基于PCA与最大后验概率分类的人脸识别方法

计算机科学 ›› 2014, Vol. 41 ›› Issue (2): 91-94.

基于PCA与最大后验概率分类的人脸识别方法

袁少锋,王士同

江南大学数字媒体学院无锡214122;江南大学数字媒体学院无锡214122

出版日期:2018-11-14 发布日期:2018-11-14

Method of Face Recognition Based on Principal Component Analysis and Maximum a Posteriori Probability Classification

YUAN Shao-feng and WANG Shi-tong

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 在运用主成分分析进行人脸识别的过程中,由于实际图像可能符合某种概率密度分布,并且实际用到的图像可能受到不同程度的噪声污染,简单的距离分类已不再适用。基于核函数的最大后验概率分类是将概率密度函数估计中的参数估计、核函数以及贝叶斯理论结合起来,能很好地考虑到概率分布情况,用多元高斯分布下的基于核函数的最大后验概率分类取代距离分类,对于含有不同参数值的高斯噪声图像有较好的识别率。用ORL标准人脸库进行验证,实验结果表明了可行性。

关键词: 主成分分析,多元高斯分布,参数估计,核函数,贝叶斯理论中图法分类号TP391.41文献标识码A

Abstract: In the processing of face recognition with PCA algorithm,the image may be eligible for some kind of probability density distribution and different levels of noise pollution,so the simple distance classification is no longer effective．Maximum posteriori classification combines the parameter estimation and kernal function and Bayes theory,can take into account the probability distribution well.Under the multivariate Gaussian distribution,using it to replace the distance classification can have the better recognition rate for the images containing the different parameter values of the Gaussian noise．The standard ORL face library was used to verify this theroy,and the result shows its feasibility.

Key words: Principal component analysis,Multivariate gaussian distribution,Parameter estimation,Kernel function,Bayesian theory

袁少锋,王士同. 基于PCA与最大后验概率分类的人脸识别方法[J]. 计算机科学, 2014, 41(2): 91-94. https://doi.org/

YUAN Shao-feng and WANG Shi-tong. Method of Face Recognition Based on Principal Component Analysis and Maximum a Posteriori Probability Classification[J]. Computer Science, 2014, 41(2): 91-94. https://doi.org/

参考文献

[1] Turk M,Pentland A．Eigenfaces for recognition[J].Journal of Cognitive Neuroscience,1993,3(1):71-86
[2] Tan Ke-ren,Chen Song-can.Adaptively weighted sub-patternPCA for face recognition[J]．Neurocomputing,2005,4(3):505-511
[3] Jain A K,Robert P W,et al.Statistical Pattern Recognition:A Review [J].IEEE Transactions on pattern analysis and machine intelligence,2000,2(1):4-37
[4] He Kun,Luan Xin-cheng,et al.Gaussian Noise Removal of Ima-ge on the Local Feature[J]．Intelligent Information Technology Application,2008,3(1):867-871
[5] Xu Zeng-lin,Huang Kai-zhu,Zhu Jian-ke,et al．A novel kernel-based maximum a posteriori classification method [J].Neural Networks,2009,2(7):977-987
[6] 张俭鸽,刘洪波．WTPCA和三阶近邻的人脸识别算法仿真[J]．计算机工程与应用,2009,45(11):175-177
[7] Motai Y,Yoshida h.Principal Composite Kernel Feature Analysis:Data-Dependent Kernel Approach[J].IEEE Transactions on Knowledge and Date Engineering,2012(99):1-13
[8] 茆诗松,程依明,濮晓龙．概率论与数理统计教程[M]．北京:高等教育出版社,2004:287-290
[9] Lukic A S,Wernick M N．Bayesian Kernel Methods for Analysis of Functional Neuroimages[J].IEEE Transactions on Medical Imaging,2007,6(12):1613-1624

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