关键词: 人脸识别, 特征提取, 核主成分分析, 离散余弦变换, 最近邻分类器

Abstract: As the nonlinear extensions of Principal Component Analysis（PCA）, Kernel Principal Component Analysis（KPCA） is effective for face recognition. In order to reduce recognition time, a face recognition method based on Discrete Cosine Transform（DCT） and KPCA is presented. The feature coefficients are extracted by DCT, and part of the coefficients are chosen to reconstruct face images. The face feature of high dimention is extracted by KPCA. The nearest neighbor classifier is used for identification. The experiment result on ORL face databases shows this method has the property of being faster, and the comprehensive performance is better than that of KPCA.

Key words: face recognition, feature extract, Kernel Principle Component Analysis（KPCA）, Discrete Cosine Transform（DCT）, nearest neighbor classifier