Abstract
For face recognition, conventional dictionary learning (DL) methods have disadvantages. In the paper, we propose a novel robust, discriminative and comprehensive DL (RDCDL) model. The proposed model uses sample diversities of the same face image to make the dictionary robust. The model includes class-specific dictionary atoms and disturbance dictionary atoms, which can well represent the data from different classes. Both the dictionary and the representation coefficients of data on the dictionary introduce discriminative information, which improves effectively the discrimination capability of the dictionary. The proposed RDCDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art sparse representation and dictionary learning methods for face recognition.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang, J.C., Wright, J., Ma, Y., Huang, T.: Image super-resolution as sparse representation of raw image patches. In: CVPR (2008)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)
Zhang, Q., Li, B.X.: Discriminative K-SVD for Dictionary Learning in Face Recognition. In: CVPR (2010)
Jiang, Z.L., Lin, Z., Davis, L.S.: Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2651–2664 (2009)
Kong, S., Wang, D.: A dictionary learning approach for classification: separating the particularity and the commonality. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 186–199. Springer, Heidelberg (2012)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing over complete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54(11), 4311–4322 (2006)
Ramirez, I., Sprechmann, P., Sapiro, G.: Classification and clustering via dictionary learning with structured incoherence and shared features. In: CVPR (2010)
Yang, M., Zhang, L., Feng, X.C., Zhang, D.: Fisher discrimination dictionary learning for sparse representation. In: ICCV (2011)
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zissserman, A.: Learning discriminative dictionaries for local image analysis. In: CVPR (2008)
Deng, W.H., Hu, J.N., Guo, J.: Extended SRC: undersampled face recognition via intraclass variation dictionary. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1864–1870 (2012)
Rosasco, L., Verri, A., Santoro, M., Mosci, S., Villa, S.: Iterative Projection Methods for Structured Sparsity Regularization. MIT Technical reports, MIT-CSAIL-TR-2009-050, CBCL-282 (2009)
Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. on Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)
Martinez, A., Benavente, R.: The AR Face Database. CVC Technical report No. 24 (1998)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image Vis. Comput. 28, 807–813 (2010)
Yang, M., Zhang, L., Feng, X.C., Zhang, D.: Sparse representation based Fisher discrimination dictionary learning for image classification. Int. J. Comput. Vis. 109, 209–232 (2014)
Zhang, B.C., Perina, A., Murino, V., Bue, A.D.: Sparse representation classification with manifold constraints transfer. In: CVPR (2015)
Jing, X.Y., Wu, F., Zhu, X.K., Dong, X.W., Ma, F., Li, Z.Q.: Multi-spectral low-rank structured dictionary learning for face recognition. Pattern Recogn. (2016). doi:10.1016/j.patcog.2016.01.023
Acknowledgment
This work is supported by the Projects under Grant no. 2015RC16 and 2015RZY01. This work is partially supported by the National Natural Science Foundation for Young Scientists of China (Grant no. 61402289) and National Science Foundation of Guangdong Province (Grant no. 2014A030313558).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Lin, G., Yang, M., Shen, L., Xie, W., Zheng, Z. (2016). Sample Diversity, Discriminative and Comprehensive Dictionary Learning for Face Recognition. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-46654-5_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46653-8
Online ISBN: 978-3-319-46654-5
eBook Packages: Computer ScienceComputer Science (R0)