摘要
传统的基于数据二阶统计矩的主元分析法(PCA)是一种有效的数据特征提取方法,是基于原始特征的一种线性变换。但是,当原始数据中存在非线性属性时,用主元分析法后留下的显著成分就可能不再反映这种非线性属性。而核主元分析则是基于原始数据的高阶统计量,是一种非线性变换,在图像识别中它可以描述多个像素之间的相关性。而KPCA方法只考虑了人脸图像的整体信息,没有考虑到局部特征信息。文章提出了分块核主元分析(MKPCA)的方法进行人脸识别,取得了很好的效果。
Principal component analysis（PCA） as a method of feature extraction demonstrates their success in face recognition,detection and tracking.The representation in PCA is based on the second order statistics of the image set,and does not describe the nonlinear relationship among the pixels.While kernel principal component analysis（KPCA） based on the higher order statistics of the images set can address the higher statistical dependencies such as the relationship among three or more pixels.The KPCA method only considers the whole face image information,the information does not take into account the local features.In this paper,Modular KPCA（MKPCA） face recognition methods have achieved good results.
出处
《计算机与数字工程》
2012年第8期119-121,共3页
Computer & Digital Engineering
