一种加权核主成分分析及其相关参数的选取
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摘要
随着社会的发展,各个方面对快速有效的自动身份验证的要求日益迫切。由于生物特征是人的内在属性,具有很强的自身稳定性和个体差异性,因此它已经成为身份验证的理想依据。这其中,利用人脸特征又是最自然直接的手段,相比其他生物特征,它具有直接、友好、方便的特点,易于为用户接受。
人脸识别是一个涉及面广且又很有挑战性的研究课题,近年来关于人脸识别的研究取得了较大的进展。而特征提取是人脸识别过程的关键。主成分分析方法是公认的特征提取的经典工具之一,核主成分分析方法作为主成分分析方法的非线性拓展,近些年也被广泛应用于人脸识别中。可是主成分分析和核主成分分析在进行特征提取时将人脸图像的各维特征平等对待,而实际上,人脸的不同特征在识别过程中所起的作用是不相同的,比如眼睛、鼻子、嘴巴这些含有丰富纹理和结构的特征在识别中会比相对平滑和缺少灰度变化的脸颊、额头等部分起的作用要大,因此对每个特征赋予相应的权重,然后再通过主成分分析或核主成分分析方法进行特征提取,将会明显提高人脸识别的效果。本文提出了一种新的加权核主成分分析方法,该方法采用一种高斯分布函数作为加权函数,突出对识别起关键作用的特征,然后与核主成分分析方法相结合来进行人脸识别。我们在标准的ORL人脸图像数据库上对提出的方法进行了实验,结果表明该方法的有效性。
在人脸识别方法中,涉及核函数的选择、核函数参数的选择、训练样本的选择、分类器的选择等诸多关键环节.虽然许多学者对这些问题进行了探讨,但是至今没有一种好的方法能够有效的指导如何选择最优的参数,目前大多是在特定的应用领域内,通过实验来指定相应的参数。因此本文通过改变加权函数参数以及核函数参数,得到相关参数的最佳范围,使识别率达到最好。
As the development of the society, there are increasing demands in automatic identity check. Since some biological characteristics are intrinsic and stable to people and strongly different from one to the others, they can be used as features for identity check. Among all the characteristics of human, the characteristics of face are the most direct tools which are friendly and convenient and can easily be accepted by the customers.
Face recognition is an extensive and challenging research topic. Recently, significant progresses have been made in the technology of the face recognition. And feature extraction is the critical stages in the face recognition. Principal Component Analysis is acknowledged one of the most powerful techniques for feature extraction. As a nonlinear form of Principal Component Analysis, Kernel Principal Component Analysis has been broad applied to face recognition in recent years. However, each dimension feature of face images is treated equally by the principal component analysis and kernel principal component analysis in feature extraction. But in fact, different features play different roles in face recognition. For example, the eyes and mouth play far greater importance role than cheeks, the former part of face is greater importance than the lower part of face, with special features of face such as mouth askew, cross-eye, and so on, to be identified more easily. In this paper, we adopt Gaussian distribution function as a weighted function, which can give prominence to the key features in face recognition. The proposed method is combined with the kernel principal component analysis to calculate the weighted subspace. Experimental results on the normal ORL face database show the proposed method is effective.
Face recognition relates to these critical steps that are options of kernel function, parameter of kernel function, train sample and classifier. Many researchers probe to these problems, but no better methods can instruct how choose optimization parameter. At present, many methods confine given applied field via experiment specifies relevant parameter. So this article receives the best range of relative parameters via changing parameters of weighted kernel function and kernel function. And obtaining the best rate of identification is its object.
人脸识别是一个涉及面广且又很有挑战性的研究课题,近年来关于人脸识别的研究取得了较大的进展。而特征提取是人脸识别过程的关键。主成分分析方法是公认的特征提取的经典工具之一,核主成分分析方法作为主成分分析方法的非线性拓展,近些年也被广泛应用于人脸识别中。可是主成分分析和核主成分分析在进行特征提取时将人脸图像的各维特征平等对待,而实际上,人脸的不同特征在识别过程中所起的作用是不相同的,比如眼睛、鼻子、嘴巴这些含有丰富纹理和结构的特征在识别中会比相对平滑和缺少灰度变化的脸颊、额头等部分起的作用要大,因此对每个特征赋予相应的权重,然后再通过主成分分析或核主成分分析方法进行特征提取,将会明显提高人脸识别的效果。本文提出了一种新的加权核主成分分析方法,该方法采用一种高斯分布函数作为加权函数,突出对识别起关键作用的特征,然后与核主成分分析方法相结合来进行人脸识别。我们在标准的ORL人脸图像数据库上对提出的方法进行了实验,结果表明该方法的有效性。
在人脸识别方法中,涉及核函数的选择、核函数参数的选择、训练样本的选择、分类器的选择等诸多关键环节.虽然许多学者对这些问题进行了探讨,但是至今没有一种好的方法能够有效的指导如何选择最优的参数,目前大多是在特定的应用领域内,通过实验来指定相应的参数。因此本文通过改变加权函数参数以及核函数参数,得到相关参数的最佳范围,使识别率达到最好。
As the development of the society, there are increasing demands in automatic identity check. Since some biological characteristics are intrinsic and stable to people and strongly different from one to the others, they can be used as features for identity check. Among all the characteristics of human, the characteristics of face are the most direct tools which are friendly and convenient and can easily be accepted by the customers.
Face recognition is an extensive and challenging research topic. Recently, significant progresses have been made in the technology of the face recognition. And feature extraction is the critical stages in the face recognition. Principal Component Analysis is acknowledged one of the most powerful techniques for feature extraction. As a nonlinear form of Principal Component Analysis, Kernel Principal Component Analysis has been broad applied to face recognition in recent years. However, each dimension feature of face images is treated equally by the principal component analysis and kernel principal component analysis in feature extraction. But in fact, different features play different roles in face recognition. For example, the eyes and mouth play far greater importance role than cheeks, the former part of face is greater importance than the lower part of face, with special features of face such as mouth askew, cross-eye, and so on, to be identified more easily. In this paper, we adopt Gaussian distribution function as a weighted function, which can give prominence to the key features in face recognition. The proposed method is combined with the kernel principal component analysis to calculate the weighted subspace. Experimental results on the normal ORL face database show the proposed method is effective.
Face recognition relates to these critical steps that are options of kernel function, parameter of kernel function, train sample and classifier. Many researchers probe to these problems, but no better methods can instruct how choose optimization parameter. At present, many methods confine given applied field via experiment specifies relevant parameter. So this article receives the best range of relative parameters via changing parameters of weighted kernel function and kernel function. And obtaining the best rate of identification is its object.
引文
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[4]刘洁.人脸识别技术综述.福建电脑.2007(6):33-34.
[5]艾英山,张德贤.人脸识别方法的综述与展望.计算机与数字工程.2005,33(10):24-27.
[6]刘丽华.人脸识别方法综述.云南师范大学学报.2005,25(6):13-18.
[7]李乐.人脸识别方法研究.天津大学硕士学位论文.2005,2.
[8]R.Brunelli,T.Poggio.Race Recognition:Features versus Templates.IEEE Trans.On Pattern Analysis and Machine Intelligence.1993,15(10):1024-1052.
[9]N.Roder,X.Li.Accuracy Analysis for Facial Feature Detection.Pattern Recognition.1996,29(1):143-157.
[10]B.Moghaddam,A.Pentlan.Probabilistic visual learning for object representation.IEEE Trans.On Pattern Analysis and Machine Intelligence.1997,19(7):696-710.
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[19]彭辉,张长水,荣钢,等.基于K-L变换的人脸自动识别方法[J].清华大学学报(自然科学版).1997,(03):67-70.
[20]M.A.Turk,A.P.Pentland."Eigenfaces for recognition".Journal of Cognitive Neuroscience.vol.3.no.1.1991,pp:71-86.
[21]P.N.Belhumeur,J.P.Hespanha,D.J.Kriegman."Eigenfaces vs.Fisherfaces:recognition using class specific linear projection".IEEE Transactions on Pattern Analysis and Machine Intelligence.vol.19.no.7.1997,pp:711-720.
[22]J R O Duda,P E Hart,D G Stork.Pattern Classification[M].John Wiley & Sons.2nd Edition.2001.
[23]Bal.Scholkopf,S.Mika,Chris J.C.Burges,et al.Input Space Versus Feature Space in Kernel-Based Methods[J].IEEE Transactions on Neural Networks.1999,10(5):1000-1017.
[24]K.I.Kim,S.H.Park,H.J.Kim.Kernel Principal Component Analysis for Texture Classification.IEEE signal processing letters.2001,8(2).
[25]S.Mika,G.R(a|¨)tsch,J.Weston,et al.Fisher discriminant analysis with kernels.In Neural Networks for Signal Processing IX,.Y.-H.Hu et al.Eds.New York:IEEE,1999,pp:41-48.
[26]M.H.Yang."Kernel eigenfaces vs.Kernel fisherfaces:Face Recognition using kernel methods".In Proc.5th Int.Conf.Automat.Face Gesture Recognition,Washington,DC,May,2002,pp:215-220.
[27]Kwang In Kim,Keechul Jung,Hang Joon Kim.Face Recognition Using Kernel Principal Component Analysis.IEEE signal processing letters,9(2),2002.
[28]赵丽红,孙宇柯,蔡玉,徐心和.基于核主成分分析分析人脸识别.沈阳:东北大学学报,2006.8.
[29]Li-Hong Zhao,Xi-Li Zhang,Xin-He Xu.Face Recognition Base on KPCA with Polynomial Kernels.Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern.
[30]A.Kocsor,L.T(?)th,D.Paczolay,et al."A nonlinearized discriminant analysis and its application to speech impediment therapy",in Proc.Text,Speech Dialogue,vol.2166.V.Matousek et al.Eds.2001,pp:249-257.Recognition,Beijing,China,2-4 Nov.2007:1213-1216.
[31]Andr(?)s Kocsor and L(?)szl(?) T(?)th,Associate Member.Kernel-Based Feature Extraction with a Speech Technology Application.IEEE TRANSACTIONS ON SIGNAL PROCESSING.VOL.52.NO.8.AUGUST 2004:2250-2263.
[32]R.Chellappa,C.L.Wilson,S.Sirohey.Human and Machine Recognition of Faces:A Survey.Proceedings of the IEEE.1995,83(5):705-740.
[33]Fei Wang,Jingdong Wang,Changshui Zhang.Spectral Feature Analysis Proceedings of International Joint Conference on Neural Networks.Montreal.Canada.July 31- August 4,2005:1971-1976.
[34]乔羽,黄席樾,柴毅等.基于加权主元分析(WPCA)的人脸识别[J].重庆大学学报.2004,27(3):28-31.
[35]韩柯,冯荃,朱秀昌.基于二维加权主元分析的人脸识别研究[J].信息技术与信息化.2006,18(5):82-84.
[36]白小曼,盛杰成.用于人脸识别系统的一种新PCA算法[J].电视技术应用与工程.2006,26(3):88-90.
[37]Mika Sato-Ilic.Weighted Principal Component Analysis for Interval-Valued Data based on Fuzzy Clustering.Institute of Policy and Planning Sciences University of Tsukuba,Tsukuba.Japan,2003,4476-4482.
[38]Hui-Yuan Wang,Xiao-Juan Wu,Weighted PCA Space and its Application in Face Recognition.Proceedings of the Fourth International Conference on Machine Learning and Cybernetics,Guangzhou.18-21 August 2005,4522-4527.
[39]Bai Xiaoman,Ruan Qiuqi.An improved WPCA plus LDA.ICSP 2006 Proceedings.
[40]Nan Liu,Han Wang.Feature Extraction with Genetic Algorithms Based Nonlinear Principal Component Analysis for Face Recognition.The 18th International Conference on Pattern Recognition(ICPR'06),IEEE.2006.
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