关键词: 离散余弦变换（DCT）, 支持向量机, 最近邻分类器, 统计特征, 人脸识别

Abstract: A face recognition algorithm based on coefficient and statistical features of block DCT is proposed. The image is partitioned into a set of blocks, choosing the DCT coefficient of the low frequency part as its features. At the same time the DCT transform is performed on each block in order to decompose it into a low-pass filtered image and a reversed L-shape blocks containing the high frequency coefficients of the DCT; the statistical measures such as mean, variance, and entropy rate are then computed on the low-pass filtered image and a reversed L-shape blocks; SVM and the nearest neighbor classifier are selected to perform face classification. The experimental results on ORL and Yale face databases show that the algorithm based on statistical features of block DCT achieves high recognition rate.

Key words: Discrete Cosine Transform（DCT）, Support Vector Machine（SVM）, nearest neighbor classifier, statistical features, face recognition