现代电子技术2018，Vol.41Issue(9)：83-86,90,5.DOI:10.16652/j.issn.1004-373x.2018.09.018
基于稀疏表示与特征融合的人脸识别方法
Face recognition method based on sparse representation and feature fusion
摘要
Abstract
Since the robustness of face recognition becomes worse due to the changes of sheltering,expression and illumination, or noise pollution,a face recognition algorithm based on sparse representation and feature fusion is proposed.The low?rank recovery algorithm is used to get the clean face images of training samples and test samples,and their feature vectors(LBP,HOG,Gabor) are extracted. The SRC classification test was performed for some training samples. A loss function is defined according to the rec?ognition result and classification residual of the SRC. The regularization least?square method is used to calculate the weight vector with minimum loss function,according to which the regularization residual is reconstructed for classification. The experiment of the method was performed on ORL,Extended Yale B and AR databases. The results show that the algorithm is superior to the single feature recognition method,and has better generalization performance on the influence of illumination,noise and sheltering.关键词
人脸识别/稀疏表示/低秩恢复/特征融合/鲁棒性/泛化性能Key words
face recognition/sparse representation/low⁃rank recovery/feature fusion/robustness/generalization performance分类
信息技术与安全科学引用本文复制引用
木立生,吕迎春..基于稀疏表示与特征融合的人脸识别方法[J].现代电子技术,2018,41(9):83-86,90,5.基金项目
国家自然科学基金资助项目(51279122)Project Supported by National Natural Science Foundation of China(51279122) （51279122）
