计算机工程与科学2018，Vol.40Issue(1)：108-115,8.DOI:10.3969/j.issn.1007-130X.2018.01.016
基于结构化低秩表示和低秩投影的人脸识别算法
Face recognition based on structured low rank representation and low rank projection
摘要
Abstract
Occlusion and corruption in the training images result in degraded performance of the sparse representation classification (SRC) algorithm in practical applications of face recognition.Aiming at the aforementioned problem,we propose a new face recognition method based on structured low rank representation (SLR) and low rank projection (LRP),called SLR_LRP.Firstly,the original training samples are decomposed via SLR to obtain clean training samples.And a LRP matrix is learned based on the original training samples and the recovered clean samples.Secondly,test samples are projected onto the LRP matrix.Finally,SRC is exploited to classify the corrected test samples.Experiments on the AR and the Extended Yale B face databases demonstrate that the SLR_LRP can effectively deal with the occlusion and pixel corruption in samples.关键词
低秩矩阵恢复/结构化低秩表示/低秩投影/稀疏表示分类Key words
low rank matrix recovery/structured low rank representation/low rank projection (LRP)/sparse representation classification分类
信息技术与安全科学引用本文复制引用
刘作军,高尚兵..基于结构化低秩表示和低秩投影的人脸识别算法[J].计算机工程与科学,2018,40(1):108-115,8.基金项目
国家自然科学青年基金(61402192) （61402192）
江苏省高校自然科学研究面上项目(14KJB520006) （14KJB520006）
江苏省先进制造重点实验室开放课题(HGAMTL-1401) （HGAMTL-1401）
