基于低秩投影与稀疏表示的人脸识别算法
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摘要
当训练和测试图像同时受到污损时,人脸识别的性能会急剧下降。为了解决这一问题,提出了一种新的人脸识别算法。首先利用鲁棒主成分分析(robust principal component analysis,RPCA)方法得到训练样本的低秩部分;然后基于原始训练样本及其低秩部分得到低秩投影矩阵,该矩阵可以对存在污损的测试图像进行恢复;最后使用稀疏表示分类(sparse representation based classification,SRC)算法对恢复后的测试图像进行分类。在两个公开数据库上进行实验,实验结果证明了本文算法的有效性,同时识别性能优于SRC及线性回归分类(linear regression classification,LRC)方法,能在一定程度上处理样本数据受到污损的情况。
When providing corrupted training and test samples,performance of face recognition will degrade dramatically. To mitigate this problem,a new method for face recognition is proposed. Firstly,the training data is decomposed via robust principal component analysis( RPCA) to obtain its low rank part,then a low-rank projection matrix is learned based on the original training data and its low rank part. This projection matrix is capable of correcting corrupted test images. Finally the corrected test samples are classified based on sparse representation based classification( SRC). Experimental results on two publicly available databases document the effectiveness of the proposed method,and it achieves better performance than SRC based approaches and linear regression classification( LRC),meanwhile it can handle the case that samples are contaminated to some extent.
When providing corrupted training and test samples,performance of face recognition will degrade dramatically. To mitigate this problem,a new method for face recognition is proposed. Firstly,the training data is decomposed via robust principal component analysis( RPCA) to obtain its low rank part,then a low-rank projection matrix is learned based on the original training data and its low rank part. This projection matrix is capable of correcting corrupted test images. Finally the corrected test samples are classified based on sparse representation based classification( SRC). Experimental results on two publicly available databases document the effectiveness of the proposed method,and it achieves better performance than SRC based approaches and linear regression classification( LRC),meanwhile it can handle the case that samples are contaminated to some extent.
引文
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5 桑园.动态稀疏表示方法在非接触式指纹图像识别中的应用[J].科学技术与工程,2018,18(21):258-263Sang Yuan. Application of dynamic sparse representation in non-contact fingerprint image recognition[J]. Science Technology and Engineering. 2018,18(21):258-263
6 Wright J,Yang A Y,Ganesh A,et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227
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8 Deng W,Hu J,Guo J. Extended SRC:Undersampled face recognition via intraclass variant dictionary[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(9):1864-1870
9 Yang M,Feng Z Z,Shiu S C K,et al. Fast and robust face recognition via coding residual map learning based adaptive masking[J].Pattern Recognition,2014,47(2):535-543
10 Wen Y D,Liu W Y,Yang M,et al. Structured occlusion coding for robust face recognition[J]. Neurocomputing,2016,178:11-24
11 李小薪,梁荣华.有遮挡人脸识别综述:从子空间回归到深度学习[J].计算机学报,2018,41(1):177-207Li Xiaoxin,Liang Ronghua. A review for face recognition with occlusion:From subspace regression to deep learning. Chinese Journal of Computers,2018,41(1):177-207
12 Candès E J,Li X,Ma Y,et al. Robust principal component analysis?[J]. Journal of the ACM(JACM),2011,58(3):11
13 Liu G C,Lin Z C,Yan S C,et al. Robust recovery of subspace structures by low-rank representation[J]. Pattern Analysis and Machine Intelligence,IEEE Transactions on,2013,35(1):171-184
14 Chen C F,Wei C P,Wang Y C F. Low-rank matrix recovery with structural incoherence for robust face recognition[C]//Computer Vision and Pattern Recognition(CVPR),2012 IEEE Conference on. New York:IEEE,2012:2618-2625
15 胡正平,李静.基于低秩子空间恢复的联合稀疏表示人脸识别算法[J].电子学报,2013,41(5):987-991Hu Zhengping,Li Jing. Face recognition of joint sparse representation based on low-rank subspace recovery[J]. Acta Electronica Sinica,2013,41(5):987-991
16 杜海顺,张旭东,侯彦东,等.一种基于低秩恢复稀疏表示分类器的人脸识别方法[J].计算机科学,2014,41(4):309-313Du Haishun,Zhang Xudong,Hou Yandong,et al. Face recognition method based on low-rank recovery sparse representation classifier[J]. Computer Science,2014,41(4):309-313
17 何林知,赵建民,朱信忠,等.基于低秩矩阵恢复与协同表征的人脸识别算法[J].计算机应用,2015,35(3):779-782,806He Linzhi,Zhao Jianmin,Zhu Xinzhong,et al. Face recognition algorithm based on low-rank matrix recovery and collaborative representation[J]. Journal of Computer Applications,2015,35(3):779-782,806
18 Bao B K,Liu G G,Xu C S,et al. Inductive robust principal component analysis[J]. IEEE Transactions on Image Processing,2012,21(8):3794-3800
19 Cao F L,Feng X S,Zhao J W. Sparse representation for robust face recognition by dictionary decomposition[J]. Journal of Visual Communication and Image Representation,2017,46:260-268
20 Figueiredo M A T,Nowak R D,Wright S J. Gradient projection for sparse reconstruction:Application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing,2007,1(4):586-597
21 Kim S J,Koh K,Lustig M,et al. An interior-point method for large-scale L1-regularized least squares[J]. IEEE Journal of Selected Topics in Signal Processing,2007,1(4):606-617
22 Sim T,Baker S,Bsat M. The CMU pose,illumination,and expression database[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(12):1615-1618
23 Martinez A M. The AR face database[R]. Barcelona:Computer Vision Center(CVC),1998,24
24 Naseem I,Togneri R,Bennamoun M. Linear regression for face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(11):2106-2112
2 Belhumeur P N,Hespanha J P,Kriegman D. Eigenfaces vs fisherfaces:Recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720
3 He X F,Yan S C,Hu Y X,et al. Face recognition using Laplacianfaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3):328-340
4 Yan S C,Xu D,Zhang B Y,et al. Graph embedding and extensions:A general framework for dimensionality reduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(1):40-51
5 桑园.动态稀疏表示方法在非接触式指纹图像识别中的应用[J].科学技术与工程,2018,18(21):258-263Sang Yuan. Application of dynamic sparse representation in non-contact fingerprint image recognition[J]. Science Technology and Engineering. 2018,18(21):258-263
6 Wright J,Yang A Y,Ganesh A,et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227
7 Yang M,Zhang L. Gabor feature based sparse representation for face recognition with gabor occlusion dictionary[C]//European Conference on Computer Vision. Berlin:Springer,2010:448-461
8 Deng W,Hu J,Guo J. Extended SRC:Undersampled face recognition via intraclass variant dictionary[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(9):1864-1870
9 Yang M,Feng Z Z,Shiu S C K,et al. Fast and robust face recognition via coding residual map learning based adaptive masking[J].Pattern Recognition,2014,47(2):535-543
10 Wen Y D,Liu W Y,Yang M,et al. Structured occlusion coding for robust face recognition[J]. Neurocomputing,2016,178:11-24
11 李小薪,梁荣华.有遮挡人脸识别综述:从子空间回归到深度学习[J].计算机学报,2018,41(1):177-207Li Xiaoxin,Liang Ronghua. A review for face recognition with occlusion:From subspace regression to deep learning. Chinese Journal of Computers,2018,41(1):177-207
12 Candès E J,Li X,Ma Y,et al. Robust principal component analysis?[J]. Journal of the ACM(JACM),2011,58(3):11
13 Liu G C,Lin Z C,Yan S C,et al. Robust recovery of subspace structures by low-rank representation[J]. Pattern Analysis and Machine Intelligence,IEEE Transactions on,2013,35(1):171-184
14 Chen C F,Wei C P,Wang Y C F. Low-rank matrix recovery with structural incoherence for robust face recognition[C]//Computer Vision and Pattern Recognition(CVPR),2012 IEEE Conference on. New York:IEEE,2012:2618-2625
15 胡正平,李静.基于低秩子空间恢复的联合稀疏表示人脸识别算法[J].电子学报,2013,41(5):987-991Hu Zhengping,Li Jing. Face recognition of joint sparse representation based on low-rank subspace recovery[J]. Acta Electronica Sinica,2013,41(5):987-991
16 杜海顺,张旭东,侯彦东,等.一种基于低秩恢复稀疏表示分类器的人脸识别方法[J].计算机科学,2014,41(4):309-313Du Haishun,Zhang Xudong,Hou Yandong,et al. Face recognition method based on low-rank recovery sparse representation classifier[J]. Computer Science,2014,41(4):309-313
17 何林知,赵建民,朱信忠,等.基于低秩矩阵恢复与协同表征的人脸识别算法[J].计算机应用,2015,35(3):779-782,806He Linzhi,Zhao Jianmin,Zhu Xinzhong,et al. Face recognition algorithm based on low-rank matrix recovery and collaborative representation[J]. Journal of Computer Applications,2015,35(3):779-782,806
18 Bao B K,Liu G G,Xu C S,et al. Inductive robust principal component analysis[J]. IEEE Transactions on Image Processing,2012,21(8):3794-3800
19 Cao F L,Feng X S,Zhao J W. Sparse representation for robust face recognition by dictionary decomposition[J]. Journal of Visual Communication and Image Representation,2017,46:260-268
20 Figueiredo M A T,Nowak R D,Wright S J. Gradient projection for sparse reconstruction:Application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing,2007,1(4):586-597
21 Kim S J,Koh K,Lustig M,et al. An interior-point method for large-scale L1-regularized least squares[J]. IEEE Journal of Selected Topics in Signal Processing,2007,1(4):606-617
22 Sim T,Baker S,Bsat M. The CMU pose,illumination,and expression database[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(12):1615-1618
23 Martinez A M. The AR face database[R]. Barcelona:Computer Vision Center(CVC),1998,24
24 Naseem I,Togneri R,Bennamoun M. Linear regression for face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(11):2106-2112