Zhe SUN, Meng WANG, Zhengping HU, Mei ZHU, Mei SUN

Sample group and misplaced atom dictionary learning for face recognition

Latest research results have demonstrated the effectiveness of both sparse (or collaborative) representation and dictionary learning for problem solving in face recognition and other signal classi cation. Considering the fact that an informative dictionary helps a lot in sparse coding, a novel model that consists of group dictionary learning and high-quality joint kernel collaborative representation was proposed in this paper, where rich information from original and virtual space was mined and constructed as a sample group space to improve classi cation accuracy. Meanwhile, joint kernel collaborative representation with an ℓ 2 -regularization-based classi er was used to capture more nonlinear structure and minimize the time cost. Experiments showed that the proposed method outperformed several similar state-of-the-art methods in terms of accuracy and computational complexity.

PDF

___

[1] Zhang H, Li F, Deng H, Li Z, Yan K, Xie C, Wang K. Adjusting samples for obtaining better l2-norm minimization based sparse representation. J Vis Commun Image R 2016; 39: 93-99.
[2] Zhang H, Wang F, Chen Y, Zhang W, Wang K, Liu J. Sample pair based sparse representation classi cation for face recognition. Expert Syst Appl 2016; 45: 352-358.
[3] Zheng C, Hou Y, Zhang J. Improved sparse representation with low-rank representation for robust face recognition. Neurocomputing 2016; 198: 114-124.
[4] Wu F, Jing XY, You X, Yue D, Hu R, Yang JY. Multi-view low-rank dictionary learning for image classi cation. Pattern Recogn 2016; 50: 143-154.
[5] Sun Y, Wang X, Tang X. Deep learning face representation by joint identi cation-veri cation. Adv Neur In 2014; 27: 1988-1996.
[6] Sun Y, Wang X, Tang X. Deeply learned face representations are sparse, selective, and robust. In: IEEE 2014 Conference on Computer Vision and Pattern Recognition; 24{27 July 2014; Columbus, OH, USA. New York, NY, USA: IEEE. pp. 2892-2900.
[7] Zhang Z, Luo P, Chen CL, Tang X. Learning deep representation for face alignment with auxiliary attributes. IEEE T Pattern Anal 2016; 38: 918-930.
[8] Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y. Robust face recognition via sparse representation. IEEE T Pattern Anal 2009; 31: 210-227.
[9] Yang M, Zhang L, Yang J, Zhang D. Robust sparse coding for face recognition. In: IEEE 2011 Computer Vision and Pattern Recognition; 20{25 July 2011; Colorado Springs, CO, USA. New York, NY, USA: IEEE. pp. 625-632.
[10] Yang M, Zhang L, Yang J, Zhang D. Regularized robust coding for face recognition. IEEE T Image Process 2013; 22: 1753-1766.
[11] Yang M, Song T, Liu F, Shen L. Structured Regularized robust coding for face recognition. In: 2015 Chinese Conference on Computer Vision; 18{20 September 2015; Xi'an, China. pp. 80-89.
[12] Zhang Q, Li B. Mining discriminative components with low-rank and sparsity constraints for face recognition. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 12{16 August 2012; Beijing, China. New York, NY, USA: ACM. pp. 1469-1477.
[13] Deng W, Hu J, Guo J. Extended SRC: undersampled face recognition via intraclass variant dictionary. IEEE T Pattern Anal 2012; 34: 1864-1870.
[14] Deng W, Hu J, Guo J. In defense of sparsity based face recognition. In: IEEE 2013 Conference on Computer Vision and Pattern Recognition; 23{28 June 2013; Portland, OR, USA. New York, NY, USA: IEEE. pp. 399-406.
[15] Zhang L, Yang M, Feng X. Sparse representation or collaborative representation: which helps face recognition? In: IEEE 2011 International Conference on Computer Vision; 6{13 November 2011; Barcelona, Spain. New York, NY, USA: IEEE. pp. 471-478.
[16] Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 1998; 2: 121-167.
[17] Turk M, Pentland A. Eigenfaces for recognition. J Cognitive Neurosci 1991; 3: 71-86.
[18] Etemad K, Chellappa R. Discriminant analysis for recognition of human face images. In: International Conference on Audio-Video-Based Biometric Person Authentication; pp. 1724-1733.
[19] He X, Yan S, Hu Y, Niyogi P, Zhang HJ. Face recognition using Laplacian faces. IEEE T Pattern Anal 2005; 27: 328-340.
[20] Kang C, Liao S, Xiang S, Pan C. Kernel sparse representation with local patterns for face recognition. In: IEEE 2011 International Conference on image Processing; 11{14 September 2011; Brussels, Belgium. New York, NY, USA: IEEE. pp. 3009-3012.
[21] Wang Z, Yang W, Yin J, Sun C. Kernel collaborative representation with regularized least square for face recognition. In: 2012 10th International Conference on Service Oriented Computing; 12{15 November 2012; Jinan, China. pp. 130-137.
[22] Yang W, Wang Z, Yin J, Sun C, Ricanek K. Image classi cation using kernel collaborative representation with regularized least square. Appl Math Comput 2013; 222: 13-28.
[23] Liu W, Yu Z, Lu L, Wen Y, Li H, Zou Y. KCRC-LCD: Discriminative kernel collaborative representation with locality constrained dictionary for visual categorization. Pattern Recogn 2015; 48: 3076-3092.
[24] Wang D, Lu H, Yang MH. Kernel collaborative face recognition. Pattern Recogn 2015; 48: 3025-3037.
[25] Chen L, Man H, Ne an AV. Face recognition based on multi-class mapping of Fisher scores. Pattern Recogn 2005; 38: 799-811.
[26] Huang GB, Mattar M, Berg T, Learned-Miller E. Labeled Faces in the Wild: A database for studying face recognition in unconstrained environments. Available online at http://vis-www.cs.umass.edu/papers/eccv2008-lfw.pdf.
[27] Li FF, Fergus R, Perona P. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Comput Vis Image Und 2004; 106: 59-70.
[28] Wang D, Lu H. Object tracking via 2DPCA and l 1 -regularization. IEEE Signal Proc Let 2012; 19: 711-714.
[29] Liu W, Yu Z, Wen Y, Yang M. Multi-kernel collaborative representation for image classi cation. In: IEEE 2015 International Conference on Image Processing; 27{30 September 2015; Quebec City, Canada. New York, NY, USA: IEEE. pp. 21-25.
[30] Xu Y, Li X, Yang J, Zhang D. Integrate the original face image and its mirror image for face recognition. Neurocomputing 2014; 131: 191-199.