有效用于人脸识别的光照不变特征表示算法

在光照变化环境下，人脸识别的鲁棒性是人脸识别系统中一大挑战。针对光照变化对人脸识别的影响，对经典光照不变特征表示算法进行了研究，提出一种基于局部标准差光照不变的人脸特征表示算法及其加权形式。结合完备线性鉴别分析（Complete-Linear Discriminant Analysis，C-LDA）算法提取特征，在Extended Yale-B与YALE 人脸库中，与其他处理光照变化的经典方法相比，如多尺度Retinex（Multi Scale Retinex，MSR）、韦伯脸（Weber-Face，WF）和局部归一化（Local Normalization，LN），提出的算法能获得更高识别率。

Efficient illumination-robust features representation algorithm for face recognition

In the change illumination conditions which can not be controlled, the robustness of face recognition is a challenge in face recognition system.Concerning the illumination changes on the influence of the recognition rate in face recognition, a new face recognition algorithm for varying illumination environment is proposed according to the classic illumination invariant feature representation algorithm, which is based on Local Standard Deviation and its weighted form. Compared with the existed representative approaches, such as WF（Weber-Face）, MSR（Multi Scale Retinex）and LN（Local Normalization）, the novel algorithm can obtain higher recognition rate on the Extended Yale-B Database and the YALE Face Database when combined with the analysis of complete linear discriminant（Complete-Linear Discriminant Analysis, C-LDA） algorithm for feature extraction.