人脸识别技术研究
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摘要
人脸识别是一个具有很高理论和应用价值的研究课题。人脸是人类视觉中最为普遍的模式,它所反映的视觉信息在人与人的交流和交往中有着及其重要的作用意义。人脸的特殊性,使得人脸识别技术成为最具潜力的身份识别方式。人脸识别技术应用广泛,并且日益受到人们的广泛关注并成为模式识别领域研究的热点。同时人脸识别又是一个复杂和困难的课题,其原因有:人脸是由复杂的三维曲面构成的可变形体,难以用数学描述;所有的人脸结构高度相似,而人脸图像又易受年龄和成像条件的影响。人脸识别涉及的技术很多,其中关键的是特征提取和分类方法,本文以此为重点进行了相关研究,主要内容如下:
人脸识别中,人脸图像的信息不仅存在于象素间的二维统计特性中,更存在于象素间的高阶统计特性中。传统的基于主分量分析(Principle Component Analysis,PCA)的人脸特征提取方法,只能得到人脸图像象素间的二阶统计信息,以此为基础的特征脸法,易受光照条件等易变因素的影响。本文将独立分量分析(Independent Component Analysis,ICA)作为人脸特征提取方法,同时用分类能力作为特征选择的依据,所提取的特征分类能力强、相互独立,并且对象素间高阶统计特性敏感,不易受光照变化的影响。因此基于ICA的人脸识别方法的识别性能优于特征脸法。传统的ICA算法(Informax算法)存在迭代次数多,难收敛的不足,并且需要人工设定步长来调整学习速度。本文采用FastICA作为ICA的快速算法,该算法无需人工参与,迭代次数少。本文还将FastICA的关键迭代步骤加以改进,减少了耗时的雅可比矩阵求逆的运算次数,进一步提高了收敛速度。
为了更好的综合人脸特征进行分类识别,本文将隐马尔可夫模型(Hidden Markov Models,HMM)引入到人脸建模中。一维HMM(1D-HMM)表现二维人脸图像存在不足,但训练识别比较简单;伪二维HMM(P2D-HMM)可以较精确描述二维人脸图像的统计特性,但结构复杂、运算量大。综合考虑二者的优缺点,结合支持向量机(SVM)对静态数据识别效率明显的长处,本文建立了基于ICA特征的SVM和HMM的混合人脸识别模型。实验结果表明该混合模型结构简单,运算量小,并且获得了与P2D—HMM相当的识别效果。
本文的主要工作包括以下几个方面:1.在人脸图像特征提取方面,提出了一种有效的基于ICA的人脸整体特征提取方法;2.在优化ICA算法方面,提出了一种改进的FastICA算法,该算法通过减少耗时的雅可比矩阵求逆的次数,进一步加快了收敛速度;3.建立了SVM/HMM的混合人脸模型。
Face recognition has very large academic and practical values. In daily life, people knowing each other uses at most of person's face. Face is the most familiar model in human vision. The visual information reflected by face has important meaning and impact between people's intercommunion and intercourse. Because of its extensive and applied realm, face recognition technique has got the extensive concern with study in near three decades and become the most potential method of identity recognition. At the same time, it is difficult to implement face recognition using computers. First, human face is a deformable object composed of complex 3D curve surfaces, which is hard to be represented in form of mathematics. Secondly, faces of different persons have the similar structure, and the face images are greatly dependent on ages and photography conditions. This paper mainly study face extraction and class method, which concept can be summarized as follows.In this paper, Independent Component Analysis (ICA) is presented as an efficient face feature extraction method. In a task such as face recognition, important information may be contained in the high-order relationship among pixels. ICA is sensitive to high-order statistic in the data and finds not-necessarily orthogonal bases, so it may better identify and reconstruct high-dimensional face image data than Principle Component Analysis (PCA). Conventional ICA algorithms, such as Informax algorithm, are time-consuming and sometimes converge difficultly. Informax algorithm need people adjust learning speed. In this paper, a modified FastICA algorithm is developed, which only need to compute Jacobian Matrix one time in many iterations and achieves the corresponding recognition effect of FastICA. Class descriminability select optimal independent components (ICs). The experiment results show that modified FastICA algorithm quickens convergence and ICs selection optimizes recognition performance. ICA based features extraction method is robust to variations and promising for face recognition.In order to better integrate face features for efficient classification, Hidden Markov Models (HMM) are used as to construct face models in this paper. Because HMM can keep the states unchanged for a given range of the change of observation vector and HMM use face similar construction. By analysis of HMM structure, tranditional 1D Hidden Markov Models (1D-HMM) has strongpoint of simple structure, Pseudo 2D HMM (P2D-HMM) is able to better model 2D data such as face images, due to its pseudo 2D structure. Combining these two models' defects and merits, an integrated model using Support Vector Machines (SVM) and HMM was proposed. Through Independent Component Analysis (ICA), some face area features are extracted. These feature vectors are as the inputs of SVM. SVM/HMM face recognition method achieved corresponding recognition performance, compared with P2D-HMM method, and experiment results show that SVM/HMM model has a simpler structure and lower computing complexity.Because face image is liable to impact of varieties and face is nonrigid and similar. Accurate face recognition is still difficult. There is still lone distance between face recognition and practicality. The progress of computer technology, pattern recognition, human intelligent and biologic psychology, vision mechanism surely promote face recognition develop.
人脸识别中,人脸图像的信息不仅存在于象素间的二维统计特性中,更存在于象素间的高阶统计特性中。传统的基于主分量分析(Principle Component Analysis,PCA)的人脸特征提取方法,只能得到人脸图像象素间的二阶统计信息,以此为基础的特征脸法,易受光照条件等易变因素的影响。本文将独立分量分析(Independent Component Analysis,ICA)作为人脸特征提取方法,同时用分类能力作为特征选择的依据,所提取的特征分类能力强、相互独立,并且对象素间高阶统计特性敏感,不易受光照变化的影响。因此基于ICA的人脸识别方法的识别性能优于特征脸法。传统的ICA算法(Informax算法)存在迭代次数多,难收敛的不足,并且需要人工设定步长来调整学习速度。本文采用FastICA作为ICA的快速算法,该算法无需人工参与,迭代次数少。本文还将FastICA的关键迭代步骤加以改进,减少了耗时的雅可比矩阵求逆的运算次数,进一步提高了收敛速度。
为了更好的综合人脸特征进行分类识别,本文将隐马尔可夫模型(Hidden Markov Models,HMM)引入到人脸建模中。一维HMM(1D-HMM)表现二维人脸图像存在不足,但训练识别比较简单;伪二维HMM(P2D-HMM)可以较精确描述二维人脸图像的统计特性,但结构复杂、运算量大。综合考虑二者的优缺点,结合支持向量机(SVM)对静态数据识别效率明显的长处,本文建立了基于ICA特征的SVM和HMM的混合人脸识别模型。实验结果表明该混合模型结构简单,运算量小,并且获得了与P2D—HMM相当的识别效果。
本文的主要工作包括以下几个方面:1.在人脸图像特征提取方面,提出了一种有效的基于ICA的人脸整体特征提取方法;2.在优化ICA算法方面,提出了一种改进的FastICA算法,该算法通过减少耗时的雅可比矩阵求逆的次数,进一步加快了收敛速度;3.建立了SVM/HMM的混合人脸模型。
Face recognition has very large academic and practical values. In daily life, people knowing each other uses at most of person's face. Face is the most familiar model in human vision. The visual information reflected by face has important meaning and impact between people's intercommunion and intercourse. Because of its extensive and applied realm, face recognition technique has got the extensive concern with study in near three decades and become the most potential method of identity recognition. At the same time, it is difficult to implement face recognition using computers. First, human face is a deformable object composed of complex 3D curve surfaces, which is hard to be represented in form of mathematics. Secondly, faces of different persons have the similar structure, and the face images are greatly dependent on ages and photography conditions. This paper mainly study face extraction and class method, which concept can be summarized as follows.In this paper, Independent Component Analysis (ICA) is presented as an efficient face feature extraction method. In a task such as face recognition, important information may be contained in the high-order relationship among pixels. ICA is sensitive to high-order statistic in the data and finds not-necessarily orthogonal bases, so it may better identify and reconstruct high-dimensional face image data than Principle Component Analysis (PCA). Conventional ICA algorithms, such as Informax algorithm, are time-consuming and sometimes converge difficultly. Informax algorithm need people adjust learning speed. In this paper, a modified FastICA algorithm is developed, which only need to compute Jacobian Matrix one time in many iterations and achieves the corresponding recognition effect of FastICA. Class descriminability select optimal independent components (ICs). The experiment results show that modified FastICA algorithm quickens convergence and ICs selection optimizes recognition performance. ICA based features extraction method is robust to variations and promising for face recognition.In order to better integrate face features for efficient classification, Hidden Markov Models (HMM) are used as to construct face models in this paper. Because HMM can keep the states unchanged for a given range of the change of observation vector and HMM use face similar construction. By analysis of HMM structure, tranditional 1D Hidden Markov Models (1D-HMM) has strongpoint of simple structure, Pseudo 2D HMM (P2D-HMM) is able to better model 2D data such as face images, due to its pseudo 2D structure. Combining these two models' defects and merits, an integrated model using Support Vector Machines (SVM) and HMM was proposed. Through Independent Component Analysis (ICA), some face area features are extracted. These feature vectors are as the inputs of SVM. SVM/HMM face recognition method achieved corresponding recognition performance, compared with P2D-HMM method, and experiment results show that SVM/HMM model has a simpler structure and lower computing complexity.Because face image is liable to impact of varieties and face is nonrigid and similar. Accurate face recognition is still difficult. There is still lone distance between face recognition and practicality. The progress of computer technology, pattern recognition, human intelligent and biologic psychology, vision mechanism surely promote face recognition develop.
引文
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[2] Allen A L. Personal Descriptions. London:Butterworth, 1950
[3] Parker F I. Computer Generated Animation of Faces, Proceedings ACM Conference, 1972(1):451-457
[4] Goldstion R J, Harmon L D, Lesk A B. Man-manchine interaction in human face identification. Bell Syst. Tech. Journal, 1972(51): 399-427
[5] Kaya Y, KobayashiK. A Basic Study on Human Recognitioh. In Frontiers of Pattern Recognition, New York: Academic, 1971,265- 289
[6] Kanad T. Picture processing system by computerand recognition of human face, PhD. Dissertationl.Kyoto: Kyoto University, 1973
[7] Samal A and Iyengar P A: Automatic recognition and analysis of human faces and facial expression: a survey, Pattern Recognition, 1992,25(1): 65-77
[8] Turk M and Pentland A.: Eigenfaces for recognition. J. Cognitive Neuroscience. 1991,3(1): 71-86
[9] 彭辉,张长水等.基于K-L变换的人脸自动识别方法.清华大学学报(自然科学版),1997,37(3):67-70
[10] Juell P, Marsh R. A Hierarchical neural network for human face detection. Pattern Recognition,1996, 29(5): 781-787
[11] Intrator N el al. Face recognition using a hybrid supervised/unsupervised neural network, Patter Recognition Letters, 1996, 17(1): 67-76
[12] Manjunath B S, Chellappa R.: A feature based approach to face recognition. Proc. IEEE Compoter Soc. Conf. on CVPR, 1992, 373-378
[13] 高西奇,周烘祥,何振亚,基于小波变换的主元分析人脸图像识别.东南大学学报,1996,26(2):137-141
[14] Adini Y., Mosed Y., Ullman S. Face recognition: The problem of compensting for changes in illumination direction. IEEE Trans. Pattern Anal. Machine Intell., 1997, 19(7): 312-317
[15] Lam K M and Yan H: An analytic-holistic approach for face recognition based on a single frontal view, IEEE Trans. Pattern Anal. Machine Intell., 1998,20(7):673-687
[16] Wishkott L, et al.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Machine Intell., 1997, 19(7): 412-416
[17] Baron R J.: Mechanisms of human facial recognition. Int J Man Manchine Studies, 1981(15): 137-178
[18] Samal A, Iyengar P A. Automatic recognition and analysis for human face and facial expressing: A survey. Pattern Recognition, 1992(25): 65- 77
[19] Baron R. Menchanisms of human facial recognition. Int. J. Man-machine Studies,1989(2): 283-310
[20] Bruce V. Recognizing faces. London: Erlbaum, 1988
[21] Bichsel M. Pereceiving and recognizing faces, Mind and Language, 1990: 342-364
[22] Ellis H et al. Aspects of face processing, Dordrecht: Nijhoff, 1986(3): 456-462
[23] 金忠.人脸图像特征提取与维数研究,[博士学位论文],南京:南京理工大学,1999
[24] Brunelli R and Poggio T: HyperBF networks for gender classification. Proc. DRRPA, Image Understanding, Workshop, 1992:311-314
[25] Ming-Hsuan Yang, David J. Kriegman, and Narendra Ahuja, Detecting Faces in Images: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, January 2002, 24(1):34-58
[26] Chellappa R., Wilson C.L. Sirohey S., Human and machine recognition of face: A survey, Proc IEEE, 1995, 83(5): 705-740
[27] Berto R, Poggio T, Face recognition: Feature versus templates. IEEE Trans. On PAMI, 1993, 15(10), 1042-1052
[28] Ziquan Hong. Algebraix feature extraction of image for recognition. Pattern Recognition, 1991, 24(3): 211-219
[29] Nakamura O, Mathur S, Minami T, Indentification of human faces based on isodensity maps. Pattern Recognition, 1991,24(3): 263-272
[30] Lades M, Vorbuggen J, Buhmann J et al, Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. on Computers, 1991, 42(3): 300-311
[31] Samaria F, Young S, HMM-based architecture for face indentification. Image and Vision Computing, 1994, 12(8): 123-129
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