Abstract: This paper presents a new Local Binary Pattern（LBP）-based face recognition method with Hamming distance constraint.The traditional LBP operator uses a uniform pattern to describe the local patterns and assort the other nonuniform patterns to one additional class，for images under expression and illumination condition changes，this method could cause more inaccuracy and instability.By assuming that the illumination，pose or expression changes of a face image are some kinds of“noise”，it introduces the widely used Hamming distance in channel coding to LBP so as to decrease the error rate caused by these noise disturbances.Experimental results on Face Recognition Grand Challenge（FRGC） show that this method improves the recognition performance obviously than the traditional LBP-based face recognition methods when face images are under uncontrolled circumstances.

Key words: face recognition, Local Binary Pattern（LBP）, Hamming distance, Face Recognition Grand Challenge（FRGC）

摘要： 提出了一种新的使用汉明距离约束的LBP（局部二值化模式）人脸识别算法。传统的LBP算子使用一致性模式（Uniform Pattern）来描述图像的局部特征，并且把其他非一致性模式都归并到另外的一个类中去，对于受光照和表情变化影响的图像，这种方法的准确性会降低。假定光照、姿态、表情的影响都可以看作是某种“噪声”，把信道编码中广泛应用的汉明距离引入到LBP算法中，减少由于这些噪声干扰产生的错误率。在FRGC上的实验结果显示：对于无约束环境下的人脸图片来说，该方法要优于传统的基于LBP的人脸识别方法。

关键词: 人脸识别, 局部二值化模式（LBP）, Hamming距离, 人脸识别竞赛（FRGC）