关键词: Curvelet变换, 分块熵, 自适应特征提取, 鉴别能力分析

Abstract: An adaptive local entropy feature extraction method based on the multi-resolution Curvelet transform is proposed for face recognition according to illumination. It compensates uneven illumination and makes local details notable through local contrast enhancement. Then the enhanced images are fed into the Curvelet transform. The block entropy of Curvelet coefficients consists of the candidate feature vectors. Feature discrimination power analysis and evaluations can adaptively select the most important features among all candidate features. Experimental results in ORL, Yale, YaleB, and AR face databases, show that the proposed method avoids suffering from the small sample size problem and SVD（Singular Value Decomposition） problem such as PCA and LDA. Meanwhile, it has light resistance and environmental adaptability.

Key words: Curvelet transform, block entropy, adaptive feature extraction, discrimination power analysis