Abstract
Recently low-rank becomes a popular tool for face representation and classification. None of these existing low-rank based classification methods are in view of the non-linear geometric structures within data, hence the data during the learning process may lose locality and similarity information. Furthermore, Lin et al. propose a Non-negative Sparse Hyper-Laplacian regularized LRR model (NSHLRR) to improve LRR in the above respect and apply it to image clustering. In this paper, we propose a novel classification method, namely NSHLRR-based Classification (NSHLRRC) for face recognition. Experimental results on public face databases clearly show our method has very competitive classification results, which also show that our method outperforms other state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)
Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition: which helps face recognition? In: Proceedings of International Conference on Computer Vision, Barcelona, Spain, pp. 471–478 (2011)
Chen, C.F., Wei, C.P., Wang, Y.C.F.: Low-rank matrix recovery with structural incoherence for robust face recognition. In: Proceedings of 2012 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2618–2625 (2012)
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2013)
Nguyen, H.V., Huang, R., Yang, W., Sun, C.: Face recognition based on low-rank matrix representation. In: Proceedings of the 33rd Chinese Control Conference (2014)
Yin, M., Gao, J., Lin, Z.: Laplacian regularized low-rank representation and its applications. IEEE Trans. Pattern Anal. Mach. Intell. 38(3), 504–517 (2016)
Kim, H., Park, H.: Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J. Matrix Anal. Appl. 30(2), 713–730 (2008)
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Proceedings of Advances in Neural Information Processing Systems, vol. 14, pp. 585–591 (2001)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image Vis. Comput. 28(5), 807–813 (2010)
Martinez, A., Benavente, R.: The AR face database. CVC Technical report, vol. 24 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, J., Chen, C., Hou, X., Wang, R. (2017). Laplacian Regularized Non-negative Sparse Low-Rank Representation Classification. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_73
Download citation
DOI: https://doi.org/10.1007/978-3-319-69923-3_73
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69922-6
Online ISBN: 978-3-319-69923-3
eBook Packages: Computer ScienceComputer Science (R0)