基于最佳鉴别特征和相关向量机的人脸识别算法

Face Recognition Algorithm Based on Optimal Discriminant Features and Relevance Vector Machine

为了获得更高的人脸识别正确率,满足人脸识别的实时性,提出一种基于最佳鉴别特征和相关向量机的人脸识别算法.首先,采用小波变换对人脸图像进行降噪预处理,提取人脸的多方向、多尺度Gabor特征;然后采用核主成分分析对人脸的Gabor特征进行筛选,找到对人脸识别结果影响较大的最佳鉴别特征,有效降低特征数量,去除特征间的冗余信息;最后采用相关向量机对最佳鉴别特征向量进行学习,建立人脸识别的多分类器.选择标准人脸库与经典人脸识别算法进行对比实验,实验结果表明,该算法的人脸平均识别率得到大幅度提高,人脸平均识别时间远少于经典人脸识别算法.

In order to obtain higher accuracy of face recognition,it could meet the real-time requirement of face recognition, we proposed a face recognition algorithm based on optimal discriminant feature and relevance vector machine.Firstly,wavelet transform was used to denoise face image,and multi direction and multi-scale Gabor features of face were extracted.Secondly,kernel principal component analysis was used to screen Gabor features of faces to find the optimal discriminant feature which had a great influence on face recognition results,the number of features was effectively reduced,and redundant information among features was removed.Finally,relevance vector machine was used to learn the optimal discriminant feature vectors and establish multi-classifier for face recognition,and standard face database was used to carried out experiments to test performance compared with the classical face recognition algorithms.The experimental results showthat the average face recognition rate of the proposed algorithm is greatly improved,and the average face recognition time is less than that of the classical face recognition algorithms.