Image-set based face recognition using K-SVD dictionary learning

With rapid development of digital imaging and communication technologies, image set based face recognition (ISFR) is becoming increasingly important and popular. On one hand, easy capture of large number of samples for each subject in training and testing makes us have more information for possible utilization. On the other hand, this large size of data will eventually increase training and classification time and possibly reduce the recognition rate if they are not used appropriately. In this paper, a new face recognition approach is proposed based on the K-SVD dictionary learning to solve this large sample problem by using joint sparse representation. The core idea of this proposed approach is to learn variation dictionaries from gallery and probe face images separately, and then we propose an improved joint sparse representation, which employs the information learned from both gallery and probe samples effectively. Finally, the proposed method is compared with some related methods on several popular face databases, including YaleB, AR, CMU-PIE, Georgia and LFW databases. The experimental results show that the proposed method outperforms several related face recognition methods.

[1]  Jar-Ferr Yang,et al.  Linear Discriminant Regression Classification for Face Recognition , 2013, IEEE Signal Processing Letters.

[2]  Alain Rakotomamonjy,et al.  Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms , 2011, Signal Process..

[3]  Xudong Jiang,et al.  Modular Weighted Global Sparse Representation for Robust Face Recognition , 2012, IEEE Signal Processing Letters.

[4]  Simon C. K. Shiu,et al.  Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization , 2012, ECCV.

[5]  Jian Yang,et al.  Regularized Robust Coding for Face Recognition , 2012, IEEE Transactions on Image Processing.

[6]  Zhi-Hua Zhou,et al.  Face recognition from a single image per person: A survey , 2006, Pattern Recognit..

[7]  David Zhang,et al.  Face recognition using FLDA with single training image per person , 2008, Appl. Math. Comput..

[8]  Allen Y. Yang,et al.  Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment , 2014, International Journal of Computer Vision.

[9]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[10]  Bingsheng He,et al.  The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent , 2014, Mathematical Programming.

[11]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[12]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[13]  Yaakov Tsaig,et al.  Fast Solution of $\ell _{1}$ -Norm Minimization Problems When the Solution May Be Sparse , 2008, IEEE Transactions on Information Theory.

[14]  Shenghua Gao,et al.  Neither Global Nor Local: Regularized Patch-Based Representation for Single Sample Per Person Face Recognition , 2014, International Journal of Computer Vision.

[15]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[16]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[17]  Wen Gao,et al.  Manifold-Manifold Distance with application to face recognition based on image set , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[19]  Lei Zhang,et al.  Sparse Variation Dictionary Learning for Face Recognition with a Single Training Sample per Person , 2013, 2013 IEEE International Conference on Computer Vision.

[20]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Weixin Luo,et al.  Discriminative analysis-synthesis dictionary learning for image classification , 2017, Neurocomputing.

[22]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[23]  Zizhu Fan,et al.  Weighted sparse representation for face recognition , 2015, Neurocomputing.

[24]  Stephen P. Boyd,et al.  An Interior-Point Method for Large-Scale $\ell_1$-Regularized Least Squares , 2007, IEEE Journal of Selected Topics in Signal Processing.

[25]  Shiguang Shan,et al.  Adaptive discriminant learning for face recognition , 2013, Pattern Recognit..

[26]  Simon C. K. Shiu,et al.  Image Set-Based Collaborative Representation for Face Recognition , 2013, IEEE Transactions on Information Forensics and Security.

[27]  Ru-Xi Ding,et al.  Variational Feature Representation-based Classification for face recognition with single sample per person , 2015, J. Vis. Commun. Image Represent..

[28]  Gang Wang,et al.  Image-to-Set Face Recognition Using Locality Repulsion Projections and Sparse Reconstruction-Based Similarity Measure , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[29]  Josef Kittler,et al.  Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Rabab Kreidieh Ward,et al.  Pseudo-Fisherface method for single image per person face recognition , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[31]  Rama Chellappa,et al.  Joint Sparse Representation for Robust Multimodal Biometrics Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Lei Zhang,et al.  Sparse representation or collaborative representation: Which helps face recognition? , 2011, 2011 International Conference on Computer Vision.

[33]  Ajmal S. Mian,et al.  Sparse approximated nearest points for image set classification , 2011, CVPR 2011.

[34]  Larry S. Davis,et al.  Covariance discriminative learning: A natural and efficient approach to image set classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  LinLin Shen,et al.  Structured regularized robust coding for face recognition , 2015, Neurocomputing.

[36]  Ajmal S. Mian,et al.  Face Recognition Using Sparse Approximated Nearest Points between Image Sets , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Zhang Yi,et al.  Learning locality-constrained collaborative representation for robust face recognition , 2012, Pattern Recognit..

[38]  Jun Guo,et al.  Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Trevor Darrell,et al.  Face recognition with image sets using manifold density divergence , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[40]  Jun Guo,et al.  In Defense of Sparsity Based Face Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Wen Gao,et al.  Adaptive discriminant analysis for face recognition from single sample per person , 2011, Face and Gesture 2011.

[42]  Thomas S. Huang,et al.  Joint dynamic sparse representation for multi-view face recognition , 2012, Pattern Recognit..

[43]  Matti Pietikäinen,et al.  From still image to video-based face recognition: an experimental analysis , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[44]  Tao Liu,et al.  Multi-step linear representation-based classification for face recognition , 2016, IET Comput. Vis..

[45]  Jiwen Lu,et al.  Coupled Discriminative Feature Learning for Heterogeneous Face Recognition , 2015, IEEE Transactions on Information Forensics and Security.

[46]  Hakan Cevikalp,et al.  Face recognition based on image sets , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[47]  Mohammed Bennamoun,et al.  Linear Regression for Face Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[48]  A. Martínez,et al.  The AR face databasae , 1998 .

[49]  Jiwen Lu,et al.  Co-Learned Multi-View Spectral Clustering for Face Recognition Based on Image Sets , 2014, IEEE Signal Processing Letters.

[50]  Liang-Tien Chia,et al.  Kernel Sparse Representation for Image Classification and Face Recognition , 2010, ECCV.

[51]  Rama Chellappa,et al.  Dictionary-Based Face Recognition from Video , 2012, ECCV.

[52]  Svetha Venkatesh,et al.  Mixed-norm sparse representation for multi view face recognition , 2015, Pattern Recognit..

[53]  Kun Shang,et al.  A Customized Sparse Representation Model With Mixed Norm for Undersampled Face Recognition , 2016, IEEE Transactions on Information Forensics and Security.

[54]  Wen Gao,et al.  Adaptive generic learning for face recognition from a single sample per person , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[55]  Jian Yang,et al.  A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[56]  Zhi-Hua Zhou,et al.  Making FLDA applicable to face recognition with one sample per person , 2004, Pattern Recognit..