基于压缩感知的人脸识别方法

doi:10.3969/j.issn.1000-3428.2012.24.032

计算机工程 ›› 2012, Vol. 38 ›› Issue (24): 133-135. doi: 10.3969/j.issn.1000-3428.2012.24.032

基于压缩感知的人脸识别方法

邹伟1，李元祥1，杨俊杰1，周则明2

(1. 上海交通大学航空航天学院，上海 200240；2. 解放军理工大学气象学院，南京 211101)

收稿日期:2011-07-25 修回日期:2011-09-19 出版日期:2012-12-20 发布日期:2012-12-18
作者简介:邹伟(1986－)，男，硕士研究生，主研方向：模式识别，压缩感知；李元祥，副教授；杨俊杰，学士；周则明，教授
基金资助:
国家自然科学基金资助项目(41174164)

Face Recognition Method Based on Compressed Sensing

ZOU Wei1, LI Yuan-xiang1, YANG Jun-jie1, ZHOU Ze-ming2

(1. School of Aeronautics and Astronautics, Shanghai Jiaotong University, Shanghai 200240, China;2. College of Meteorology, PLA University of Science and Technology, Nanjing 211101, China)

Received:2011-07-25 Revised:2011-09-19 Online:2012-12-20 Published:2012-12-18

摘要/Abstract

摘要： 基于稀疏重构的分类方法具有较好的识别效果，但计算复杂度高。为此，提出基于压缩感知的人脸识别方法COMP，将L1范数最小化重构算法替换成正交匹配追踪(OMP)算法，以降低复杂度，并在OMP中引入模式类别信息，使该方法具有更强的分类能力。基于YaleB人脸库的实验结果表明，COMP在低维度时识别率高于OMP。

关键词: 基于稀疏重构的分类方法, 稀疏重构, L1范数最小化, 正交匹配追踪算法, COMP方法

Abstract: Sparse Representation-based Classification(SRC) method performs excellent in face recognition but shows high complexity in computation. This paper proposes face recognition method based on Compressed Sensing(CS) named Classified Orthogonal Matching Pursuit(COMP). L1-norm minimization representation algorithm is replaced by Orthogonal Matching Pursuit(OMP) algorithm to reduce complexity, and mode category information is introduced in OMP to endow the method stronger ability to category. Experiments based on YaleB face database clarify that the recognition rate of COMP is higher than OMP.

Key words: Sparse Representation-based Classification(SRC) method, sparse representation, L1-norm minimization, Orthogonal Matching Pursuit(OMP) algorithm, Classified Orthogonal Matching Pursuit(COMP) method

中图分类号:

TP391

邹伟, 李元祥, 杨俊杰, 周则明. 基于压缩感知的人脸识别方法[J]. 计算机工程, 2012, 38(24): 133-135.

JU Wei, LI Yuan-Xiang, YANG Dun-Jie, ZHOU Ze-Meng. Face Recognition Method Based on Compressed Sensing[J]. Computer Engineering, 2012, 38(24): 133-135.

http://www.ecice06.com/CN/Y2012/V38/I24/133

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