基于支持向量机的局部二值模式加权算法在人脸识别中的应用

Application of Local Binary Pattern Weighting Algorithm in Face Recognition Based on Support Vector Machine

针对人脸特征的提取问题,提出了在SVM的基础上结合局部二值模式(LBP)加权算法。首先描述了人脸图像的LBP和深度LBP特征,通过加权形成人脸特征向量,通过这些向量采用支持向量机进行分类,依托人脸数据库进行仿真。实验结果表明,本文算法提高了人脸平均识别率以及识别效率,具有一定的推广价值。

With regard to the problem of facial feature extraction, a weighted algorithm is proposed on the basis of the SVM and combined with the local binary pattern （LBP）. First, the LBP and in-depth LBP features of face images are described, to form facial feature vectors through weighting. These vectors are classified by using support vector machine （SVM）, and simulation is performed relying on the face database. The experimental results show that the algorithm improves the average recognition rate of human faces and the efficiency of recognition, and thus is worth popularizing.