbia, Canada, 2004: 153-160.
[187] W. Jacob, L. Gwen, F. Ian, et al. Towards Practical Smile Detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(11): 2106-2111.
[188] T. Serre, M. Kouh, C. Cadieu, et al. A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex[R]. CBCL MIT paper, 2005.
[189] D. Hubel, T. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J].The Journal of Physiology, 1962, 160(1): 106-154.
[190] J. Sporring, L. Florack, M. Nielsen, et al. Gaussian scale-space theory[M]. USA: Kluwer Academic Publishers Norwell, 1997:1-286.
[191]应自炉,李景文,张有为.基于融合的多类支持向量机[J].计算机工程, 2009, 35(19):187-188.
[192] B. Fasel, J. Luettin. Automatic facial expression analysis: a survey[J]. Pattern Recognition, 2003, 36(1):259-275.
[193] Z.H. Zeng, M. Pantic, G.I. Roisman, et al. A survey of affect recognition methods: Audio, visual, and spontaneous expressions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(1): 39-58.
[194] A. Samala, P.A. Iyengara. Automatic recognition and analysis of human faces and facial expressions: a survey[J]. Pattern Recognition, 1992, 25(1):65-77.
[195] A. Ghaoui. Encyclopedia of human computer interaction[M]. Hershey, PA, USA: Idea Group Inc (IGI), 2006:1-826.
[196] N. Sebe, M.S. Lew, Y. Sun, et al. Authentic facial expression analysis[J]. Image and Vision Computing, 2007, 25(12):1856-1863.
[197] P.S. Aleksic, A.K. Katsaggelos. Automatic facial expression recognition using facial animation parameters and multistream HMMs[J].IEEE Transactions on Information Forensics and Security, 2006, 1(1):3-11.
[198] M.S. Bartlett, G. Littlewort, I. Fasel, et al. Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction[C]. 2003 Conference on Computer Vision and Pattern Recognition Workshop: Madison, Wisconsin, 2003: 53-60.
[199] T. Ojala, M. Pietik?inen, D. Harwood. A Comparative Study of Texture Measures with Classification based on Feature Distribution[J]. Pattern Recognition, 1996, 29(1): 51-59.
[200] T. Ojala, M. Pietik?inen, T. M?enp??. Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns[J].IEEE Transaction on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
[201] C.C. Chiu, Y.L. Chang, Y.J. Lai. The analysis and recognition of human vocal emotions[C]. Proceeding of International Computer Symposium: Tsihchu, Taiwan, R.O.C., 1994: 83-88.
[202] A.J.T. Cacioppo, L.G. Tassinary. Inferring psychological significance from physiological signals[J]. American Psychologist, 1990, 45(1):16-28.
[203] G. Heusch, Y. Rodriguez, S. Marcel. Local Binary Patterns as an Image Preprocessing for Face Authentication[C]. 7th International Conference on Automatic Face and Gesture Recognition (FG'06): Southampton, UK, 2006:9-14.
[2] B. Fasel, J. Luettin. Automatic facial expression analysis: Survey[J]. Pattern Recognition, 2003, 36(1): 259-275.
[3] M. Pantic and L. Rothkrantz. Automatic analysis of facial expressions: the state of the art[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12): 1424-1445.
[4] P. Ekman, W.V. Friesen. Unmasking the face: A guide to recognizing emotions from facial clues[M]. Englewood Cliffs, N.J.: Prentice-Hall, 1975:1-167.
[5] P. Ekman. Emotion In the Human Face[M]. 2nd edition. New York: Pergamon Press, 1982:1-141.
[6] P. Ekman. Facial expression of emotion[J]. American Psychologist, 1993, 48(4): 384-392.
[7] P. Ekman, W.V. Friesen. Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage[M]. 3rd edition. New York, USA: W.W. Norton & Company, 2001:1-167.
[8] Z. Zeng, M. Pantic, G. Roisman, et al. A survey of affect recognition methods: Audio, visual, and spontaneous expressions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(1):39-58.
[9] L. Parr, B. M. Waller. Understanding chimpancee facial expression: insights into the evolution of communication[J]. Social Cognitive and Affective Neuroscience, 2006, 1(3): 221-228.
[10] M. Bartlett, G. Littlewort, M. Frank, et al. Fully automatic facial action recognition in spontaneous behavior[C]. Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG'06): University of Southampton,UK, 2006: 223-228.
[11] Y.B. Wang, H.Z. Ai, B. Wu, et al. Real time facial expression recognition with AdaBoost[C]. 17th International Conference on Pattern Recognition (ICPR'04): Cambridge, England, UK, 2004: 926-929.
[12] M. Pantic, J. Rothkrantz. Facial action recognition for facial expression analysis from static face images[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(3):1449-1461.
[13] P. Ekman, W.V. Friesen. Pictures of facial affect[M]. Palo Alto, CA, USA: Consulting Psychologists Press, 1975: 1-110.
[14] J.C. Hager, P. Ekman. Long distance transmission of facial affect signals[J]. Ethnology and Sociobiology, 1979, 1(1): 77-82.
[15] P. Ekman. W.V. Friesen. Felt, false, and miserable smiles[J]. Journal of Nonverbal Behavior, 1982, 6(4): 238-252.
[16] Y. Tian, T. Kanade, J. Cohn. Recognizing action units for facial expression analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2):97-115.
[17] A. Kapoor, Y. Qi, R.W. Picard. Fully automatic upper facial action recognition[C]. IEEE International Workshop on Analysis and Modeling of Faces and Gestures: Media Lab., MIT, Cambridge, MA, USA, 2003: 195-202.
[18] Kotsia, I. Pitas. Facial expression recognition in image sequences using geometric deformation features and support vector machines[J]. IEEE Transactions on Image Processing, 2007, 16(1):172-187.
[19] Z. Wen, T.H. Huang. Capturing subtle facial motions in 3D face tracking[C]. Ninth IEEE International Conference on Computer Vision (ICCV'03): Nice, France, 2003: 1343-1350.
[20] C. Cortes, M. Mohri. Confidence intervals for the area under the roc curve[C]. Advances in Neural Information Processing Systems: Vancouver, Canada, 2004: 17-28.
[21] J.R. Movellan. Tutorial on gabor filters[R]. MPLab Tutorials, UCSD MPLab, http://mplab.ucsd.edu/tutorials/gabor.pdf, 2005.
[22] M. Lades, J.C. Vorbruggen, J. Buhmann, et al. Distortion invariant object recognition in the dynamic link architecture[J]. IEEE Transactions on Computers, 1993, 42(3): 300-311.
[23] G. Donato, M.S. Bartlett, J.C. Hager, et al. Classifying facial actions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(10): 974-989.
[24] N. Sebe, Y. Sun, E. Bakker, et al. Towards authentic emotion recognition[C]. IEEE International Conference on Systems, Man and Cybernetics: The Hague, Netherlands: 2004: 623-628.
[25] J.F. Cohn, K.L. Schmidt. The timing of facial motion in posed and spontaneous smiles[J]. International Journal of Wavelets, Multiresolution and Information Processing (IJWMIP'04), 2004, 2(2): 121-132.
[26] Cohen, N. Sebe, A. Garg, et al. Facial expression recognition from video sequences: Temporal and static modeling[J]. Computer Vision and Image Understanding, Special Issue on Face Recognition, 2003, 91(1-2): 160-187.
[27] Y. Zhang, Q. Ji. Active and dynamic information fusion for facial expressionunderstanding from image sequences[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5): 699-714.
[28] P. Yang, Q. Liu, D. Metaxas. Boosting coded dynamic features for facial action units and facial expression recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition: Minneapolis, MN, USA: 2007: 688-693.
[29] M. J. McDonnell. Box-filtering techniques[J]. Computer Graphics and Image Processing, 1981, 17(1):65-70.
[30] S. Castan, J. Shen. Fast approximate realization of linear filters by translating cascading sum-box technique[C]. Proceedings of Computer Vision and Pattern Recognition, San Francisco, CA, USA: 1985, 678-680.
[31] Paul Viola, Michael Jones. Robust real-time object detection[J]. International Journal of Computer Vision, 2002,57(2):137-154.
[32] J. Whitehill, C.W. Omlin. Haar features for FACS AU recognition[C]. Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG'06), Los Alamitos: 2006, 97-101.
[33] Y. Tong, W.H. Liao, Q. Ji. Facial action unit recognition by exploiting their dynamic and semantic relationships[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(10):1683-1699.
[34] G.Y. Zhao, M. Pietika¨inen. Dynamic texture recognition using local binary patterns with an application to facial expressions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6):915-928.
[35] Z.W. Zhu, Q. Ji. Robust real-time face pose and facial expression recovery[J]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06): New York, NY: 2006, 1:681-688.
[36] T. Kanade, J. Cohn, Y.-L. Tian. Comprehensive database for facial expression analysis[C]. Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG'00): Grenoble, France, 2000: 46-53.
[37] T. Sim, S. Baker, M. Bsat. The CMU pose, illumination, and expression database[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12): 1615-1618.
[38] M. Pantic, M. Valstar, R. Rademaker, et al. Web-based database for facial expression analysis[C]. IEEE International Conference on Multimedia and Expo: Amsterdam, Netherlands: 2005: 5-5.
[39] A.J. O'Toole, J. Harms, S.L.Snow, et al. A video database of moving faces andpeople[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5): 812-816.
[40] P.J. Phillips, H. Moon, S.A. Rizvi, et al. The FERET evaluation methodology for face-recognition algorithms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(10):1090-1104.
[41] T. Kanade, J.F. Cohn, Y.L. Tian. Comprehensive database for facial expression analysis[C]. Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition: Grenoble, France, 2000: 46-53.
[42] Y. Freund, R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting[C]. Second Annual European Conference on Computational Learning Theory: Barcelona, Spain, 1995: 23-37.
[43] M. Pantic, L.J.M. Rothkranz. Expert system for automatic analysis of facial expression[J]. Image and Vision Computing, 2000, 18(11): 881-905.
[44] A. Mehrabian, N. Epstein. A measure of emotional empathy[J]. Journal of Personality, 1972, 40:525–543.
[45] C. Darwin. The expression of the emotions in man and animals[M]. London, UK: John Murray Press, 1872:1-367.
[46] P. Ekman, W.V. Friesen. Constants across cultures in the Face and emotion[J] .Journal of Personality and Social Psychology, 1971, 17(2) :124-129.
[47] P. Ekman, W.V. Friesen. Facial action coding system: investigator’s guide[M]. Palo Alto, CA, USA: Consulting Psychologists Press, 1978: 1-231.
[48] P. Ekman, W.V. Friesen. The Facial Action Coding System: A Technique for The Measurement of Facial Movement[M]. San Francisco, USA: Consulting Psychologists Press, 1978: 1-225.
[49] M. Suwa, N. Sugie, K. Fujimora. A preliminary note on pattern recognition of human emotional expression[C]. Proceedings of the Fourth International Joint Conference on Pattern Recognition: Kyoto, Japan, 1978: 408-410.
[50] I.A. Essa, A.P. Pentland. Coding, analysis, interpretation, and recognition of facial expressions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 757-763.
[51]金辉,高文.人脸面部混合表情识别系统[J].计算机学报, 2000, 23(6):602-608.
[52] W. Gao, B. Cao, S.G. Shan, et al. The CAS-PEAL Large-Scale Chinese Face Database and Evaluation Protocols[R]. No. JDL_TR_04_FR_001, Joint Research & Development Laboratory, CAS, 2004.
[53] Fasel, B. Fortenberry, J. Movellan. A generative framework for real time object detection and classification[J]. Computer Vision and Image Understanding In Special Issue on Eye Detection and Tracking, 2005, 98(1):182-210.
[54] J.R. Movellan, F. Tanaka, I.R. Fasel, et al. The rubi project: A progress report[C]. Proceedings of the 2007 ACM/IEEE Second International Conference on Human-Robot Interaction: Arlington, USA, 2007: 333-339.
[55] W. Zhao, R. Chellappa, J. Phillips, et al. Face Recognition: A Literature Survey[J]. ACM Computing Survey, 2003, 35(4): 399-458.
[56] P. Campadelli, R. Lanzarotti, G. Lipori. The fundamentals of verbal and non verbal communication and the biometrical issues: Eye localization: a survey[M]. Amsterdam, Netherlands: IOS Press: 2007:234-245.
[57] O. Jesorsky, K.J. Kirchberg, R.W. Frischholz. Robust face detection using the hausdorff distance[C]. Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication: London, UK, 2001: 90-95.
[58] P. Wang, M.B. Green, Q. Ji, et al. Automatic eye detection and its validation[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRW'05)-Workshops: San Diego, California, USA, 2005: 164-164.
[59] I.R. Fasel, M.S. Bartlett, J.R. Movellan. A comparison of Gabor filter methods for automatic detection of facial landmarks[C]. Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FG'02): Washington DC, 2002: 242-246.
[60] Huang, H. Wechsler. Eye detection using optimal wavelet packets and radial basis functions (RBFs)[J]. International Journal of Pattern Recognition and Artificial Intelligence, 1999, 13 (7): 1009-1025.
[61] R. Kothari, J.L. Mitchell. Detection of eye locations in unconstrained visual images[C]. Proceedings of International Conference on Image Processing: Lausanne, Switzerland, 1996: 519-522.
[62] T.K. Leung, M.C. Burl, P. Perona. Finding faces in cluttered scenes using random labeled graph matching[C]. Fifth International Conference on Computer Vision (ICCV'95): Cambridge, MA, 1995: 637-644.
[63] T.M. Mitchell著,曾华军,张银奎等译.机器学习[M].北京:机械工业出版社, 2003: 1-282.
[64] T.M. Mitchell. Does machine learning really work?[J]. AI Magazine, 1997, 18(3): 11-20.
[65]周志华.机器学习及其挑战[PPT].南京大学计算机软件新技术国家重点实验室网站, http://cs.nju.edu.cn/people/zhouzh/,南京,中国, 2003.12.
[66]周志华.机器学习的研究[PDF].国家自然科学基金委员会信息科学部AI战略研讨会报告, http://www.intsci.ac.cn/research/zhouzh05.pdf,北京,中国2005.6.
[67]李闯,丁晓青,吴佑寿.一种改进的AdaBoost算法——AD AdaBoost[J].计算机学报, 2007, 30(1): 103-109.
[68]王珏.机器学习研究回顾与趋势[PPT].中科院自动化研究所模式识别国家重点实验室网站http://www.intsci.ac.cn/research/wangj04.ppt, 2004.
[69] L. Wiskott, J.M. Fellous, N. Kruger, et al. Face recognition by elastic bunch graph matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7):775-779.
[70] D. Cristinacce, T. Cootes. Facial feature detection using AdaBoost with shape constraints[C]. 14th British Machine Vision Conference: Norwich, England, 2003: 231-240.
[71] A.L. Yuille, H.H. Bulthoff. Bayesian decision theory and psychophysics[R] MaxPlanck Institut fur Biologische Kybernetic, Arbeitsgruppe, Bulthoff, 1993.
[72] T. Hastie, R. Tibshirani, J.H. Friedman. The Elements of Statistical Learning[M]. Berlin, Heidelberg: Springer-Verlag: 2009: 1-745.
[73] A. Tefas, C. Kotropoulos, I. Pitas. Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(7):735-746.
[74] J. Qin, Z.S. He. A SVM face recognition method based on Gabor-featured key points[C]. Proceedings of International Conference on Machine Learning and Cybernetics: Guangzhou, China, 2005: 5144-5149.
[75] P. Moreno, A. Bernardino, J.S. Marques. et al. Gabor parameter selection for local feature detection[C]. Pattern Recognition Image Analysis: Berlin, Heidelberg, 2005: 11-19.
[76] D. Gonzalez-Jimenez, J.L. Alba-Castro. Shape-driven Gabor jets for face description and authentication[J]. IEEE Transactions on Information Forensics and Security, 2007, 2 (4): 769-780.
[77] J. Zou, Q. Ji, G. Nagy. A comparative study of local matching approach for face recognition[J]. IEEE Transactions on Image Processing, 2007, 16(10): 2617-2628.
[78] J. Ilonen, J.K. Kamarainen, P. Paalanen, et al. Image feature localization by multiplehypothesis testing of Gabor features[J]. IEEE Transactions on Image Processing, 2008, 17(3): 311-325.
[79] T.S. Lee. Image Representation Using 2D Gabor Wavelets[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(10): 959-971.
[80] C.J. Liu, H. Wechsler. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J]. IEEE Transactions on Image Processing, 2002, 11(4): 467-476.
[81] Y. Su, , S.G. Shan, X.L. Chen, et al. Hierarchical ensemble of Gabor Fisher classifier for face recognition[C]. Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG'06): University of Southampton,UK, 2006: 91-96.
[82] S.G. Shan, P. Yang, X. Chen, et al. AdaBoost Gabor fisher classifier for face recognition[C]. Proceeding of IEEE International Workshop on Analysis and Modelling of Faces and Gestures: Berlin, Heidelberg, 2005: 279-292.
[83] W.C. Zhang, S.G. Shan, W. Gao, et al. Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition[C]. Tenth IEEE International Conference on Computer Vision (ICCV'05): Beijing, China, 2005: 786-791.
[84] C.J. Liu. Gabor-based kernel PCA with fractional power polynomial models for face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(5): 572-581.
[85] X.D. Xie, K.M. Lam. Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image[J]. IEEE Transactions on Image Processing, 2006, 15(9): 2481-2492.
[86] L.L. Shen, L. Bai. Gabor wavelets and kernel direct discriminant analysis for face recognition[C]. 17th International Conference on Pattern Recognition (ICPR'04): Cambridge UK, 2004: 284-287.
[87] J.P. Jones, L.A. Palmer An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex[J]. Journal of Neural physiology, 1987, 58(6): 1233-1258.
[88]á. Serrano, I.M. de Diego, C. Conde, et al. Fusion of support vector classifiers for parallel Gabor methods applied to face verification[C]. Seventh International Workshop on Multiple Classifier Systems: Berlin, Heidelberg, 2007: 141-150.
[89] Y. Jin, Q.Q. Ruan. Gabor-based improved locality preserving projections for face recognition[C]. IEEE International Conference on Image Processing: San Antonio, TX,2007: 153-156.
[90] L. Wang, Y.P. Li, C.B. Wang, et al. Face recognition using Gabor face-based 2DPCA and (2D)2PCA classification with ensemble and multi-channel model[C]. Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications: Honolulu, HI, 2007: 343-349.
[91] L.H. He, D. Hu, C.J. Jiang. Gabor binary codes for face recognition[C]. Fifth Mexican International Conference on Artificial Intelligence (MICAI'06): Apizaco, Mexico, 2006: 53-60.
[92] B.H. Zhang, S.G. Shan, X.L. Chen, et al. Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition[J]. IEEE Transactions on Image Processing, 2007, 16(1): 57-68.
[93]刘正光,叶剑华.基于局部二值模式和级联AdaBoost的多模态人脸识别[J].计算机应用, 2008, 28(11): 2853-2855.
[94]叶剑华.三维及多模态人脸识别研究.[D]天津大学, 2008.
[95] G.C. Littlewort, M.S. Bartlett, I. Fasel, et al. Dynamics of facial expression extracted automatically from video[J]. Image and Vision Computing, 2006, 24(6): 615-625.
[96] M.S. Bartlett, G.C. Littlewort, M.G. Frank, et al. Automatic recognition of facial actions in spontaneous expressions[J]. Journal of Multimedia, 2006, 1(6): 22-35.
[97] F. Gosselin, P.G. Schyns. Bubbles: a technique to reveal the use of information in recognition tasks[J]. Vision Research, 2001, 41(17): 2261-2271.
[98] P. Ekman, W.V. Friesen. The Facial Action Coding System: A Technique For The Measurement of Facial Movement[M]. San Francisco, CA: Consulting Psychologists Press: 1978:1-200.
[99] P. Wang, Q. Ji. Learning Discriminant Features for Multi-View Face and Eye Detection[C]. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'05): San Diego, CA, 2005: 373-379.
[100] A. Pnevmatikakis, A. Stergiou, E. Rentzeperis, et al. Impact of face registration errors on recognition[C]. third IFIP Conference on Artificial Intelligence Applications and Innovations: Boston, USA, 2006: 187-194.
[101]á. Serrano, I.M. de Diego, C. Conde, et al. Recent advances in face biometrics with Gabor wavelets: A review[J]. Pattern Recognition Letters, 2010, 31(5): 372-381.
[102] A.L. Yullie, P.W. Hallinan, D.S. Cohen. Feature extraction from faces using deformable templates[J]. International Journal of Computer Vision, 1992, 8(2): 99-111.
[103] J. Movellan. Tutorial on gabor filters[R]. MPLab Tutorials, UCSD MPLab, 2005.
[104] P. Viola, M.J. Jones. Robust real-time face detection[J]. International Journal of Computer Vision, 2004, 57(2): 137-154.
[105] M. Everingham, A. Zisserman. Regression and classification approaches to eye localization[C]. Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG'06): University of Southampton, UK, 2006: 441-446.
[106] N. Dalal, B. Triggs. Histograms of oriented gradients for human detection[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05): San Diego, California, USA, 2005: 886-893.
[107] M. Kirby, L. Sirovich.Application of the Karhunen-Loe‘ve Procedure for the Characterization of Human Faces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(1):103-108.
[108] M. Turk, A.P. Pentland. Eigenfaces for Recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1):71-86.
[109] Y. Xirouhakis, G. Votsis, A. Delopoulos. Estimation of 3D Motion and Structure of Human Faces[C]. Proceeding of European Robotics, Intelligent Systems and Control Conference (EURISCON'99): Athens, Greece, 1999: 333-344.
[110]叶敬福,詹永照.基于Gabor小波变换的人脸表情特征提取[J].计算机工程, 2005, 31(15):172-174.
[111] D.G. Lowe. Object recognition from local scale-invariant features[C]. Proceedings of the Seventh IEEE International Conference on Computer Vision (ICCV'99): Corfu, Greece, 1999: 1150-1157.
[112] K. Levi, Y. Weiss. Learning object detection from a small number of examples: The importance of good features[C]. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04): Washington, D.C., USA: 2004: 53-60.
[113] T. Ojala, M. Pietikainen, D. Harwood. A comparative study of texture measures with classification based on feature distributions[J]. Pattern Recognition, 1996, 29(1): 51-59.
[114] H.-X. Jia, Y.-J. Zhang. Fast Human Detection by Boosting Histograms of Oriented Gradients[C]. Proceedings of the Fourth International Conference on Image and Graphics (ICIG 2007): Chengdu, Sichuan, China, 2007: 683-688,.
[115] Zhu, M.-C. Yeh, K.-T. Cheng. Fast Human Detection Using a Cascade of Histograms of Oriented Gradients[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition: Washington, D.C., USA, 2006: 1491-1498.
[116]王珏,周志华,周傲英.机器学习及其应用[M].北京:清华大学出版社, 2006:1-324.
[117] T. Sakaguchi, S. Morishima. Facial Expression Recognition from Image Sequences Using Hidden Markov Model[C]. Proceedings of Second Asian Conference on Computer Vision: Singapore, 1995: 713-717.
[118]尹星云,王洵,董兰芳,等.用隐马尔可夫模型设计人脸表情识别系统[J].电子科技大学学报, 2003, 32(6): 725-728.
[119] A. Samaria. Face Recognition Using Hidden Markov Model [D] University of Cambridge, 1994.
[120] H. Othman, T. Aboulnasr. A separable low complexity 2D HMM with application to face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(10):1229-1238.
[121] S. Russell, P. Vorvig著,姜哲,金弈江,张敏等译.人工智能:一种现代方法(第二版)[M].北京:人民邮电出版社, 2010: 1-758.
[122] V.N. Vapnik著.张学工译.统计学习理论的本质[M].北京:清华大学出版社, 2004:1-226.
[123] C.J.C. Burges. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
[124]张学工.关于统计学习理论与支持向量机[J].自动化学报, 2000, 26(1): 32-42.
[125]基于统计学习理论的支持向量机算法研究[PDF]. http://bidm.stat.fju.edu.tw:81/Data%20Mining2009課程/資料採礦相關資訊/SVM/SVM.pdf.
[126] J.W. Han, M. Kamber著,范明,孟晓峰等译.数据挖掘:概念与技术[M].原书第2版.北京:机械工业出版社, 2001: 1-374.
[127] M. Savvides, B.V.K.V. Kumar, P.K. Khosla. Eigenphases vs. Eigenfaces[C]. Proceedings of the 17th International Conference on Pattern Recognition: Pittsburgh, PA, USA, 2004: 810-813.
[128] P.J. Phillips, P. Grother, R.J. Micheals, et al. FRVT 2002: Evaluation Report[R]. http://www.frvt.org/DLs/FRVT_2002_Evaluation_Report.pdf, 2003.
[129] T. Sim, T. Kanade. Combining Models and Exemplars for Face Recognition: An Illuminating Example[C]. Proceeding of Workshop on Models versus Exemplars in Computer Vision(CVPR'01): Kauai, Hawaii, 2001: 283-292.
[130] C.J. Liu, H. Wechsler. Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition[J]. IEEE Transactions on Image Processing, 2002, 11(4): 467-476.
[131] R. Singh, M. Vatsa, A. Noore. Texture Feature Based Face Recognition for Single Training Images[J]. Electronics Letters, 2005, 41(11): 640-641.
[132] J. Daugman. How Iris Recognition Works[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2004, 14(1): 21-30.
[133] B. Moghaddam, T. Jebara, A.P. Pentland. Bayesian Face Recognition[J]. Pattern Recognition, 2000, 33(11): 1771-1782.
[134] H.F. Chen, P.N. Belhumeur, D.W. Jacobs. In Search of Illumination Invariants[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition: Hilton Head Island, SC , USA, 2000: 254-261.
[135] J.G. Daugman. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters[J]. Journal of the Optical Society of America A: Optics, Image Science, and Vision, 1985, 2(7): 1160-1169.
[136] A.S. Georghiades, P.N. Belhumeur, D.J. Kriegman. From Few to Many: Illumination Cone Models for Face Recognition under Differing Pose and Lighting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 643-660.
[137] L. Zhang, D. Samaras. Face Recognition under Variable Lighting using Harmonic Image Exemplars[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03): Madison, Wisconsin, 2003: 19-25.
[138] M. O’Sullivan, P. Ekman, W.V. Friesen. The effect of comparisons on detecting deceit[J]. Journal of Nonverbal Behavior, 1988, 12(2): 203-215.
[139] J.M. Leppa¨nen, J.K. Hietanen. Is there more in a happy face than just a big smile?[J]. Visual Cognition, 2007, 15(4): 468-490.
[140] C. Papageorgiou, T. Poggio. A Trainable System for Object Detection[J]. International Journal of Computer Vision, 2000, 38(1): 15-33.
[141] L. Valiant. A Theory of Learnability[J]. Communication of the Association for Computing Machinery, 1984, 27:1134-1142.
[142] M. Kearns, L.G. Valiant. Cryptographic Limitations on Learning Boolean Formulae and Finite Automata[J]. Journal of the Association for Computing Machinery, 1994, 41(1): 67-95.
[143] R.E. Schapire. A Brief Introduction of Boosting[C]. Proceedings of the 16thinternational joint conference on Artificial intelligence: Stockholm, Sweden, 1999: 1401-1406.
[144] R. Lienhart, J. Maydt. An extended set of Haar-like features for rapid object detection[C]. IEEE Conference on Image Processing: Rochester, New York, USA, 2002: 900-903.
[145] P. Yang, S.G. Shan, W. Gao, et al. Face recognition using AdaBoosted Gabor features[C]. Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FG'04): Seoul, Korea, 2004: 356-361.
[146] P. Viola, M. Jones. Rapid object detection using a boosted cascade of simple features[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01): Kauai, Hawaii, 2001: 511-518.
[147] H.S. Seung, D.D. Lee, The Manifold Ways of Perception[J]. Science, 2000, 290(5500): 2268-2269.
[148] J.B. Tenenbaum, V de Silva, J.C. Langford. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323.
[149] S.T. Roweis, L.K. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding[J]. Science, 2000, 290(5500): 2323-2324.
[150] X. He, P. Niyogi. Locality Preserving Projection[R]. Technical Report TR-2002-09, Department of Computer Science, the University of Chicago, 2002.
[151] X. He, P. Niyogi. Locality Preserving Projections[C]. Advances in Neural Information Processing Systems 16:00 Granada, Spain, 2003: 153-160.
[152] M.A. Turk, A.P. Pentland. Face Recognition Using Eigenfaces[C]. IEEE International Conference on Computer Vision and Pattern Recognition: Maui, HI, USA, 1991: 586-591.
[153] P.N. Belhumeour, J.P. Hespanha, D.J. Kriegman. Eigenfaces vs. fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7):711-720.
[154] M. Belkin, P. Niyogi. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation[J]. Neural Computation, 2003, 15(6):1373-1396.
[155] Y. Bengio, J.F.O. Paiement, P. Vincent, et al, Out-of-Sample Extensions for LLE, ISOMAP, MDS, Eigenmaps, and Spectral Clustering[C]. Advances in Neural Information Processing Systems: Granada, Spain, 2004: 177-184.
[156] J. Ham, D.D. Lee, S. Mika, et al, A Kernel View of the Dimensionality Reduction of Manifolds[C]. Proceedings of the twenty-first international conference on Machinelearning: Banff, Alberta, Canada, 2004: 47-54.
[157] S. Yan, D. Xu, B. Zhang, et al. Graph Embedding: A General Framework for Dimensionality Reduction[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'05): San Diego, CA, USA, 2005: 830-837.
[158] J. Gui, W. Jia, L. Zhu, et al. Locality preserving discriminant projections for face and palmprint recognition[J]. Neurocomputing, 2010, 73(13-15), 2696-2707.
[159] C. Deng, X. He, Y. Hu, et al. Learning a Spatially Smooth Subspace for Face Recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition: Minneapolis, MN, USA, 2007: 650-656.
[160] S.C. Yan, H. Wang, Y. Fu, et al. Synchronized sub-manifold embedding for person-independent pose estimation and beyond[J]. IEEE transactions on Image Processing, 2009, 18(1): 202-210.
[161] X.D. Xie, K.M. Lam. Face recognition using elastic local reconstruction based on a single face image[J]. Pattern Recognition, 2008, 41(1): 406-417.
[162] T.P. Zhang, B. Fang, Y.Y. Tang, et al. Topology preserving non-negative matrix factorization for face recognition[J]. IEEE transactions on image processing, 2008, 17(4): 574-584.
[163] J. Chen, X. Chen, J. Yang, et al. Optimization of a training set for more robust face detection[J]. Pattern Recognition, 2009, 42(11): 2828-2840.
[164] O. Arandjelovic, R. Cipolla. A pose-wise linear illumination manifold model for face recognition using video[J]. Computer Vision and Image Understanding, 2009, 113(1): 113-125.
[165] F. Wang, C. Zhang, J.D. Wang, et al. Semi-Supervised Classification Using Linear Neighborhood Propagation[C]. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'06): New York, NY, USA, 2006: 160-167.
[166] S.C. Yan, D. Xu, B.Y. Zhang, et al. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51.
[167] D. de Ridder, R.P.W. Duin. Locally linear embedding for classification[R]. Pattern Recognition Group, Department of Imaging Science&Technology, Delft University of Technology: Delft, Netherlands: 2002.
[168] O. Kouropteva, O. Okun, M. Pietik¨ainen. Selection of the optimal parameter value for the locally linear embedding algorithm[C]. 1st International Conference on Fuzzy Systems and Knowledge Discovery: Singapore, 2002: 359-363.
[169] D. de Ridder, O. Kouropteva, O. Okun, et al. Supervised locally linear embedding[C]. Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing: Istanbul, Turkey, 2003: 333-341.
[170] H. Choi, S. Choi. Robust kernel Isomap[J]. Pattern Recognition, 2007, 40(3): 853-862.
[171] J. Chen, R.P. Wang, S.G. Shan, et al. Isomap Based on the Image Euclidean Distance[C]. 18th International Conference on Pattern Recognition (ICPR'06): Hong Kong, China, 2006: 1110-1113.
[172] J.B. Li, J.S. Pan, S.C. Chu, et al. Kernel class-wise locality preserving projection[J]. Information Sciences, 2008, 178(7): 1825-1835.
[173]赵继东,鲁珂,吴跃.保局投影算法的优化研究[J].电子科技大学学报, 2008, 37(5): 750-752.
[174] J.D. Zhao, K. Lu, Y. Wu. Research on the Optimization of Locality Preserving Projections[J]. Journal of University of Electronic Science and Technology of China, 2008, 37(5): 37-39.
[175] J.G. Daugman. Two-dimensional spectral analysis of cortical receptive field profiles[J]. Vision Research, 1980, 20(10): 847–856.
[176] D. Gabor. Theory of communication[J]. Journal of institute for electrical engineering, 1946, 93: 429-457.
[177] J.J. Henriksen. 3D surface tracking and approximation using Gabor filters [D]. Odense, Denmark: South Denmark University, 2007.
[178] H.J. Nussbaumer. Fast Fourier transform and convolution algorithms[M]. New York, USA: Springer-Verlag, 1982: 1-287.
[179] J.G. Daugman. Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression[J]. IEEE Transaction on Acoustics, Speech, and Signal Processing, 1988, 36(7): 1169–1179.
[180]朱健翔,苏光大,李迎春.结合Gabor特征与AdaBoost的人脸表情识别[J].光电子·激光, 2006, 17(8): 993-998.
[181] Q. Zhu, M.-C. Yeh, K.-T. Cheng, et al. Fast Human Detection Using a Cascade of Histograms of Oriented Gradients[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition: University of California at Santa Barbara, CA, USA, 2007: 1491-1498.
[182] A. Bosch, A. Zisserman, X. Munoz. Representing shape with a spatial pyramid kernel[C]. Proceedings of the 6th ACM international conference on Image and videoretrieval: Amsterdam, Netherlands: 2007: 401-408.
[183] Y. Bai, L.H. Guo, L.W. Jin. A novel feature extraction method using Pyramid Histogram of Orientation Gradients for smile recognition[C]. 16th IEEE International Conference on Image Processing: Cairo, Egypt, 2009: 3305-3308.
[184] P. Viola, M. Jones. Fast and robust classification using asymmetric AdaBoost and a detector cascade[C]. Advances in Neural Information Processing Systems: Cambridge, British Columbia, Canada, 2002: 1311-1318.
[185] T. Serre, L. Wolf, T. Poggio. Object Recognition with Features Inspired by Visual Cortex[C]. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition: Los Alamitos, CA, USA, 2005: 994-1000.
[186] X.F. He, P. Niyogi. Locality preserving projections[C]. Advances in Neural Information Processing Systems: Cambridge, British Columbia, Canada, 2004: 153-160.
[187] W. Jacob, L. Gwen, F. Ian, et al. Towards Practical Smile Detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(11): 2106-2111.
[188] T. Serre, M. Kouh, C. Cadieu, et al. A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex[R]. CBCL MIT paper, 2005.
[189] D. Hubel, T. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J].The Journal of Physiology, 1962, 160(1): 106-154.
[190] J. Sporring, L. Florack, M. Nielsen, et al. Gaussian scale-space theory[M]. USA: Kluwer Academic Publishers Norwell, 1997:1-286.
[191]应自炉,李景文,张有为.基于融合的多类支持向量机[J].计算机工程, 2009, 35(19):187-188.
[192] B. Fasel, J. Luettin. Automatic facial expression analysis: a survey[J]. Pattern Recognition, 2003, 36(1):259-275.
[193] Z.H. Zeng, M. Pantic, G.I. Roisman, et al. A survey of affect recognition methods: Audio, visual, and spontaneous expressions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(1): 39-58.
[194] A. Samala, P.A. Iyengara. Automatic recognition and analysis of human faces and facial expressions: a survey[J]. Pattern Recognition, 1992, 25(1):65-77.
[195] A. Ghaoui. Encyclopedia of human computer interaction[M]. Hershey, PA, USA: Idea Group Inc (IGI), 2006:1-826.
[196] N. Sebe, M.S. Lew, Y. Sun, et al. Authentic facial expression analysis[J]. Image and Vision Computing, 2007, 25(12):1856-1863.
[197] P.S. Aleksic, A.K. Katsaggelos. Automatic facial expression recognition using facial animation parameters and multistream HMMs[J].IEEE Transactions on Information Forensics and Security, 2006, 1(1):3-11.
[198] M.S. Bartlett, G. Littlewort, I. Fasel, et al. Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction[C]. 2003 Conference on Computer Vision and Pattern Recognition Workshop: Madison, Wisconsin, 2003: 53-60.
[199] T. Ojala, M. Pietik?inen, D. Harwood. A Comparative Study of Texture Measures with Classification based on Feature Distribution[J]. Pattern Recognition, 1996, 29(1): 51-59.
[200] T. Ojala, M. Pietik?inen, T. M?enp??. Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns[J].IEEE Transaction on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
[201] C.C. Chiu, Y.L. Chang, Y.J. Lai. The analysis and recognition of human vocal emotions[C]. Proceeding of International Computer Symposium: Tsihchu, Taiwan, R.O.C., 1994: 83-88.
[202] A.J.T. Cacioppo, L.G. Tassinary. Inferring psychological significance from physiological signals[J]. American Psychologist, 1990, 45(1):16-28.
[203] G. Heusch, Y. Rodriguez, S. Marcel. Local Binary Patterns as an Image Preprocessing for Face Authentication[C]. 7th International Conference on Automatic Face and Gesture Recognition (FG'06): Southampton, UK, 2006:9-14.