为了提高光照条件下人脸识别系统的识别率，提出了一种非下采样Contourlet 变换和伪Zernike 矩相结合的人脸识别新算法．该算法首先利用软阈值去噪方法在Lambertian光照模型中提取人脸的光照不变成分，然后计算其伪Zernike 矩向量作为人脸的分类特征．在Extended YaleB 和CMU PIE 人脸库上的实验结果表明，与常用的人脸识别算法相比，文中算法能够更有效地去除光照影响，适应尺度和姿态变化，极大地提高了人脸识别的精度．

In order to improve the face recognition rate under varying lighting conditions,a novel face recognition algorithm based on the nonsubsampled Contourlet transform and the pseudo-Zernike moment is proposed. In this algorithm,first,invariant illumination components are extracted via the soft-threshold denoising in the Lambertian illumination model. Then,the corresponding pseudo-Zernike moment vectors are calculated and are used as face classification features. Experimental results on Extended YaleB and CMU PIE face databases show that,as compared with the common face recognition algorithms,the proposed algorithm can eliminate the effect of illumination more effectively and adapt to the variation of scale and pose,so that it significantly improves the accuracy of face recognition.