一种局部保留C2DPCA人脸特征提取方法

 首先分析了主成分分析(PCA)、二维主成分分析(2DPCA)以及完全二维主成分分析(C2DPCA)存在的不足，针对PCA方法不能解决的小样本问题以及2DPCA和C2DPCA存在对所有识别信息都采用同等对待的不足，提出了局部保留的C2DPCA方法，此方法首先将人脸图像划分为5个区域，并对双眼、嘴唇和轮廓进行保留，其它部分采用降低其散列度的方式进行处理，然后再采用C2DPCA方法进行数据降维和特征提取，经过在ORL人脸数据库上实验研究表明，与C2DPCA相比在进一步降低了特征矩阵的维数的基础上又提高了识别率，并且在识别率方面优于经典的Fisherfaces和ICA方法.

A locality preserving C2DPCA facial feature extraction method

This paper first analyzes the Principal Component Analysis (PCA),2-Dimensional PCA (2DPCA) and Complete 2D PCA (C2DPCA) shortcomings,for the PCA method can not solve the problem of small sample,2DPCA and C2DPCA treat equally to all identifying information,To solve the problem,this paper propose a locality preserving C2DPCA method.Firstly,the face image is divided into five regions,To preserve eyes,lips and contour.reduce the degree of scatter to other region,then use C2DPCA method to dimensionality reduction and feature extraction,it shown that this method reduce the dimension of feature matrix and improve recognition rate through experiment by ORL face database compared with C2DPCA.and the recognition rate is superior to classic Fisherfaces and ICA methods.