吉林大学学报（理学版）2017，Vol.55Issue(5)：1227-1233,7.DOI:10.13413/j.cnki.jdxblxb.2017.05.34
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
摘要
Abstract
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 show that 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.关键词
人脸图像/最佳鉴别特征/人脸分类器/相关向量机/特征降维Key words
face image/optimal discriminant feature/face classifier/relevance vector machine/feature dimension reduction分类
信息技术与安全科学引用本文复制引用
彭亮清,陈君,伍雁鹏..基于最佳鉴别特征和相关向量机的人脸识别算法[J].吉林大学学报（理学版）,2017,55(5):1227-1233,7.基金项目
湖南省自然科学基金(批准号:2016JJ6136)和湖南省教育厅项目(批准号:17C1438). （批准号:2016JJ6136）
