由主成分分析(PCA)可知任何一幅人脸图像都可以通过一组特征脸的线性加权来重构,PCA是最小均方误差意义下图像的最优表示,但是传统的PCA最终只通过比较加权系数的欧氏距离来进行识别,没有考虑残差。因此,提出非相似尺度的概念,将两个样本同时投影到相同向量上,在确定它们关系时既考虑投影系数,也考虑重构所产生的残差。两者的投影系数和残差相差越大,说明这两个样本越不相似。和保局投影(LPP)有所不同,非相似度保持投影算法不必预先设定近邻个数,它是利用非相似度的概念,创建非相似度散布矩阵,最终通过最大化目标函数获取最优子空间。在AR库和Feret库上的实验结果证明了该方法的有效性。

We know that Principal Components Analysis(PCA) can represent each face image in terms of a linear combination of the eigenface, we also know that the PCA algorithm gives the best representation of images under the sense of minimum mean square error. However,PCA only compares the Euclidean distance between projection coefficients of samples and ignores the residue between the original sample and its reconstructed one. Therefore a new concept called dissimilarity distance metric is proposed in this paper. We project the two images into the same subspace and then characterize the similarity between pairs of samples by comparing to both the projecting coefficients and the approximation errors simultaneously. The higher is the value,the more dissimilar are the two samples. Different from Locality Preserving Projections,a new method,called Dissimilarity Preserving Projections,uses the concept of the dissimilarity above,and constructs the dissimilarity scatter matrix. This algorithm does not have to pre-set the number of neighbors,finally it gets the optimal projection subspace by maximizing the Objective function. The experimental results on AR and FERET face image database demonstrate the effectiveness of the proposed method.