Journal Home > Volume 2 , Issue 4
Computational Visual Media 2016, 2(4): 357-365 https://doi.org/10.1007/s41095-016-0061-5
Research Article | Open Access | Issue | Published: 15 November 2016

Weighted average integration of sparse representation and collaborative representation for robust face recognition

Show Author's Information Shaoning Zeng1( ),  Yang Xiong1
Huizhou University, Guangdong 516007, China.
Keywords:
sparse representation, image classification, collaborative representation, face recognition
Cite this article:
Zeng S, Xiong Y. Weighted average integration of sparse representation and collaborative representation for robust face recognition. Computational Visual Media, 2016, 2(4): 357-365. https://doi.org/10.1007/s41095-016-0061-5
Download citation
EndNote(RIS)
BibTeX

620

Views

28

Downloads

Citations

5

Crossref

N/A

WoS

5

Scopus

0

CSCD

Abstract Full text About this article

Sparse representation is a significant method to perform image classification for face recognition. Sparsity of the image representation is the key factor for robust image classification. As an improvement to sparse representation-based classification, collaborative representation is a newer method for robust image classification. Training samples of all classes collaboratively contribute together to represent one single test sample. The ways of representing a test sample in sparse representation and collaborative representation are very different, so we propose a novel method to integrate both sparse and collaborative representations to provide improved results for robust face recognition. The method first computes a weighted average of the representation coefficients obtained from two conventional algorithms, and then uses it for classification. Experiments on several benchmark face databases show that our algorithm outperforms both sparse and collaborative representation-based classification algorithms, providing at least a 10% improvement in recognition accuracy.


menu
Abstract
Full text
Outline
About this article

Weighted average integration of sparse representation and collaborative representation for robust face recognition

Show Author's information Shaoning Zeng1( ), Yang Xiong1
Huizhou University, Guangdong 516007, China.

Abstract

Sparse representation is a significant method to perform image classification for face recognition. Sparsity of the image representation is the key factor for robust image classification. As an improvement to sparse representation-based classification, collaborative representation is a newer method for robust image classification. Training samples of all classes collaboratively contribute together to represent one single test sample. The ways of representing a test sample in sparse representation and collaborative representation are very different, so we propose a novel method to integrate both sparse and collaborative representations to provide improved results for robust face recognition. The method first computes a weighted average of the representation coefficients obtained from two conventional algorithms, and then uses it for classification. Experiments on several benchmark face databases show that our algorithm outperforms both sparse and collaborative representation-based classification algorithms, providing at least a 10% improvement in recognition accuracy.

Keywords: sparse representation, image classification, collaborative representation, face recognition

References(39)

[1]
Brunelli, R.; Poggio, T. Face recognition: Features versus templates. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 15, No. 10, 1042-1052, 1993.
DOI Google Scholar
[2]
Wright, J.; Yang, A. Y.; Ganesh, A.; Sastry, S. S.; Ma, Y. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 31, No. 2, 210-227, 2009.
DOI Google Scholar
[3]
Xu, Y.; Zhang, D.; Yang, J.; Yang, J.-Y. An approach for directly extracting features from matrix data and its application in face recognition. Neurocomputing Vol. 71, Nos. 10–12, 1857-1865, 2008.
DOI Google Scholar
[4]
Turk, M.; Pentland, A. Eigenfaces for recognition. Journal of Cognitive Neuroscience Vol. 3, No. 1, 71-86, 1991.
DOI Google Scholar
[5]
Park, S. W.; Savvides, M. A multifactor extension of linear discriminant analysis for face recognition under varying pose and illumination. EURASIP Journal on Advances in Signal Processing Vol. 2010, 158395, 2010.
DOI Google Scholar
[6]
Lu, J.; Plataniotis, K. N.; Venetsanopoulos, A. N. Face recognition using LDA-based algorithms. IEEE Transactions on Neural Networks Vol. 14, No. 1, 195-200, 2003.
DOI Google Scholar
[7]
Debruyne, M.; Verdonck, T. Robust kernel principal component analysis and classification. Advances in Data Analysis and Classification Vol. 4, No. 2, 151-167, 2010.
DOI Google Scholar
[8]
Muller, K.-R.; Mika, S.; Ratsch, G.; Tsuda, K.; Scholkopf, B. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks Vol. 12, No. 2, 181-201, 2001.
DOI Google Scholar
[9]
Yang, J.; Wright, J.; Huang, T. S.; Ma, Y. Image super-resolution via sparse representation. IEEE Transactions on Image Processing Vol. 19, No. 11, 2861-2873, 2010.
DOI Google Scholar
[10]
Xu, Y.; Zhang, D.; Yang, J.; Yang, J.-Y. A two-phase test sample sparse representation method for use with face recognition. IEEE Transactions on Circuits and Systems for Video Technology Vol. 21, No. 9, 1255-1262, 2011.
DOI Google Scholar
[11]
Zhong, D.; Zhu, P.; Han, J.; Li, S. An improved robust sparse coding for face recognition with disguise. International Journal of Advanced Robotic Systems Vol. 9, 126, 2012.
DOI Google Scholar
[12]
Xu, Y.; Zhu, Q.; Zhang, D. Combine crossing matching scores with conventional matching scores for bimodal biometrics and face and palmprint recognition experiments. Neurocomputing Vol. 74, No. 18, 3946-3952, 2011.
DOI Google Scholar
[13]
Yang, A. Y.; Zhou, Z.; Balasubramanian, A. G.; Sastry, S. S.; Ma, Y. Fast l1-minimization algorithms for robust face recognition. IEEE Transactions on Image Processing Vol. 22, No. 8, 3234-3246, 2013.
DOI Google Scholar
[14]
Mairal, J.; Bach, F.; Ponce, J.; Sapiro, G.; Zisserman, A. Discriminative learned dictionaries for local image analysis. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008.
DOI
[15]
Gao, S.; Tsang, I. W.-H.; Chia, L.-T. Kernel sparse representation for image classification and face recognition. In: Computer Vision—ECCV 2010. Daniilidis, K.; Maragos, P.; Paragios, N. Eds. Springer Berlin Heidelberg, 1-14, 2010.
DOI
[16]
Yang, J.; Wright, J.; Huang, T.; Ma, Y. Image super-resolution as sparse representation of raw image patches. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008.
[17]
Zhang, Z.; Li, Z.; Xie, B.; Wang, L.; Chen, Y. Integrating globality and locality for robust representation based classification. Mathematical Problems in Engineering Vol. 2014, Article No. 415856, 2014.
DOI Google Scholar
[18]
Zhang, L.; Yang, M.; Feng, X. Sparse representation or collaborative representation: Which helps face recognition? In: Proceedings of IEEE International Conference on Computer Vision, 471-478, 2011.
DOI
[19]
Zhang, L.; Yang, M.; Feng, X.; Ma, Y.; Zhang, D. Collaborative representation based classification for face recognition. arXiv preprint arXiv:1204.2358, 2012.
Google Scholar
[20]
Chen, X.; Ramadge, P. J. Collaborative representation, sparsity or nonlinearity: What is key to dictionary based classification? In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 5227-5231, 2014.
DOI
[21]
Li, W.; Du, Q.; Zhang, B. Combined sparse and collaborative representation for hyperspectral target detection. Pattern Recognition Vol. 48, No. 12, 3904-3916, 2015.
DOI Google Scholar
[22]
Feng, Q.; Pan, J.-S.; Yan, L. Restricted nearest feature line with ellipse for face recognition. Journal of Information Hiding and Multimedia Signal Processing Vol. 3, No. 3, 297-305, 2012.
Google Scholar
[23]
Elhamifar, E.; Vidal, R. Sparse subspace clustering: Algorithm, theory, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 35, No. 11, 2765-2781, 2013.
DOI Google Scholar
[24]
Beck, A.; Teboulle, M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences Vol. 2, No. 1, 183-202, 2009.
DOI Google Scholar
[25]
Zhang, Z.; Xu, Y.; Yang, J.; Li, X.; Zhang, D. A survey of sparse representation: Algorithms and applications. IEEE Access Vol. 3, 490-530, 2015.
DOI Google Scholar
[26]
Xu, Z.; Zhang, H.; Wang, Y.; Chang, X.; Liang, Y. L1/2 regularization. Science China Information Sciences Vol. 53, No. 6, 1159-1169, 2010.
DOI Google Scholar
[27]
Yang, M.; Zhang, L. Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary. In: Computer Vision—–ECCV 2010. Daniilidis, K.; Maragos, P.; Paragios, N. Eds. Springer Berlin Heidelberg, 448-461, 2010.
DOI
[28]
Cheng, B.; Yang, J.; Yan, S.; Fu, Y.; Shuang, T. S. Learning with l1-graph for image analysis. IEEE Transactions on Image Processing Vol. 19, No. 4, 858-866, 2010.
DOI Google Scholar
[29]
Qiao, L.; Chen, S.; Tan, X. Sparsity preserving projections with applications to face recognition. Pattern Recognition Vol. 43, No. 1, 331-341, 2010.
DOI Google Scholar
[30]
Yang, J.; Yu, K.; Gong, Y.; Huang, T. Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1794-1801, 2009.
[31]
Deng, W.; Hu, J.; Guo, J. In defense of sparsity based face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 399-406, 2013.
DOI
[32]
Xu, Y.; Zhu, X.; Li, Z.; Liu, G.; Lu, Y.; Liu, H. Using the original and ‘symmetrical face’ training samples to perform representation based two-step face recognition. Pattern Recognition Vol. 46, No. 4, 1151-1158, 2013.
DOI Google Scholar
[33]
Xu, Y.; Zhang, Z.; Lu, G.; Yang, J. Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification. Pattern Recognition Vol. 54, 68-82, 2016.
DOI Google Scholar
[34]
Liu, Z.; Song, X.; Tang, Z. Fusing hierarchical multi-scale local binary patterns and virtual mirror samples to perform face recognition. Neural Computing and Applications Vol. 26, No. 8, 2013-2026, 2015.
DOI Google Scholar
[35]
Lee, H.; Battle, A.; Raina, R.; Ng, A. Y. Efficient sparse coding algorithms. In: Proceedings of Advances in Neural Information Processing Systems, 801-808, 2006.
[36]
AT&T Laboratories Cambridge. The database of faces. 2002. Available at http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
[37]
Computer Vision Online. Georgia Tech face database. 2015. Available at http://www.computervisiononline.com/dataset/1105138700.
[38]
NIST Information Technology Laboratory. Color FERET database. 2016. Available at https://www.nist.gov/itl/iad/image-group/color-feret-database.
[39]
Xu, Y.; Zhang, B.; Zhong, Z. Multiple representations and sparse representation for image classification. Pattern Recognition Letters Vol. 68, 9-14, 2015.
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Revised: 05 May 2016
Accepted: 22 September 2016
Published: 15 November 2016
Issue date: December 2016

Copyright

© The Author(s) 2016

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61502208), the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20150522), the Scientific and Technical Program of City of Huizhou (Grant No. 2012-21), the Research Foundation of Education Bureau of Guangdong Province of China (Grant No. A314.0116), and the Scientific Research Starting Foundation for Ph.D. in Huizhou University (Grant No. C510.0210).

Rights and permissions

This article is published with open access at Springerlink.com

The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.

Return