基于低秩矩阵恢复与协同表征的人脸识别算法  

Face recognition algorithm based on low-rank matrix recovery and collaborative representation

作　　者：何林知[1] 赵建民[1] 朱信忠[1] 吴建斌[1] 杨凡[1] 郑忠龙[1] 

机构地区：[1]浙江师范大学数理与信息工程学院,浙江金华321004

出　　处：《计算机应用》2015年第3期779-782,806,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61272468;61170109);浙江省自然科学基金资助项目(LY14F030008;LY13F020015)

摘　　要：针对人脸图像不完备的问题和人脸图像在不同视角、光照和噪声下所造成训练样本污损的问题,提出了一种快速的人脸识别算法——RPCA_CRC。首先,将人脸训练样本对应的矩阵D0分解为类间低秩矩阵D和稀疏误差矩阵E;其次,以低秩矩阵D为基础,得到测试样本的协同表征;最后,通过重构误差进行分类。相对于基于稀疏表征的分类(SRC)方法,所提算法运行速度平均提高25倍;且在训练样本数不完备的情况下,识别率平均提升30%。实验证明该算法快速有效,识别率高。Since the face images might be not over-complete and they might be also corrupted under different viewpoints or different lighting conditions with noise, an efficient and effective method for Face Recognition （FR） was proposed, namely Robust Principal Component Analysis with Collaborative Representation based Classification （ RPCA_CRC）. Firstly, the face training dictionary DO was decomposed into two matrices as the low-rank matrix D and the sparse error matrix E ; Secondly, the test image could be collaboratively represented based on the low-rank matrix D ; Finally, the test image was classified by the reconstruction error. Compared with SRC （ Sparse Representation based Classification）, the speed of RPCA_CRC on average is 25-times faster. Meanwhile, the recognition rate of RPCA_CRC increases by 30% with less training images. The experimental results show the proposed method is fast, effective and accurate.

关 键 词：低秩 稀疏 人脸识别 协同表征 误差矩阵 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]