关键词: 鲁棒人脸识别, 自适应直方图均衡化, 离散余弦变换, 系数重变换, 核主成分分析

Abstract: The performance of face recognition is seriously impacted by illumination, expression, posture and occlusion variations, for which low frequency Discrete Cosine Transform（DCT） coefficients retransforming based on Contrast Limiting Adaptive Histogram Equalization（CLAHE） is proposed. Original images are divided into some non-overlapping patches and CLAHE is used to do local contrast stretching so as to reduce noise. Illustration variation of face image is removed by reducing suit numbers of low frequency DCT coefficients. Kernel principle component analysis is used to extract features. Nearest neighbor classifier is used to finish classification and recognition. The effectiveness and reliability of proposed algorithm have been verified by experiments on ORL,extended YaleB and AR face database. Experimental results show that proposed algorithm has higher recognition accuracy than several advanced standardized technologies.

Key words: robust face recognition, adaptive histogram equalization, Discrete Cosine Transform（DCT）, coefficients retransforming, Kernel Principal Component Analysis（KPCA）