基于PCA与最大后验概率分类的人脸识别方法  

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

作　　者：袁少锋[1] 王士同[1] 

机构地区：[1]江南大学数字媒体学院,无锡214122

出　　处：《计算机科学》2014年第2期91-94,共4页Computer Science

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。In the processing of face recognition with PCA algorithm, the image may be eligible for some kind of proba- bility density distribution and different levels of noise pollution, so the simple distance classification is no longer effec- tive. 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 ORE face library was used to verify this theroy, and the result shows its feasibility.

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 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]