中文摘要：

为了改善复杂光照条件下人脸识别的性能，提出结合小波变换和LBP（LocalBinaryPattern，LBP）提取复杂光照下人脸图像的对数域特征来进行人脸识别。本文首先将人脸图像由空域变换到对数域，再做两级离散小波分解，并利用高频分量重构原图，也即对人脸图像进行高通滤波，滤除低频光照成分，以达到复杂光照补偿的目的，最后利用分块LBP提取光照补偿后图像的局部纹理特征，并将这些特征应用于人脸识别。基于Yale—B和CMU-PIE人脸库上的实验结果显示本文算法对复杂光照具有较强鲁棒性，具备提取复杂光照条件下人脸图像有效特征的能力。

英文摘要：

In order to improve the performance of face system under complex illumination conditions, we presents a algo- rithm that combining wavelet transform and LBP （ Local Binary Pattern） descriptor in the logarithmic domain to identify face images under complex lighting. First of all, the face images were transformed from spatial domain to frequency domain, and then two level two-dimension discrete wavelet decomposition was calculated on the transformed face images. Then six high frequency coefficients of wavelet transform were used to reconstruct images for face illumination compensation, that is mak- ing high-pass filter on face images, elminating the low-frequency lighting eomponets. Then we partitioned the lighting com- pensated face images into a grid of fixed size cells, and LBP descriptor extracted the local texture features of each subimag- es. The features of these subimages were concentration into one feature. At last, we used these features to face recognition. Experimental results based on Yale-B and CMU-PIE show that the proposed algorithm is rubost to complex illumination, and it can express useful features of face images under complex lighting correctly.