多姿态人脸检测与表情识别关键技术研究
|
摘要
在人工智能技术研究领域,人脸作为一种重要的生物特征,其检测、识别和表情提取是实现机器智能化的前提和关键技术之一,具有广阔的应用前景,逐渐成为一个活跃的研究分支。
随着图像处理和模式识别技术的进步,人脸信息处理技术获得长足发展,但仍存在很多需要深入研究的问题。例如:姿态、表情、年龄、遮挡、光照等因素依然严重影响人脸检测与识别的效果,表情识别的研究仍处于起步阶段,理论和方法仍有待完善。本文主要是对多姿态人脸检测、人脸表情特征提取与降维、表情分类与表情识别应用等问题进行了深入研究,研究内容与创新性工作主要如下:
1、提出了融合特征和图像方法,解决多姿态人脸检测这一实际应用中亟待解决的难题。算法首先利用人脸特征快速确定人脸方向,分割出大致正向的人脸候选区域,从而避免增加姿态检测器而增大算法的复杂度,克服了基于图像方法对多姿态人脸检测率低的缺点。分别运用AdaBoost、SVM和RVM三种基于图像的方法对人脸候选区域进行分类检测。实验表明,融合特征和图像方法进行多姿态人脸检测,可明显提高检测速度和检测率。
2、通过分析RVM的稀疏性和泛化性能等优于SVM,将RVM用于人脸检测作了有益的尝试。在基于人脸特征方法的基础上,分别采用AdaBoost、SVM和RVM进行多姿态人脸检测,对比研究表明,AdaBoost的检测速度比SVM和RVM快,检测率略低,SVM和RVM的光照鲁棒性比AdaBoost强,RVM的检测效果优于SVM,在光照变化较大的环境下,利用RVM进行人脸检测不惜为一种好方法。
3、针对AdaBoost、SVM、RVM等基于图像的人脸检测方法对样本依赖较强的问题,为了提高样本有效性,取样本长宽比为1.2,重点突出人脸中央纹理较中的部分。特别是在SVM、RVM算法中,提出了采用彩色样本的新方法,并在YC b Cr彩色空间中,有区别地选取各色彩分量的DCT系数作为特征向量,用于分类器的训练。实验表明,该方法增强了样本有效性,这样可提高算法对光照的鲁棒性和检测率。
4、分析了基于Gabor变换的人脸表情特征提取方法,针对Gabor特征向量维数过高和传统特征降维方法的不足,提出了对主要表情区域进行局部Gabor表情特征提取,根据各表情子区域对表情识别贡献不同,采用非均匀采样,提取出既能反映表情变化,又利于表情分类的表情特征,以达到有效的降维。最后,运用DWT和DCT对表情特征做进一步降维,较好地解决了特征提取与降维的问题。
5、对多分类SVM和RVM进行了深入的研究,针对传统的一对一、一对余等多分类方法的不足,提出了基于二对二多分类SVM与RVM的表情分类方法。该方法以四类别分类问题为研究基础,根据每个子分类器的输出结果进行判断,以相对较少的子分类器组合实现快速分类,从而减少了分类误差。在决策根节点,采用近似最优方法将多类别样本分成两组,并使其聚类中心距离最大,且每组样本数据分歧最小。根据六种基本表情识别率不同,设计出较为合理的决策方案,减小误差的累积,获取最优的分类性能。实验表明,该方法明显缩减了训练和测试时间,提高了分类性能。另外,RVM分类性能略优于SVM。
6、研究了基于人脸识别的身份认证系统的活体检测技术,及其在提高系统的防欺骗性能的优缺点。根据当前人脸表情识别在与人相关时识别率高,与人无关时识别率低的特点,提出了通过访问人员能够接受又容易做到的4种基本人脸表情进行识别,在保证系统运行速度和较低的错误接受率的基础上,实现活体检测,有效提高了基于人脸识别身份认证系统的防欺骗性能。
In the field of artificial intelligence research, face is an important biological characteristic. Face detection, face recognition and facial expression recognition, which are the prerequisite of achieving machine intelligence and one of the key technologies of machine intelligence and have broad application prospects, are becoming an active research branch.
With the development of image processing and pattern recognition technology, face process technology has got rapid progress. However, there are still many problems for further research. For example, pose, facial expression, age, occlusion, illumination and other factors still heavily influence the effect of face detection and recognition. Facial expression recognition research is still in its infancy, so the theory and method remains to be improved. In this paper, some issues, such as multi-pose face detection, facial expression feature extraction and dimension reduction, facial expression classification and facial expression recognition applications and others have been deeply studied. The main study contents and innovative work are shown as follows:
1. A method based on combining facial features and image-based method is proposed to solve multi-pose face detection problem which is urgent in practical applications. Firstly, face direction is quickly determined based on facial features, then the approximate frontal face candidates are segmented. Thus reduced the algorithm complexity caused by excessive posture detector. Moreover, overcome the disadvantage of low detection rate of multi-pose face detection in image-based method. Three image-based methods, AdaBoost, SVM and RVM, are used to classify the face candidate regions. The experimental results show that combining facial features and image-based method in multi-pose face detection can significantly improve the detection speed and detection rate.
2. By analyzing the RVM's sparsity and generalization performance are better than SVM's. The RVM is applied for face detection in this paper and have got some useful experience. Based on facial feature method, the comparative study is made on AdaBoost, SVM and RVM used for multi-pose face detection. Experiments show that AdaBoost is faster than SVM and RVM but slightly lower detection rate. SVM and RVM have strong illumination robust than AdaBoost. RVM used for face detection is regarded as a good method in the large illumination change environment.
3. For AdaBoost, SVM, RVM and other image-based face detection methods rely stronger on the issue of the sample, the effectiveness of the sample can be improved by the sample aspect ratio of 1.2, which the face center rich texture information is focused. Especially in SVM and RVM algorithm, a new method using color samples is proposed. In the YC b Cr color space, the DCT coefficients of the color component are differently selected and are regarded as the feature vectors for classifier training. Experiments show that the method enhances the effectiveness of the sample, so that it can improve the robustness and the detection rate in different lighting conditions.
4. By analyzing the facial expression feature extraction methods based on Gabor transformation, as the high dimension of Gabor feature vector and the inadequate of traditional dimensional reduction methods, local Gabor feature extraction method is proposed in major expression regions. For different expression sub-regions have the different contribution to facial expression recognition, inhomogeneous sampling is adopted. the expression changes can be effectively reflected and the expression classification can be easily done by the expression features. And effective dimension reduction can be achieved. Finally, DWT and DCT are used for further dimension reduction, and the feature extraction and dimensionality reduction problem are well resolved.
5. After an in-depth study of the multi-classification SVM and RVM, the expression classification method based on two-against-two SVM or RVM are proposed for the defects of the traditional classification methods based on one-against-one, one-against-the rest and so on. Based on the research of four-category classification method, the two-against-two classification method judges the results according to output of each classifier. It realizes fast classification with a relatively small sub-classifier combination, reducing the classification error. Using approximately optimal approach, multi-class samples are divided into two groups in the decision root node, and the maximum distance of cluster center and the minimal differences of sample data can gained in the same group data. According to the different recognition rate of six basic facial expressions, a more rational decision scheme is designed to reduce the accumulation of error and obtain the best classification performance. Experiments show that the multi-classification method based on two-against-two can obviously reduce the training and testing time and improve the classification performance. Additionally, RVM classification performance is better than SVM.
6. A study of the vivo detection technology, which is used in the identification system based face recognition, and the advantages and disadvantages of using it to improve anti-cheat for system, have been done. The existing facial expression recognition rate is high in case of person-dependent and is low in case of person-independent. Based four basic facial expressions which the visitor can accept and easily to do, the expression recognition method is proposed to realize vivo detection. It can ensure the system speed and low false acceptance rate, and improve the anti-cheat performance for the identification system which is based on face recognition.
随着图像处理和模式识别技术的进步,人脸信息处理技术获得长足发展,但仍存在很多需要深入研究的问题。例如:姿态、表情、年龄、遮挡、光照等因素依然严重影响人脸检测与识别的效果,表情识别的研究仍处于起步阶段,理论和方法仍有待完善。本文主要是对多姿态人脸检测、人脸表情特征提取与降维、表情分类与表情识别应用等问题进行了深入研究,研究内容与创新性工作主要如下:
1、提出了融合特征和图像方法,解决多姿态人脸检测这一实际应用中亟待解决的难题。算法首先利用人脸特征快速确定人脸方向,分割出大致正向的人脸候选区域,从而避免增加姿态检测器而增大算法的复杂度,克服了基于图像方法对多姿态人脸检测率低的缺点。分别运用AdaBoost、SVM和RVM三种基于图像的方法对人脸候选区域进行分类检测。实验表明,融合特征和图像方法进行多姿态人脸检测,可明显提高检测速度和检测率。
2、通过分析RVM的稀疏性和泛化性能等优于SVM,将RVM用于人脸检测作了有益的尝试。在基于人脸特征方法的基础上,分别采用AdaBoost、SVM和RVM进行多姿态人脸检测,对比研究表明,AdaBoost的检测速度比SVM和RVM快,检测率略低,SVM和RVM的光照鲁棒性比AdaBoost强,RVM的检测效果优于SVM,在光照变化较大的环境下,利用RVM进行人脸检测不惜为一种好方法。
3、针对AdaBoost、SVM、RVM等基于图像的人脸检测方法对样本依赖较强的问题,为了提高样本有效性,取样本长宽比为1.2,重点突出人脸中央纹理较中的部分。特别是在SVM、RVM算法中,提出了采用彩色样本的新方法,并在YC b Cr彩色空间中,有区别地选取各色彩分量的DCT系数作为特征向量,用于分类器的训练。实验表明,该方法增强了样本有效性,这样可提高算法对光照的鲁棒性和检测率。
4、分析了基于Gabor变换的人脸表情特征提取方法,针对Gabor特征向量维数过高和传统特征降维方法的不足,提出了对主要表情区域进行局部Gabor表情特征提取,根据各表情子区域对表情识别贡献不同,采用非均匀采样,提取出既能反映表情变化,又利于表情分类的表情特征,以达到有效的降维。最后,运用DWT和DCT对表情特征做进一步降维,较好地解决了特征提取与降维的问题。
5、对多分类SVM和RVM进行了深入的研究,针对传统的一对一、一对余等多分类方法的不足,提出了基于二对二多分类SVM与RVM的表情分类方法。该方法以四类别分类问题为研究基础,根据每个子分类器的输出结果进行判断,以相对较少的子分类器组合实现快速分类,从而减少了分类误差。在决策根节点,采用近似最优方法将多类别样本分成两组,并使其聚类中心距离最大,且每组样本数据分歧最小。根据六种基本表情识别率不同,设计出较为合理的决策方案,减小误差的累积,获取最优的分类性能。实验表明,该方法明显缩减了训练和测试时间,提高了分类性能。另外,RVM分类性能略优于SVM。
6、研究了基于人脸识别的身份认证系统的活体检测技术,及其在提高系统的防欺骗性能的优缺点。根据当前人脸表情识别在与人相关时识别率高,与人无关时识别率低的特点,提出了通过访问人员能够接受又容易做到的4种基本人脸表情进行识别,在保证系统运行速度和较低的错误接受率的基础上,实现活体检测,有效提高了基于人脸识别身份认证系统的防欺骗性能。
In the field of artificial intelligence research, face is an important biological characteristic. Face detection, face recognition and facial expression recognition, which are the prerequisite of achieving machine intelligence and one of the key technologies of machine intelligence and have broad application prospects, are becoming an active research branch.
With the development of image processing and pattern recognition technology, face process technology has got rapid progress. However, there are still many problems for further research. For example, pose, facial expression, age, occlusion, illumination and other factors still heavily influence the effect of face detection and recognition. Facial expression recognition research is still in its infancy, so the theory and method remains to be improved. In this paper, some issues, such as multi-pose face detection, facial expression feature extraction and dimension reduction, facial expression classification and facial expression recognition applications and others have been deeply studied. The main study contents and innovative work are shown as follows:
1. A method based on combining facial features and image-based method is proposed to solve multi-pose face detection problem which is urgent in practical applications. Firstly, face direction is quickly determined based on facial features, then the approximate frontal face candidates are segmented. Thus reduced the algorithm complexity caused by excessive posture detector. Moreover, overcome the disadvantage of low detection rate of multi-pose face detection in image-based method. Three image-based methods, AdaBoost, SVM and RVM, are used to classify the face candidate regions. The experimental results show that combining facial features and image-based method in multi-pose face detection can significantly improve the detection speed and detection rate.
2. By analyzing the RVM's sparsity and generalization performance are better than SVM's. The RVM is applied for face detection in this paper and have got some useful experience. Based on facial feature method, the comparative study is made on AdaBoost, SVM and RVM used for multi-pose face detection. Experiments show that AdaBoost is faster than SVM and RVM but slightly lower detection rate. SVM and RVM have strong illumination robust than AdaBoost. RVM used for face detection is regarded as a good method in the large illumination change environment.
3. For AdaBoost, SVM, RVM and other image-based face detection methods rely stronger on the issue of the sample, the effectiveness of the sample can be improved by the sample aspect ratio of 1.2, which the face center rich texture information is focused. Especially in SVM and RVM algorithm, a new method using color samples is proposed. In the YC b Cr color space, the DCT coefficients of the color component are differently selected and are regarded as the feature vectors for classifier training. Experiments show that the method enhances the effectiveness of the sample, so that it can improve the robustness and the detection rate in different lighting conditions.
4. By analyzing the facial expression feature extraction methods based on Gabor transformation, as the high dimension of Gabor feature vector and the inadequate of traditional dimensional reduction methods, local Gabor feature extraction method is proposed in major expression regions. For different expression sub-regions have the different contribution to facial expression recognition, inhomogeneous sampling is adopted. the expression changes can be effectively reflected and the expression classification can be easily done by the expression features. And effective dimension reduction can be achieved. Finally, DWT and DCT are used for further dimension reduction, and the feature extraction and dimensionality reduction problem are well resolved.
5. After an in-depth study of the multi-classification SVM and RVM, the expression classification method based on two-against-two SVM or RVM are proposed for the defects of the traditional classification methods based on one-against-one, one-against-the rest and so on. Based on the research of four-category classification method, the two-against-two classification method judges the results according to output of each classifier. It realizes fast classification with a relatively small sub-classifier combination, reducing the classification error. Using approximately optimal approach, multi-class samples are divided into two groups in the decision root node, and the maximum distance of cluster center and the minimal differences of sample data can gained in the same group data. According to the different recognition rate of six basic facial expressions, a more rational decision scheme is designed to reduce the accumulation of error and obtain the best classification performance. Experiments show that the multi-classification method based on two-against-two can obviously reduce the training and testing time and improve the classification performance. Additionally, RVM classification performance is better than SVM.
6. A study of the vivo detection technology, which is used in the identification system based face recognition, and the advantages and disadvantages of using it to improve anti-cheat for system, have been done. The existing facial expression recognition rate is high in case of person-dependent and is low in case of person-independent. Based four basic facial expressions which the visitor can accept and easily to do, the expression recognition method is proposed to realize vivo detection. It can ensure the system speed and low false acceptance rate, and improve the anti-cheat performance for the identification system which is based on face recognition.
引文
[1] Pieard R. W.. Affective computing [M]. London, England : MIT Press, 1997
[2]王志良.人工心理学[M].北京:机械工业出版社, 2007
[3] Mehrabian A.. Communication without words [J]. Psychology Today, 1968, 2(4): 53-56
[4] Darwin C. R.. The expression of the emotions in man and animals [M]. London, UK: John Murray, 1872
[5] Ekman P., Friesen W.. Constants across cultures in the Face and emotion [J].Journal of Personality and Social Psycho-Logy, 1975, (17) :123-129
[6] Ekman P., Friesen W.. Facial action coding system: investi-gator’s guide [M].Palo alto: consulting psychologists press, 1978
[7] Suwa M., Sugie N., Fujimora K.. A preliminary note on pattern recognition of human emotional expression [A]. The Fourth International Joint Conference on Pattern Recognition [C]. Japan: 1978: 408-410
[8]文沁,汪增福.基于三维数据的人脸表情识别[J].计算机仿真, 2005, 22(07): 99-103
[9]黄福珍,苏剑波.人脸检测[M].上海:上海交通大学出版社, 2006
[10] Yang M.-H., Kriegman D., Ahuja N.. Detecting Faces in Images: A Survey [J], IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, 24(I): 34-58
[11]梁路宏,艾海舟,徐光祐,等.人脸检测研究综述[J].计算机学报, 2002, 25(05): 449-458
[12]刘芳.应用图像处理技术的人脸表情识别研究[D].北京:北京科技大学, 2003
[13] Hjelmas E., Low B. K.. Face detection: a survey [J]. Computer Vision and Image Understanding, 2001, 83(1): 236-274
[14] Chen D. S., Liu Z. K.. A Survey of Skin Color Detection [J]. Chinese Journal of Computers, 2006, 29(2): 194-207
[15] Yang J., Lu W., Waibel. A Skin Color Modeling and Adaptation [R]. Pittsburgh, PA: CMU, 1997
[16] Hsu R. L., Abdel-Mottaleb M., Jain A. K.. Face detection in color images [J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, 24(5): 696-706
[17] Yullie A, Hallinan P, Cohen D.. Feature extraction from faces using deformable templates [J]. International Journal of Computer Vision, 1992, 8(2):99-111
[18] Turk M., Pentland A..Eigenfaces for Recognition [J].Cognitive Neuro-science, 1991,3(1): 71-86.
[19] Rowley H. A., Baluja S., Kanade T.. Neural Network-Based Face Detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998: 20(1): 23-38.
[20] Nefian A., Hayes M.. Hidden Markov Models for Face Recognition [A]. IEEE International Conference on Acoustics, Speech and Signals Processings [C]. Seattle, Washington: 1998: 2721-2724
[21] Vapnik V. N.. The Nature of Statistical Learning Theory [M]. New York: Springer-Verlag, 1995
[22]张学工.关于统计学习理论与支撑向量机[J].自动化学报, 2000, 26(1): 32-42
[23] Osuna E., Freund R.. Training Support Vector Machines: an Application to Face Detection [A]. Computer Vision and Pattern Recognition [C].San Juan, Puerto Rico: IEEE Computer Soc, 1997: 130-136
[24]马勇,丁晓青.基于层次型支撑向量机的人脸检测[J].清华大学学报(自然科学版), 2003, 43(1): 35-38
[25] Stan Z. Li, Long Zhu, ZhenQiu Zhang, et al. Statistical Learning of Multi-View Face Detection [A]. The 7th European Conference on Computer Vision [C]. Copenhagen, Denmark: 2002: 117-121
[26] Pantic M., Rothkrantz L. J. M.. Automatic analysis of facial expressions: the state of the art [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12): 1424-1446
[27] Fasel B., Luettin J.. Automatic facial expression analysis: a survey [J]. Pattern Recognition, 2003, 36(1): 259-275
[28]薛雨丽,毛峡,郭叶,等.人机交互中的人脸表情识别研究进展[J].中国图象图形学报, 2009, 14(5): 764-772
[29]章品正,王征,赵宏玉.面部表情特征抽取的研究进展[J].计算机工程与应用, 2006, 42(9): 38-41
[30] Andrew J., Calder A., Burton M., et al. A Principal Component Analysis of Facial Expressions [J]. Vision research, 2001, 1(9): 1179-1208
[31]程剑,应自炉.基于二维主分量分析的面部表情识别[J].计算机工程与应用, 2006, 42(5): 32-33
[32] Bartlett M. S., hades H. M., Sejnowski T. J.. Independent component representations for face recognition [A]. Human Vision and Electronic Imaging [C]. 1998: 528-539
[33]徐正光,闫恒川,张立欣.独立成分分析在表情识别中的应用[J].微计算机信息,2006, 22(17): 287-289
[34] Lee T.. Image representation using 2-D Gabor wavelets [J]. IEEE Trans Pattern Analysis and Machine Intelligence, 1996, 18(10): 959-971
[35] Ye Jingfu, Zhan Yongzhao, Song Shunlin. Facial expression features extraction based on Gabor wavelet transformation [J]. IEEE International Conference on Systems, Man and Cybernetics, 2004, 3: 2215-2219
[36]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
[37]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
[38]万峰,杜明辉.人脸识别中一种新的Gabor特征提取方法[J].华南理工大学学报(自然科学版) , 2004, 32 (8) : 528
[39]刘晓昊,章毓晋.基于Gabor直方图特征和MVBoost的人脸表情识别[J].计算机研究与发展, 2007, 44(7): 1089-1096
[40] Littlewort G., Bartlett M. S., Fasel I., et al. Dynamics of facial expression extracted automatieally from video [J]. Image and Vision Computing, 2006, 24(6): 615-625
[41] Shan C., Gong S., McOwan P. W.. Robust facial expression recognition using local binary patterns [A]. IEEE International Conference on Image Proeessing [C]. 2005: 370-373
[42]崔洁,冯晓毅.一种新的人面部表情识别方法[J].计算机工程与应用, 2006, 42(29): 78-80.
[43]付晓峰,韦巍.基于多尺度中心化二值模式的人脸表情识别[J].控制理论与应用, 2009, 26(6): 629-633
[44] Cootes T., Edwards G., Taylor C.. Active Appearance Models [A]. The 5th European Conference on Computer Vision [C]. Freiburg, Germany: Springer, 1998: 484-498
[45]左坤隆,刘文耀.基于活动外观模型的人脸表情分析与识别[J].光电子.激光, 2004, 15(7): 853-857
[46] Cootes T.F., Taylor C.J.. Active Shape Models-‘Smart Snakes’[A]. British Machine Vision Conference [C]. 1992: 266-275
[47] Huang C. L., Huang Y. M.. Facial Expression Recognition Using Model-based Feature Extraction and Action Parameters Classification [J]. Journal of Visual Communication and Image Representation, 1997, 8(3): 278-290
[48]杨国亮,于仲安.基于改进光流算法和HMM的面部表情识别[J].微计算机信息, 2008,24(01)
[49]金辉,高文.基于特征流的面部表情运动分析及应用[J].软件学报, 2003, 14(12): 2098-2105
[50]余棉水,黎绍发.基于光流的动态人脸表情识别[J].微电子学与计算机, 2005, 22(7): 113-115
[51] Horn B., Schunck B.. Determining Optical Flow [J]. Artifical Intelligenee, 1981, 17(1-3): 185-203
[52]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
[53] Bourel F., Chibelushi C. C., Low A. A robust facial expression recognition using a state-based model of spatially-localized facial dynamics [A]. IEEE International Conference on Automatic Face and Gesture Recognition [C]. Washington, DC, USA: 2002:106-111
[54]陈伟宏.基于特征点跟踪的面部表情强度度量[J].重庆工学院学报(自然科学版), 2008, 22(9): 176-180
[55] Ma L., Khorasani K.. Facial Expression Recognition Using Constructive Feedforward Neural Networks [J]. IEEE Transactions on Systems Man and Cybernetics, 2004, 34(3): 1588-1595
[56]刘松,应自炉.基于局部特征和整体特征融合的面部表情识别[J].电子技术应用, 2005, 31(3): 4-6
[57] Baum L. E., Petrie T.. Statistical inference for probabilistic functions of finite stateMarkov chains [J]. Ann. Math. Stat, 1966, 37(7): 1554-1563
[58] Baum L. E.. An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov process [J], Inequalities, 1972, 3(1): 1-8
[59] Baum L. E., Eagon J. A.. An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for recology [J]. Bull Amer. Math. Stat, 1967, 37(2): 360-363
[60] Asakaguchi T., Morishima S.. Face feature extraction from spa-tial frequency for dynamic expression recognition [A]. The International Conference on Pattern Recognition [C]. Washington, DC: IEEE Computer Society, 1996: 451-455
[61]郑芳颖,赵杰煜.基于改进型嵌入式隐马尔可夫模型的表情识别方法[J].模式识别与人工智能, 2008, 21(6): 836-842
[62]周书仁,梁昔明,朱灿,等.基于ICA与HMM的表情识别[J].中国图象图形学报, 2008, 13(12): 2321-2328
[63] Padgett C., Cottrell G. W.. Representing Face Images for Emotion Classification[A]. Conference Advances in Neural Information Processing Systems [C]. Denver, USA: 1996: 894-900
[64]周书仁,梁昔明,杨秋芬,等.类间学习神经网络的人脸表情识别[J].计算机应用研究, 2008, 25(7): 2219-2222
[65]唐京海,张有为.基于FLD特征提取的SVM人脸表情识别方法[J].计算机工程与应用, 2006, (11): 10-12
[66]徐琴珍,章品正,裴文江,等.基于混淆交叉支撑向量机树的自动面部表情分类方法[J].中国图象图形学报, 2008, 13(7): 1329-1134
[67] Sim T., Baker S., BsatM.. Proceedings of Automatic Face and Gesture Recognition [J] . The CMU Pose, Illumination, and Expression(PIE) database, Washington, DC, USA, 2002, 9(6): 53-58
[68] Wen Gao, Bo Cao, Shiguang Shan, et al.The CAS-PEAL Large-Scale Chinese Face Database and Evaluation Protocols [R]. Joint Research & Development Laboratory, CAS, 2004
[69] Phillips P. J., Wechsler H., Huang J., et al.. The FERET database and evaluation procedure for face recognition algorithms [J]. Image and Vision Computing, 1998, 16(5): 295-306
[70] Phillips P. J., Moon H., Rizvi S. A., et al.. The FERET evaluation methodology for face recognition algorithms [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22: 1090-1104
[71] Lyons M. J., Akamatsu S., Kamachi M., et al.. Coding facial expressions with Gabor wavelets [A]. Third IEEE International Conference on Automatic Face and Gesture Recognition [C]. Nara Japan: IEEE Computer Society, 1998, 200-205
[72] Yang M.-H., Kriegman D., Ahuja N.. Detecting Faces in Images: A Survey [J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, 24(I): 34--58
[73] Moses Y., Adini Y., Ullman S.. Face recognition: the problem of compensating for changes in illumination direction [A]. ECCV-94 [C]. Berlin: Springer-Verlag, 1994: 286-296
[74] Adini Yael, Moses Yael, Ullman Shimon. Face Recognition: The Problem of Compensating for Changes in Illumination Ditection [J]. IEEE Transactions On PatternAnalysis And Machine Intelligence, 1997, 19(7):721-733
[75] Viola P., Jones M.. Robust Real-time Face Detection [A].IEEE ICCV Workshop on Statistical and Computational Theories of Vision [C]. Vancouver,Canada: 2001:7-14
[76] Schneiderman H., Kanade T.. A statistical method for 3D object detection applied to faces and cars [A]. Computer Vision and Pattern Recognition [C]. Hilton Head Island, USA, 2000: 746-751
[77] Li Y.,Gong S.,Liddell H.Support vector regression and classification based multi-view face detection and recognition [A]. Automatic Face and Gesture Recognition [C].Grenoble, France, 2000: 300-305
[78] Feraud R.,Bernier O.J.,Viallet J.-E., et al. A fast and accurate face detector based on neural networks [J].IEEE Trans.Pattern Analysis and Machine Intelligence, 2001, 23(1): 42-53
[79] Shih F. Y., Chuang C.. Automatic extraction of head and face boundaries and facial features [J]. Information Sciences, 2004, 158: 117-130
[80] YANG J., LU W., WAIBEL. A Skin-color modeling and adaptation [A] Third Asian Conference on Computer Vision [C]. London, UK: Springer-Verlag, 1998: 687-694
[81] Kakumanu P., Makrogiannis S., Bourbakis N.. A survey of skin-color modeling and detection methods [J]. Pattern Recognition, 2007, 40(3): 1106 - 1122
[82]赵丽红,孙晓琳,王宇飞,等.基于肤色的人脸检测[J].工程图学学报, 2005, 26(3): 84-88
[83] YANG J., WAIBEL A.. Real-time face tracker [A] The 3rd IEEE Workshop on Applications of Computer Vision [C]. Washington, USA: IEEE Computer Society, 1996: 142-147
[84]衣晓飞,陈福接.单人图像中人眼的快速定位[J].计算机工程与科学, 2001, 23(3): 16-17
[85] VIOLA P., JONES M.. Rapid object detection using a boosted cascade of simple features [A]. IEEE Conference on Computer Vision and Pattern Recognition [C]. Washington, USA: IEEE Computer Society, 2001: 511 - 518
[86] Pageorgiou C., Oren M., Poggio T.. A General Framework for Objeet Deteetion [A]. International Confereneeon Computer Vision [C]. Jnuar, 1998: 555-562
[87] Oren M., Pageorgiou C., Poggio T.. Example-based objeet detection in imges by eomponents [J]. IEEE Trans.on Pattern Analysis and Maehine Inielligenee, 23(4), 2001: 349-361
[88] Freund Y., Schapire R. E.. A decision-theoretic generalization of online learning and an application to boosting [A] The second european conference on computational learning theory[C]. London, UK: Springer-Verlag, 1995: 23-37
[89] Kearns M., Valiant L. G.. Cryptographic limitations on learning boolean formulae and finite automata [J]. Journal of the ACM, 1994, 41(1): 67-95
[90]边肇祺,张学工.模式识别[M]. 2版.北京:清华大学出版社, 2000
[91]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
[92] Campbell C. Algorithmic approaches to training support vector machines: a survey [A]. The 8th european symposium on artificial neural networks [C]. Bruges, Belgium: 2000: 27-36
[93] Vapnik V N.统计学习理论的本质[M].张学工.北京:清华大学出版社, 2000
[94] Vapnik V, Levin E, Le Cun Y. Measuring the VC-dimension of a learning machine [J]. Neural computation, 1994, 6: 851-876
[95] Burges C J C. A tutorial on support vector machines for pattern recognition [J]. Data mining and knowledge discovery, 1998, 2(2): 121-167
[96] Tipping M. The Relevance Vector Machine. Advances in Neural Information Processing System [J]. MIT Press, 2000, 12: 652-658
[97] Tipping M. E., Faul A. C.. Fast Marginal Likelihood Maximization for Sparse Bayesian Models [R]. http://www.miketipping.com/papers/met-fastsbl.pdf, 2003-01-06
[98] Yang Z. R.. A fast algorithm for relevance vector machine [A]. The 7th intelligent data engineering and automated learning [C]. Berlin Heidelberg: Springer-Verlag, 2006: 33-39
[99] Chan W., Chan C., Cheung K., et a1. On the modeling of nonlinear dynamic systems using support vector neural networks [J]. Engineering application of artificial intelligence, 2001, (14): 105-113
[100] Smola A., Scholkhopf B.. A tutorial on support vector regression [J]. Statistics and computing, 2004, (14): 199-222
[101] Muller K., Mika S., Ratsch G., et al. An introduction to kernel-based learning algorithms [J]. IEEE Trans.Neural Network, 2001, 12(2): 181-201
[102] Suykens J.. Support vector machines:a nonlinear modeling and control perspective [J]. Euro.J. Control, Special Issue on Fundamental Issues in Control, 2001, 7(2-3): 311-327
[103] Espinoza M., Suykens J., Moor B.. Kernel based partially linear models and nonlinear identification [J]. IEEE Transactions on Automatic Control, 2005, 50(10): 1602-1606
[104] Zhang H., Wang X., Zhang C., et al. Robust identification of non-linear dynamic systems using support vector machine [J]. IEE Proceedings-Science, Measurement and Technology, 2006, 153(3): 125-129
[105] Tipping M. Sparse bayesian learning and the relevance vector machine [J]. Journal of machine learning research, 2001, 1: 211-244
[106] N Aronszajn. Theory of reproducting kernels [J]. Transactions of the American mathematical society, 1950, 686, 337-404
[107] Chen S., Gurm S., Harris C.. The relevance vector machine technique for channel equalization application [J]. IEEE Trans.Neural Networks, 2001,12(6): 1529-1532
[108] Pierre Baldi, Soren Brunak.Bioinformatics: the machine learning approach [M]. 2. MIT Press, 2001
[109]王利民.贝叶斯学习理论中若干问题的研究[D].吉林:吉林大学, 2005
[110] Bishop C.M.. Pattem recognition and machine learning [M]. Springer, 2006
[111] Tipping M. Bayesian inference:An introduction to principles and practice in machine learning [J]. Advanced lectures on machine learning, 2004, 3176: 4l-62
[112]宫秀军.贝叶斯学习理论及其应用研究[D].中国科学院研究生院(计算技术研究所), 2002
[113]曲寒冰.贝叶斯框架下广义结合泛函网络与广义约束神经网络的研究与应用[D].中国科学院研究生院(中国科学院自动化所), 2007
[114] Lienhart R., Maydt J. An extended set of haar-like features for rapid object detection [A] International Conference on Image Processing [C]. Piscataway, USA: IEEE Press, 2002: 900-903.
[115] Jones M., Viola P.. Fast multi-view face detection [EB/OL]. [2003-08-16]. http://www.merl.com/reports/docs/TR2003-96.pdf.
[116] Chang C. C., Lin C. J.. LIBSVM -- A Library for Support Vector Machines[DB/OL].http://www.csie.ntu.edu.tw/~cjlin/libsvm/ 2009
[117]唱轶钲,山世光,高文,等. Gabor特征判别分析人脸识别方法的误配准鲁棒性分析[J].计算机工程与应用, 2005, 41(5): 56-59
[118] Gabor D.. Theory of communication [J]. Journal of institute for electrical engineering, 1946, 93: 429-457
[119] Daugman J.G.. 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, 1985,85(2): 1160-1169
[120] Daugman J.G.. Two-dimensional spectral analysis of cortical receptive field profiles [J]. Vision Research, 1980, 20(10): 847-856
[121] Kong W.K., Zhang D, Li W.. Palmprint feature extraction using 2-D Gabor filters [J]. Patern Recognition. 2003, 36: 2339-2347
[122] Su Y. M., Wang J.F.. A novel stroke extraction method for Chinese characters using Gabor filters [J]. Patern Recognition. 2003, 36: 635-647
[123] Marcelja S.. Mathematical description of the responses of simple cortical cells [J]. Journal of the Optical Society of America, 1980, 70(11): 1297-1300
[124] Daugman J. G., Two-dimensional spectral analysis of cortical receptive field profiles[J], Vision Research, 1980, 20(10), 847-856
[125] Liu C. J.. 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
[126]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[J].中国图象图形学报, 2007, 12(2): 322-329
[127] Lyons M., Budynek J., Akamastu S.. Automatic classification of single facial images [J]. IEEE Trans.pattemanalysisandmaehineIntelligenee, 1999, 21(12): 1357-1362
[128]焦峰,山世光,崔国勤,等.一种基于局部特征分析的人脸识别方法[J].计算机辅助设计与图形学学报. 2003, 15(1): 53-58
[129] LiuD H., Lam K.M., Shen L.S.. Optinal sampling of Gabor features for face recognition[J].Pattern recognition letters, 2004, 25(2): 267-276
[130]史金玉.人脸表情识别算法研究[D].重庆:重庆大学, 2009
[131]曹怀信,赵建伟.小波分析发展综述[J].成阳师范学院学报, 2002, 17(6): 5-7
[132]冉启华.小波变换与分数傅里叶变换理论及应用[M],哈尔滨工业大学出版社, 2001
[133]李晓华,沈兰荪,基于压缩域的图像检索技术[J],计算机学报, 2003, 26(9): 1051-1059
[134] Mandal I. K., Idris F., Panchanatha S.. A critical evaluation of image and video indexing techniques in the compressed domain [J]. Image and Vision Computing, 1999, 17: 513-529
[135]刘艳,李宏东. DCT域图象处理和特征提取技术[J].中国图象图形学报, 2003, 8(2): 121-128
[136] Ahmed N., Rao K. R.. Discrete Cosine Trransform [J]. IEEE Trans. on computers, 1974,23(1): 90-93
[137] Feng G. C., Jiang J.. Image extraction in DCT domain [A]. Image Signal Process [C]. 2003, 1(150): 20–27
[138]蒋斌,杨国胜,马晓燕. DCT和2DPCA降维与识别算法的比较研究[J].河南大学学报(自然科学版), 2008, 38(6):636-639
[139] David Salomon,数据压缩原理与应用[M],电子工业出版社, 2002
[140]范听炜.支持向量机算法的研究及其应用[D].杭州:浙江大学, 2003
[141]王晓辉.支持向量机在人脸识别中的应用[D].哈尔滨:哈尔滨工业大学, 2006
[142]应自炉,唐京海,李景文,等.支持向量鉴别分析在人脸表情识别中的应用[J].电子学报, 2008, 36(4): 725-730
[143] Hsu Chih-Wei, Lin Chih-Jen. A comparison of methods for multiclass support vector machines [J]. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425
[144]刘志刚,李德仁,秦前清,等.支持向量机在多类分类问题中的推广[J].计算机工程与应用, 2004, (7): 10-13
[145]黄勇,郑春颖,宋忠虎.多类支持向量机算法综述[J].计算机技术与自动化, 2005, 24(4): 61-63
[146] Hu Zhonghui, Cai Yunze, he Xing, et al. Fusion of multi-class support vector machines for fault diagnosis [J]. The American Control Conference. Portland, United States: 2005: 1941-1945
[147] Irene Kotsia, Ioannis 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
[148] Irene Kotsia, Nikolaos Nikolaidis, Ioannis Pitas. Facial Expression Recognition in Videos using a Novel Multi-Class Support Vector Machines Variant [J]. Acoustics, Speech and Signal Processing, 2007, 2: 585-588
[149]王志良,刘芳,王莉.基于计算机视觉的表情识别技术综述[J].计算机工程,2006, 32(11): 231-233
[150] Gwen Littlewort, Ian Fasel, Marian Stewart Bartlett, et al.. Fully Automatic Coding of Basic Expressions from video [R]. INC MPLab TR, 2002
[151]刘志刚,李德仁,秦前清,等.支持向量机在多类分类问题中的推广[J].计算机工程与应用, 2004, 40(7):10-13
[152] Krebel U.. Pairwise classification and support vector machines [A]. Advances in kernelMethods: Support Vector Learning [C]. MA: Cambridge, MIT press, 1999: 255-268
[153] Platt J. C., Cristianini N., Shawe-Taylor J.. Large margin DAGs for multi-class classification [A]. Advances in Neural Information Processing Systems [C], 2000, 12: 541-553
[154] Cheong S.. Support vector machines with Binary tree architecture for multi-class classification [J]. Neural Information Porcessing-Leters and Reviews, 2004, 2: 47-51
[155]孟媛媛,刘希玉.一种新的基于二叉树的SVM多类分类方法[J].计算应用, 2005, 25(11): 2653-2657
[156] Ghani R.. Using error-correcting codes for text classification [A]. International Conference on Machine Learning [C], 2002, 303--310
[157] Solbing F. S.. Multi-class pattern recognition problems with tree-structured support vector machines [A]. Pattern Recognition: 23rd DAGM Symposium [C], Munich, Germany, 2001:283-290
[158]于清,赵晖.一种2_a_2支持向量机多类分类新方法[J].计算机工程与应用. 2008, 44(25): 186-188
[159] Kanade T., Cohn J. F., Tian Y.. Comprehensive database for facial expression analysis [A]. Automatic Face and Gesture Recognition [C], Grenoble, France, 200: 46-53.
[160] A. Jain, R. Bolle, and S. Pankanti, Introduction to Biometrics [M], Kluwer Academic Publishers, 1999
[161] Jain A. K., Flynn P., Ross A.. Handbook of Biometrics [M], Springer, 2007
[162]李子青.人脸识别发展新趋势—“三化两合”[J].中国安防, 2008(7): 76-80
[163]田捷,杨鑫.生物特征识别技术理论与应用[M].北京:电子工业出版社, 2005
[164]孙瀚.基于指纹识别身份验证的数据采集系统设计与实现[D].电子科技大学, 2005
[165]曹国到,毛志红,梅园,等.一种快速的指纹参照点对选取方法研究[J].中国图象图形学报, 2009, 14(3): 401-407
[166] Prokoski F. J.. History, current status, and future of infrared identification [A]. IEEE Workshop on Computer Vision beyond Visible Spectrum : Methods and Applications[C], Hilton Head, SC, USA, 2000: 5
[167]李江.红外图像人脸识别方法研究[D].长沙:国防科学技术大学, 2005
[168]伍世虔,韦礼珍,方志军.红外人脸识别技术[J].中国图象图形学报, 2008, 13(12): 2260-2269
[169] Kong S. G., Heo J., Abidi B. R., et al. Recent advances in visual and infrared racerecognition--a review [J]. Computer Vision and Image Understanding, 2005, 97(1): 103-135
[170]陈倩.多生物特征融合身份识别研究[D].浙江:浙江大学, 2007
[171] BioID homepage [EB/OL]. http://www.bioid.com/
[172]张一鸣.人脸表情识别[D].辽宁:大连理工大学, 2006
[173] Zhao W., Chellappa R., Rosenfeld A., et al. Face Recognition: A Literature Survey [J]. ACM Computing Surveys, 2003:399-458
[174]刘党辉,沈兰荪, Kin-Man Lam.人脸识别研究进展[J].电路与系统学报, 2004, 9(1):85-94
[175] Turk M., Pentland A.. Eigenfaes for recognition [J]. Journal of Cognitive Neuroscience, 1991, 3(1):71-86
[176] Kirby M., Sirovieh L.. Application of the Karhunen-Loeve procedure for the characterizization of human faces [J]. IEEE Transactiontions on Pattern Analysis and Machcine Intelligence, 1991, 12(l): 103-108
[177] Belhumeur P. N., Hespanha J. P., Kriegman D. J.. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720
[178] Zheng W. S., Lai J. H., Yuen P. C.. A-fisher: a new LDA-based face recognition algorithm with selcction of principal components [J]. IEEE Transactions on Systems, Man and Cybernetics, 2005, 35(5):1065-1078
[179] Yank M.H., Ahuja N., Kriegman D.. Face recognition using kernel eigenfaces [A], International Conference on Image Processing[C], 2000:37-40
[180]刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法[J].自动化学报, 2003, 129(16): 901-911
[181] Lu J., Plataniotis K.N., Venetsanopoulos A.N.. Face rcoognition using kernel direct disaiminant analysis algorithms [J]. IEEE Transactions on Neural Networks, 2003, 14(1):117-126
[182] Yang J., Frangi A.F, Yang J.Y., et al. KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(2): 230-244
[183] T. Kohonen. Self-organization and associative memory[M]. Berlin: Springer, 1998
[184] Lahdenoja O., Laiho M., Paasio A.. Reducing the feature vector length in local binary pattern based face recognition [A]. Image Processing[C], Finland, 2005, 2: 914-917
[185] Rodriguez Y., Marcel S., Face authentication using adapted local binary pattern histograms[A], European Conference on Computer Vision, ser. Lecture Notes in Computer Science[C], Springer: 2006, 3954: 321-332
[186] Lee T., Image representation using 2-D Gabor wavelets [J]. IEEE Trans Pattern Analysis and Machine Intelligence , 1996, 18(10): 959-971
[187] Li B., Liu Y. H.. When eigenfaces are combined with wavelets [J]. Knowledge-based Systems, 2002, 15: 343-347
[188] Lai J. H., Yuen P. C., Feng G. C.. Face recognition using holistic Fourier invariant features [J]. Pattern Recognition, 2001, 34(1): 95-109
[189]王志良,孟秀艳.人工工程学[M].北京:机械工业出版社, 2008
[2]王志良.人工心理学[M].北京:机械工业出版社, 2007
[3] Mehrabian A.. Communication without words [J]. Psychology Today, 1968, 2(4): 53-56
[4] Darwin C. R.. The expression of the emotions in man and animals [M]. London, UK: John Murray, 1872
[5] Ekman P., Friesen W.. Constants across cultures in the Face and emotion [J].Journal of Personality and Social Psycho-Logy, 1975, (17) :123-129
[6] Ekman P., Friesen W.. Facial action coding system: investi-gator’s guide [M].Palo alto: consulting psychologists press, 1978
[7] Suwa M., Sugie N., Fujimora K.. A preliminary note on pattern recognition of human emotional expression [A]. The Fourth International Joint Conference on Pattern Recognition [C]. Japan: 1978: 408-410
[8]文沁,汪增福.基于三维数据的人脸表情识别[J].计算机仿真, 2005, 22(07): 99-103
[9]黄福珍,苏剑波.人脸检测[M].上海:上海交通大学出版社, 2006
[10] Yang M.-H., Kriegman D., Ahuja N.. Detecting Faces in Images: A Survey [J], IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, 24(I): 34-58
[11]梁路宏,艾海舟,徐光祐,等.人脸检测研究综述[J].计算机学报, 2002, 25(05): 449-458
[12]刘芳.应用图像处理技术的人脸表情识别研究[D].北京:北京科技大学, 2003
[13] Hjelmas E., Low B. K.. Face detection: a survey [J]. Computer Vision and Image Understanding, 2001, 83(1): 236-274
[14] Chen D. S., Liu Z. K.. A Survey of Skin Color Detection [J]. Chinese Journal of Computers, 2006, 29(2): 194-207
[15] Yang J., Lu W., Waibel. A Skin Color Modeling and Adaptation [R]. Pittsburgh, PA: CMU, 1997
[16] Hsu R. L., Abdel-Mottaleb M., Jain A. K.. Face detection in color images [J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, 24(5): 696-706
[17] Yullie A, Hallinan P, Cohen D.. Feature extraction from faces using deformable templates [J]. International Journal of Computer Vision, 1992, 8(2):99-111
[18] Turk M., Pentland A..Eigenfaces for Recognition [J].Cognitive Neuro-science, 1991,3(1): 71-86.
[19] Rowley H. A., Baluja S., Kanade T.. Neural Network-Based Face Detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998: 20(1): 23-38.
[20] Nefian A., Hayes M.. Hidden Markov Models for Face Recognition [A]. IEEE International Conference on Acoustics, Speech and Signals Processings [C]. Seattle, Washington: 1998: 2721-2724
[21] Vapnik V. N.. The Nature of Statistical Learning Theory [M]. New York: Springer-Verlag, 1995
[22]张学工.关于统计学习理论与支撑向量机[J].自动化学报, 2000, 26(1): 32-42
[23] Osuna E., Freund R.. Training Support Vector Machines: an Application to Face Detection [A]. Computer Vision and Pattern Recognition [C].San Juan, Puerto Rico: IEEE Computer Soc, 1997: 130-136
[24]马勇,丁晓青.基于层次型支撑向量机的人脸检测[J].清华大学学报(自然科学版), 2003, 43(1): 35-38
[25] Stan Z. Li, Long Zhu, ZhenQiu Zhang, et al. Statistical Learning of Multi-View Face Detection [A]. The 7th European Conference on Computer Vision [C]. Copenhagen, Denmark: 2002: 117-121
[26] Pantic M., Rothkrantz L. J. M.. Automatic analysis of facial expressions: the state of the art [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12): 1424-1446
[27] Fasel B., Luettin J.. Automatic facial expression analysis: a survey [J]. Pattern Recognition, 2003, 36(1): 259-275
[28]薛雨丽,毛峡,郭叶,等.人机交互中的人脸表情识别研究进展[J].中国图象图形学报, 2009, 14(5): 764-772
[29]章品正,王征,赵宏玉.面部表情特征抽取的研究进展[J].计算机工程与应用, 2006, 42(9): 38-41
[30] Andrew J., Calder A., Burton M., et al. A Principal Component Analysis of Facial Expressions [J]. Vision research, 2001, 1(9): 1179-1208
[31]程剑,应自炉.基于二维主分量分析的面部表情识别[J].计算机工程与应用, 2006, 42(5): 32-33
[32] Bartlett M. S., hades H. M., Sejnowski T. J.. Independent component representations for face recognition [A]. Human Vision and Electronic Imaging [C]. 1998: 528-539
[33]徐正光,闫恒川,张立欣.独立成分分析在表情识别中的应用[J].微计算机信息,2006, 22(17): 287-289
[34] Lee T.. Image representation using 2-D Gabor wavelets [J]. IEEE Trans Pattern Analysis and Machine Intelligence, 1996, 18(10): 959-971
[35] Ye Jingfu, Zhan Yongzhao, Song Shunlin. Facial expression features extraction based on Gabor wavelet transformation [J]. IEEE International Conference on Systems, Man and Cybernetics, 2004, 3: 2215-2219
[36]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
[37]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
[38]万峰,杜明辉.人脸识别中一种新的Gabor特征提取方法[J].华南理工大学学报(自然科学版) , 2004, 32 (8) : 528
[39]刘晓昊,章毓晋.基于Gabor直方图特征和MVBoost的人脸表情识别[J].计算机研究与发展, 2007, 44(7): 1089-1096
[40] Littlewort G., Bartlett M. S., Fasel I., et al. Dynamics of facial expression extracted automatieally from video [J]. Image and Vision Computing, 2006, 24(6): 615-625
[41] Shan C., Gong S., McOwan P. W.. Robust facial expression recognition using local binary patterns [A]. IEEE International Conference on Image Proeessing [C]. 2005: 370-373
[42]崔洁,冯晓毅.一种新的人面部表情识别方法[J].计算机工程与应用, 2006, 42(29): 78-80.
[43]付晓峰,韦巍.基于多尺度中心化二值模式的人脸表情识别[J].控制理论与应用, 2009, 26(6): 629-633
[44] Cootes T., Edwards G., Taylor C.. Active Appearance Models [A]. The 5th European Conference on Computer Vision [C]. Freiburg, Germany: Springer, 1998: 484-498
[45]左坤隆,刘文耀.基于活动外观模型的人脸表情分析与识别[J].光电子.激光, 2004, 15(7): 853-857
[46] Cootes T.F., Taylor C.J.. Active Shape Models-‘Smart Snakes’[A]. British Machine Vision Conference [C]. 1992: 266-275
[47] Huang C. L., Huang Y. M.. Facial Expression Recognition Using Model-based Feature Extraction and Action Parameters Classification [J]. Journal of Visual Communication and Image Representation, 1997, 8(3): 278-290
[48]杨国亮,于仲安.基于改进光流算法和HMM的面部表情识别[J].微计算机信息, 2008,24(01)
[49]金辉,高文.基于特征流的面部表情运动分析及应用[J].软件学报, 2003, 14(12): 2098-2105
[50]余棉水,黎绍发.基于光流的动态人脸表情识别[J].微电子学与计算机, 2005, 22(7): 113-115
[51] Horn B., Schunck B.. Determining Optical Flow [J]. Artifical Intelligenee, 1981, 17(1-3): 185-203
[52]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
[53] Bourel F., Chibelushi C. C., Low A. A robust facial expression recognition using a state-based model of spatially-localized facial dynamics [A]. IEEE International Conference on Automatic Face and Gesture Recognition [C]. Washington, DC, USA: 2002:106-111
[54]陈伟宏.基于特征点跟踪的面部表情强度度量[J].重庆工学院学报(自然科学版), 2008, 22(9): 176-180
[55] Ma L., Khorasani K.. Facial Expression Recognition Using Constructive Feedforward Neural Networks [J]. IEEE Transactions on Systems Man and Cybernetics, 2004, 34(3): 1588-1595
[56]刘松,应自炉.基于局部特征和整体特征融合的面部表情识别[J].电子技术应用, 2005, 31(3): 4-6
[57] Baum L. E., Petrie T.. Statistical inference for probabilistic functions of finite stateMarkov chains [J]. Ann. Math. Stat, 1966, 37(7): 1554-1563
[58] Baum L. E.. An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov process [J], Inequalities, 1972, 3(1): 1-8
[59] Baum L. E., Eagon J. A.. An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for recology [J]. Bull Amer. Math. Stat, 1967, 37(2): 360-363
[60] Asakaguchi T., Morishima S.. Face feature extraction from spa-tial frequency for dynamic expression recognition [A]. The International Conference on Pattern Recognition [C]. Washington, DC: IEEE Computer Society, 1996: 451-455
[61]郑芳颖,赵杰煜.基于改进型嵌入式隐马尔可夫模型的表情识别方法[J].模式识别与人工智能, 2008, 21(6): 836-842
[62]周书仁,梁昔明,朱灿,等.基于ICA与HMM的表情识别[J].中国图象图形学报, 2008, 13(12): 2321-2328
[63] Padgett C., Cottrell G. W.. Representing Face Images for Emotion Classification[A]. Conference Advances in Neural Information Processing Systems [C]. Denver, USA: 1996: 894-900
[64]周书仁,梁昔明,杨秋芬,等.类间学习神经网络的人脸表情识别[J].计算机应用研究, 2008, 25(7): 2219-2222
[65]唐京海,张有为.基于FLD特征提取的SVM人脸表情识别方法[J].计算机工程与应用, 2006, (11): 10-12
[66]徐琴珍,章品正,裴文江,等.基于混淆交叉支撑向量机树的自动面部表情分类方法[J].中国图象图形学报, 2008, 13(7): 1329-1134
[67] Sim T., Baker S., BsatM.. Proceedings of Automatic Face and Gesture Recognition [J] . The CMU Pose, Illumination, and Expression(PIE) database, Washington, DC, USA, 2002, 9(6): 53-58
[68] Wen Gao, Bo Cao, Shiguang Shan, et al.The CAS-PEAL Large-Scale Chinese Face Database and Evaluation Protocols [R]. Joint Research & Development Laboratory, CAS, 2004
[69] Phillips P. J., Wechsler H., Huang J., et al.. The FERET database and evaluation procedure for face recognition algorithms [J]. Image and Vision Computing, 1998, 16(5): 295-306
[70] Phillips P. J., Moon H., Rizvi S. A., et al.. The FERET evaluation methodology for face recognition algorithms [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22: 1090-1104
[71] Lyons M. J., Akamatsu S., Kamachi M., et al.. Coding facial expressions with Gabor wavelets [A]. Third IEEE International Conference on Automatic Face and Gesture Recognition [C]. Nara Japan: IEEE Computer Society, 1998, 200-205
[72] Yang M.-H., Kriegman D., Ahuja N.. Detecting Faces in Images: A Survey [J]. IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, 24(I): 34--58
[73] Moses Y., Adini Y., Ullman S.. Face recognition: the problem of compensating for changes in illumination direction [A]. ECCV-94 [C]. Berlin: Springer-Verlag, 1994: 286-296
[74] Adini Yael, Moses Yael, Ullman Shimon. Face Recognition: The Problem of Compensating for Changes in Illumination Ditection [J]. IEEE Transactions On PatternAnalysis And Machine Intelligence, 1997, 19(7):721-733
[75] Viola P., Jones M.. Robust Real-time Face Detection [A].IEEE ICCV Workshop on Statistical and Computational Theories of Vision [C]. Vancouver,Canada: 2001:7-14
[76] Schneiderman H., Kanade T.. A statistical method for 3D object detection applied to faces and cars [A]. Computer Vision and Pattern Recognition [C]. Hilton Head Island, USA, 2000: 746-751
[77] Li Y.,Gong S.,Liddell H.Support vector regression and classification based multi-view face detection and recognition [A]. Automatic Face and Gesture Recognition [C].Grenoble, France, 2000: 300-305
[78] Feraud R.,Bernier O.J.,Viallet J.-E., et al. A fast and accurate face detector based on neural networks [J].IEEE Trans.Pattern Analysis and Machine Intelligence, 2001, 23(1): 42-53
[79] Shih F. Y., Chuang C.. Automatic extraction of head and face boundaries and facial features [J]. Information Sciences, 2004, 158: 117-130
[80] YANG J., LU W., WAIBEL. A Skin-color modeling and adaptation [A] Third Asian Conference on Computer Vision [C]. London, UK: Springer-Verlag, 1998: 687-694
[81] Kakumanu P., Makrogiannis S., Bourbakis N.. A survey of skin-color modeling and detection methods [J]. Pattern Recognition, 2007, 40(3): 1106 - 1122
[82]赵丽红,孙晓琳,王宇飞,等.基于肤色的人脸检测[J].工程图学学报, 2005, 26(3): 84-88
[83] YANG J., WAIBEL A.. Real-time face tracker [A] The 3rd IEEE Workshop on Applications of Computer Vision [C]. Washington, USA: IEEE Computer Society, 1996: 142-147
[84]衣晓飞,陈福接.单人图像中人眼的快速定位[J].计算机工程与科学, 2001, 23(3): 16-17
[85] VIOLA P., JONES M.. Rapid object detection using a boosted cascade of simple features [A]. IEEE Conference on Computer Vision and Pattern Recognition [C]. Washington, USA: IEEE Computer Society, 2001: 511 - 518
[86] Pageorgiou C., Oren M., Poggio T.. A General Framework for Objeet Deteetion [A]. International Confereneeon Computer Vision [C]. Jnuar, 1998: 555-562
[87] Oren M., Pageorgiou C., Poggio T.. Example-based objeet detection in imges by eomponents [J]. IEEE Trans.on Pattern Analysis and Maehine Inielligenee, 23(4), 2001: 349-361
[88] Freund Y., Schapire R. E.. A decision-theoretic generalization of online learning and an application to boosting [A] The second european conference on computational learning theory[C]. London, UK: Springer-Verlag, 1995: 23-37
[89] Kearns M., Valiant L. G.. Cryptographic limitations on learning boolean formulae and finite automata [J]. Journal of the ACM, 1994, 41(1): 67-95
[90]边肇祺,张学工.模式识别[M]. 2版.北京:清华大学出版社, 2000
[91]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
[92] Campbell C. Algorithmic approaches to training support vector machines: a survey [A]. The 8th european symposium on artificial neural networks [C]. Bruges, Belgium: 2000: 27-36
[93] Vapnik V N.统计学习理论的本质[M].张学工.北京:清华大学出版社, 2000
[94] Vapnik V, Levin E, Le Cun Y. Measuring the VC-dimension of a learning machine [J]. Neural computation, 1994, 6: 851-876
[95] Burges C J C. A tutorial on support vector machines for pattern recognition [J]. Data mining and knowledge discovery, 1998, 2(2): 121-167
[96] Tipping M. The Relevance Vector Machine. Advances in Neural Information Processing System [J]. MIT Press, 2000, 12: 652-658
[97] Tipping M. E., Faul A. C.. Fast Marginal Likelihood Maximization for Sparse Bayesian Models [R]. http://www.miketipping.com/papers/met-fastsbl.pdf, 2003-01-06
[98] Yang Z. R.. A fast algorithm for relevance vector machine [A]. The 7th intelligent data engineering and automated learning [C]. Berlin Heidelberg: Springer-Verlag, 2006: 33-39
[99] Chan W., Chan C., Cheung K., et a1. On the modeling of nonlinear dynamic systems using support vector neural networks [J]. Engineering application of artificial intelligence, 2001, (14): 105-113
[100] Smola A., Scholkhopf B.. A tutorial on support vector regression [J]. Statistics and computing, 2004, (14): 199-222
[101] Muller K., Mika S., Ratsch G., et al. An introduction to kernel-based learning algorithms [J]. IEEE Trans.Neural Network, 2001, 12(2): 181-201
[102] Suykens J.. Support vector machines:a nonlinear modeling and control perspective [J]. Euro.J. Control, Special Issue on Fundamental Issues in Control, 2001, 7(2-3): 311-327
[103] Espinoza M., Suykens J., Moor B.. Kernel based partially linear models and nonlinear identification [J]. IEEE Transactions on Automatic Control, 2005, 50(10): 1602-1606
[104] Zhang H., Wang X., Zhang C., et al. Robust identification of non-linear dynamic systems using support vector machine [J]. IEE Proceedings-Science, Measurement and Technology, 2006, 153(3): 125-129
[105] Tipping M. Sparse bayesian learning and the relevance vector machine [J]. Journal of machine learning research, 2001, 1: 211-244
[106] N Aronszajn. Theory of reproducting kernels [J]. Transactions of the American mathematical society, 1950, 686, 337-404
[107] Chen S., Gurm S., Harris C.. The relevance vector machine technique for channel equalization application [J]. IEEE Trans.Neural Networks, 2001,12(6): 1529-1532
[108] Pierre Baldi, Soren Brunak.Bioinformatics: the machine learning approach [M]. 2. MIT Press, 2001
[109]王利民.贝叶斯学习理论中若干问题的研究[D].吉林:吉林大学, 2005
[110] Bishop C.M.. Pattem recognition and machine learning [M]. Springer, 2006
[111] Tipping M. Bayesian inference:An introduction to principles and practice in machine learning [J]. Advanced lectures on machine learning, 2004, 3176: 4l-62
[112]宫秀军.贝叶斯学习理论及其应用研究[D].中国科学院研究生院(计算技术研究所), 2002
[113]曲寒冰.贝叶斯框架下广义结合泛函网络与广义约束神经网络的研究与应用[D].中国科学院研究生院(中国科学院自动化所), 2007
[114] Lienhart R., Maydt J. An extended set of haar-like features for rapid object detection [A] International Conference on Image Processing [C]. Piscataway, USA: IEEE Press, 2002: 900-903.
[115] Jones M., Viola P.. Fast multi-view face detection [EB/OL]. [2003-08-16]. http://www.merl.com/reports/docs/TR2003-96.pdf.
[116] Chang C. C., Lin C. J.. LIBSVM -- A Library for Support Vector Machines[DB/OL].http://www.csie.ntu.edu.tw/~cjlin/libsvm/ 2009
[117]唱轶钲,山世光,高文,等. Gabor特征判别分析人脸识别方法的误配准鲁棒性分析[J].计算机工程与应用, 2005, 41(5): 56-59
[118] Gabor D.. Theory of communication [J]. Journal of institute for electrical engineering, 1946, 93: 429-457
[119] Daugman J.G.. 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, 1985,85(2): 1160-1169
[120] Daugman J.G.. Two-dimensional spectral analysis of cortical receptive field profiles [J]. Vision Research, 1980, 20(10): 847-856
[121] Kong W.K., Zhang D, Li W.. Palmprint feature extraction using 2-D Gabor filters [J]. Patern Recognition. 2003, 36: 2339-2347
[122] Su Y. M., Wang J.F.. A novel stroke extraction method for Chinese characters using Gabor filters [J]. Patern Recognition. 2003, 36: 635-647
[123] Marcelja S.. Mathematical description of the responses of simple cortical cells [J]. Journal of the Optical Society of America, 1980, 70(11): 1297-1300
[124] Daugman J. G., Two-dimensional spectral analysis of cortical receptive field profiles[J], Vision Research, 1980, 20(10), 847-856
[125] Liu C. J.. 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
[126]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[J].中国图象图形学报, 2007, 12(2): 322-329
[127] Lyons M., Budynek J., Akamastu S.. Automatic classification of single facial images [J]. IEEE Trans.pattemanalysisandmaehineIntelligenee, 1999, 21(12): 1357-1362
[128]焦峰,山世光,崔国勤,等.一种基于局部特征分析的人脸识别方法[J].计算机辅助设计与图形学学报. 2003, 15(1): 53-58
[129] LiuD H., Lam K.M., Shen L.S.. Optinal sampling of Gabor features for face recognition[J].Pattern recognition letters, 2004, 25(2): 267-276
[130]史金玉.人脸表情识别算法研究[D].重庆:重庆大学, 2009
[131]曹怀信,赵建伟.小波分析发展综述[J].成阳师范学院学报, 2002, 17(6): 5-7
[132]冉启华.小波变换与分数傅里叶变换理论及应用[M],哈尔滨工业大学出版社, 2001
[133]李晓华,沈兰荪,基于压缩域的图像检索技术[J],计算机学报, 2003, 26(9): 1051-1059
[134] Mandal I. K., Idris F., Panchanatha S.. A critical evaluation of image and video indexing techniques in the compressed domain [J]. Image and Vision Computing, 1999, 17: 513-529
[135]刘艳,李宏东. DCT域图象处理和特征提取技术[J].中国图象图形学报, 2003, 8(2): 121-128
[136] Ahmed N., Rao K. R.. Discrete Cosine Trransform [J]. IEEE Trans. on computers, 1974,23(1): 90-93
[137] Feng G. C., Jiang J.. Image extraction in DCT domain [A]. Image Signal Process [C]. 2003, 1(150): 20–27
[138]蒋斌,杨国胜,马晓燕. DCT和2DPCA降维与识别算法的比较研究[J].河南大学学报(自然科学版), 2008, 38(6):636-639
[139] David Salomon,数据压缩原理与应用[M],电子工业出版社, 2002
[140]范听炜.支持向量机算法的研究及其应用[D].杭州:浙江大学, 2003
[141]王晓辉.支持向量机在人脸识别中的应用[D].哈尔滨:哈尔滨工业大学, 2006
[142]应自炉,唐京海,李景文,等.支持向量鉴别分析在人脸表情识别中的应用[J].电子学报, 2008, 36(4): 725-730
[143] Hsu Chih-Wei, Lin Chih-Jen. A comparison of methods for multiclass support vector machines [J]. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425
[144]刘志刚,李德仁,秦前清,等.支持向量机在多类分类问题中的推广[J].计算机工程与应用, 2004, (7): 10-13
[145]黄勇,郑春颖,宋忠虎.多类支持向量机算法综述[J].计算机技术与自动化, 2005, 24(4): 61-63
[146] Hu Zhonghui, Cai Yunze, he Xing, et al. Fusion of multi-class support vector machines for fault diagnosis [J]. The American Control Conference. Portland, United States: 2005: 1941-1945
[147] Irene Kotsia, Ioannis 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
[148] Irene Kotsia, Nikolaos Nikolaidis, Ioannis Pitas. Facial Expression Recognition in Videos using a Novel Multi-Class Support Vector Machines Variant [J]. Acoustics, Speech and Signal Processing, 2007, 2: 585-588
[149]王志良,刘芳,王莉.基于计算机视觉的表情识别技术综述[J].计算机工程,2006, 32(11): 231-233
[150] Gwen Littlewort, Ian Fasel, Marian Stewart Bartlett, et al.. Fully Automatic Coding of Basic Expressions from video [R]. INC MPLab TR, 2002
[151]刘志刚,李德仁,秦前清,等.支持向量机在多类分类问题中的推广[J].计算机工程与应用, 2004, 40(7):10-13
[152] Krebel U.. Pairwise classification and support vector machines [A]. Advances in kernelMethods: Support Vector Learning [C]. MA: Cambridge, MIT press, 1999: 255-268
[153] Platt J. C., Cristianini N., Shawe-Taylor J.. Large margin DAGs for multi-class classification [A]. Advances in Neural Information Processing Systems [C], 2000, 12: 541-553
[154] Cheong S.. Support vector machines with Binary tree architecture for multi-class classification [J]. Neural Information Porcessing-Leters and Reviews, 2004, 2: 47-51
[155]孟媛媛,刘希玉.一种新的基于二叉树的SVM多类分类方法[J].计算应用, 2005, 25(11): 2653-2657
[156] Ghani R.. Using error-correcting codes for text classification [A]. International Conference on Machine Learning [C], 2002, 303--310
[157] Solbing F. S.. Multi-class pattern recognition problems with tree-structured support vector machines [A]. Pattern Recognition: 23rd DAGM Symposium [C], Munich, Germany, 2001:283-290
[158]于清,赵晖.一种2_a_2支持向量机多类分类新方法[J].计算机工程与应用. 2008, 44(25): 186-188
[159] Kanade T., Cohn J. F., Tian Y.. Comprehensive database for facial expression analysis [A]. Automatic Face and Gesture Recognition [C], Grenoble, France, 200: 46-53.
[160] A. Jain, R. Bolle, and S. Pankanti, Introduction to Biometrics [M], Kluwer Academic Publishers, 1999
[161] Jain A. K., Flynn P., Ross A.. Handbook of Biometrics [M], Springer, 2007
[162]李子青.人脸识别发展新趋势—“三化两合”[J].中国安防, 2008(7): 76-80
[163]田捷,杨鑫.生物特征识别技术理论与应用[M].北京:电子工业出版社, 2005
[164]孙瀚.基于指纹识别身份验证的数据采集系统设计与实现[D].电子科技大学, 2005
[165]曹国到,毛志红,梅园,等.一种快速的指纹参照点对选取方法研究[J].中国图象图形学报, 2009, 14(3): 401-407
[166] Prokoski F. J.. History, current status, and future of infrared identification [A]. IEEE Workshop on Computer Vision beyond Visible Spectrum : Methods and Applications[C], Hilton Head, SC, USA, 2000: 5
[167]李江.红外图像人脸识别方法研究[D].长沙:国防科学技术大学, 2005
[168]伍世虔,韦礼珍,方志军.红外人脸识别技术[J].中国图象图形学报, 2008, 13(12): 2260-2269
[169] Kong S. G., Heo J., Abidi B. R., et al. Recent advances in visual and infrared racerecognition--a review [J]. Computer Vision and Image Understanding, 2005, 97(1): 103-135
[170]陈倩.多生物特征融合身份识别研究[D].浙江:浙江大学, 2007
[171] BioID homepage [EB/OL]. http://www.bioid.com/
[172]张一鸣.人脸表情识别[D].辽宁:大连理工大学, 2006
[173] Zhao W., Chellappa R., Rosenfeld A., et al. Face Recognition: A Literature Survey [J]. ACM Computing Surveys, 2003:399-458
[174]刘党辉,沈兰荪, Kin-Man Lam.人脸识别研究进展[J].电路与系统学报, 2004, 9(1):85-94
[175] Turk M., Pentland A.. Eigenfaes for recognition [J]. Journal of Cognitive Neuroscience, 1991, 3(1):71-86
[176] Kirby M., Sirovieh L.. Application of the Karhunen-Loeve procedure for the characterizization of human faces [J]. IEEE Transactiontions on Pattern Analysis and Machcine Intelligence, 1991, 12(l): 103-108
[177] Belhumeur P. N., Hespanha J. P., Kriegman D. J.. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720
[178] Zheng W. S., Lai J. H., Yuen P. C.. A-fisher: a new LDA-based face recognition algorithm with selcction of principal components [J]. IEEE Transactions on Systems, Man and Cybernetics, 2005, 35(5):1065-1078
[179] Yank M.H., Ahuja N., Kriegman D.. Face recognition using kernel eigenfaces [A], International Conference on Image Processing[C], 2000:37-40
[180]刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法[J].自动化学报, 2003, 129(16): 901-911
[181] Lu J., Plataniotis K.N., Venetsanopoulos A.N.. Face rcoognition using kernel direct disaiminant analysis algorithms [J]. IEEE Transactions on Neural Networks, 2003, 14(1):117-126
[182] Yang J., Frangi A.F, Yang J.Y., et al. KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(2): 230-244
[183] T. Kohonen. Self-organization and associative memory[M]. Berlin: Springer, 1998
[184] Lahdenoja O., Laiho M., Paasio A.. Reducing the feature vector length in local binary pattern based face recognition [A]. Image Processing[C], Finland, 2005, 2: 914-917
[185] Rodriguez Y., Marcel S., Face authentication using adapted local binary pattern histograms[A], European Conference on Computer Vision, ser. Lecture Notes in Computer Science[C], Springer: 2006, 3954: 321-332
[186] Lee T., Image representation using 2-D Gabor wavelets [J]. IEEE Trans Pattern Analysis and Machine Intelligence , 1996, 18(10): 959-971
[187] Li B., Liu Y. H.. When eigenfaces are combined with wavelets [J]. Knowledge-based Systems, 2002, 15: 343-347
[188] Lai J. H., Yuen P. C., Feng G. C.. Face recognition using holistic Fourier invariant features [J]. Pattern Recognition, 2001, 34(1): 95-109
[189]王志良,孟秀艳.人工工程学[M].北京:机械工业出版社, 2008
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |