基于核主成分分析的人脸识别

核主成分分析方法是主成分分析的改进算法,其采用非线性方法提取主成分.把核主成分分析应用到人脸识别中,利用核主成分分析方法选择合适的核函数在高维空间提取人脸图像的主成分.核主成分分析与传统主成分分析相比,可以得到更好的适合分类的特征.基于ORL人脸库,识别核主成分分析提取出的主成分的相关性系数.实验结果表明,核主成分分析不仅实现了降维,而且能取得比传统主成分分析更好的识别性能,正确识别率为92.5%.

Face Recognition Based on Kernel PCA

KPCA extracting principal component with nonlinear method is an improved conventional PCA.The Kernel Principal Component Analysis(KPCA),is used in face recognition,which can make full use of the high correlation between different face images for feature extraction by selecting the proper kernel function.So KPCA can extract the feature set more suitable in categorization than classical conventional PCA.Based on ORL face database,recognizes correlation coefficients of principal component extracted by KPCA.Experimental results demonstrate that KPCA is not only good at dimensional reduction,but available to get better performance than conventional PCA,of which the correct recognition rate is up to 92.5%.