中文摘要：

为了克服非约束性（光照、遮挡、姿势等变化）条件下会大大降低人脸识别率的缺陷,提出一种基于Gabor相位和幅值信息的统一化局部二进制模式＆稀疏表示人脸识别算法.首先将人脸图像经过Gabor滤波器滤波得到Gabor相位和幅值图像,然后分块提取其统一化的局部二进制直方图,最后通过稀疏表示判断测试图像所属类.利用AR数据库进行实验的结果表明,与SRC、结合LBP和SRC特征的分割识别算法相比,该算法在非约束性条件下识别率最高.

英文摘要：

In order to overcome the defects that the face recognition accuracy can be greatly reduced in the uncontrolled environments such as the change of illumination, occlusion, and posture, etc, an uniform local binary pattern sparse representation face recognition algorithm based on Gabor phase and amplitude was proposed. In the proposed algorithm, the Gabor phase and amplitude images of a face image are obtained via Gabor filter, then uniform local binary histogram is extracted via block, finally the test image can be classified as the existing class via sparse representation. The experimental results based on AR face database show that the proposed algorithm has the highest face recognition accuracy in the existing uncontrolled environments comparison with the SRC（sparse representation-based classifier） face recognition algorithm, and face segmentation recognition algorithm based on LBP and SRC.