计算机工程与应用2016，Vol.52Issue(13)：206-211,6.DOI:10.3778/j.issn.1002-8331.1408-0120
基于拓展稀疏表示模型和LC-KSVD的人脸识别
Face recognition based on extend sparse representation and LC-KSVD
摘要
Abstract
To improve the face recognition rate, speed and robustness, this paper proposes a face recognition algorithm based on extended sparse representation model and LC-KSVD(Label Consist K-SVD). For solving the problem that dic-tionary learning only contains representation ability but no class information, the algorithm adds residual vector as coeffi-cient amending vector into original sparse representation model, making the extended model have stronger robustness. The algorithm also adds sparse coding and classifier parameter constraints into the process of dictionary learning and updates sparse coding and classifier parameters in the process, making the dictionary possess good representation and dis-crimination ability. The experimental results show that the algorithm has high recognition rate, low recognition speed and good robustness.关键词
稀疏表示/字典学习/人脸识别/LC-KSVD算法Key words
sparse representation/dictionary learning/face representation/LC-KSVD(Label Consist K-SVD)分类
信息技术与安全科学引用本文复制引用
张建明,何双双,吴宏林,熊兵,李艺敏..基于拓展稀疏表示模型和LC-KSVD的人脸识别[J].计算机工程与应用,2016,52(13):206-211,6.基金项目
国家自然科学基金(No.61202439) （No.61202439）
湖南省教育厅优秀青年项目(No.12B003) （No.12B003）
湖南省交通运输厅科技进步与创新项目(No.201334). （No.201334）
