计算机应用研究2017，Vol.34Issue(10)：3157-3161,5.DOI:10.3969/j.issn.1001-3695.2017.10.061
基于鉴别性低秩表示及字典学习的鲁棒人脸识别算法
Robust face recognition of discriminative low-rank representation with dictionary learning
摘要
Abstract
In consideration of noises appeared in the images used in face recognition,this paper proposed a novel recognition method of discriminative low-rank representation with dictionary learning.It used a discriminative low-rank representation(DLRR) method to obtain a clean dictionary whose sub-dictionaries for distinct classes required to be as independent as possible.By introducing a Fisher discrimination dictionary learning(FDDL) method,it could obtain a structured dictionary,and each sub-dictionary in the whole structured dictionary had good representation ability to the training samples from the associated class.The coding coefficients had small within-class scatter but big between-class scatter.Finally,the samples from the correct class did greater contributions to the sparse linear representation of the test sample.The experimental results on the standard face databases show that the proposed method has good performance.关键词
人脸识别/低秩表示/字典学习/稀疏线性表示Key words
face recognition/low-rank representation/dictionary learning/sparse linear representation分类
信息技术与安全科学引用本文复制引用
赵雯,吴小俊..基于鉴别性低秩表示及字典学习的鲁棒人脸识别算法[J].计算机应用研究,2017,34(10):3157-3161,5.基金项目
国家自然科学基金面上项目(61373055,61672265) （61373055,61672265）
江苏省教育厅科技成果产业化推进项目(JH10-28) （JH10-28）
江苏省产学研创新项目(BY2012059) （BY2012059）
