计算机技术与发展Issue(2)：22-25,4.DOI:10.3969/j.issn.1673-629X.2016.02.005
基于加权稀疏近邻表示的人脸识别
Face Recognition Based on Weighted Sparse Neighbor Representation
摘要
Abstract
Currently,face recognition via sparse representation has gained widespread attention. Since the sparse neighbor representation algorithm without considering the different weight of training samples to reconstruct the test sample,simultaneously,to improve the recog-nition rate of face recognition based on sparse neighbor representation, in this paper, a face recognition algorithm of weighted sparse neighbor representation was proposed. First,in each class of the training samples, k samples nearest to the test samples are selected,con-structed new training samples in this class. And then do the same operation in each class,so as to construct a new training dictionary, when solving sparse coefficient with l1 norm minimization,a weight is given to the sparse coefficient of each new training sample. Finally with the new training dictionary,according to the minimum reconstruction error to complete the recognition task. The most experiments results on Yale B face database and ORL face database show that the proposed method achieves higher recognition rate compared with KNN and SNRC ( Sparse Neighbor Representation for Classification) ,which confirms the effectiveness of the algorithm.关键词
稀疏表示/特征提取/加权近邻/人脸识别Key words
sparse representation/feature extraction/weighted nearest neighbor/face recognition分类
信息技术与安全科学引用本文复制引用
谢文浩,翟素兰..基于加权稀疏近邻表示的人脸识别[J].计算机技术与发展,2016,(2):22-25,4.基金项目
安徽省高校省级优秀青年人才基金重点项目(2013SQRL005ZD) （2013SQRL005ZD）
