A Novel Low Rank Spectral Clustering Method for Face Identification

Author(s): Danhua Xu, Chuan Li, Teng Chen, Fei Lang*.

Journal Name: Recent Patents on Engineering

Volume 13 , Issue 4 , 2019

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Graphical Abstract:


Abstract:

Background: Low rank is a recent significant model to explore the inner subspace structure of samples, which has been successfully used in many pattern recognition tasks.

Methods: In this paper, we proposed a novel spectral clustering method to address the face identification problem. There are three main contributions in our paper. Firstly, the sparse coding under a cluster-based learned dictionary is taken as the character sample of each face. Secondly, the collaborative low rank representation is incorporated in the comprehensive optimization framework to construct an effective affinity graph iteratively, which is different from the conventional ones to tackle the graph construction and spectral clustering independently. We revised all the patents relating to the face identification. Thirdly, a numerical algorithm is developed to solve the optimization framework and obtain a stable solution.

Results: The experimental results showed the superior performance of the proposed method on recognition ratio.

Conclusion: It means that our proposed low rank based identification algorithm outperforms the existed excellent methods.

Keywords: Face identification, spectral clustering, low rank, affinity graph, numerical algorithm, identification problem.

[1]
W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, "Face recognition: A literature survey", ACM Comput. Surv., vol. 35, pp. 399-458, 2003.
[2]
Y. Mu, J. Dong, X. Yuan, and S. Yan, "Accelerated low-rank visual recovery by random projection In ", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CO, USA 2011
[3]
J. Lu, Y.P. Tan, and G. Wang, "Discriminative multimanifold analysis for face recognition from a single training sample per person", IEEE T. Pattern Anal., vol. 35, pp. 39-51, 2013.
[4]
J. Lu, V.E. Liong, X. Zhou, and J. Zhou, "Learning Compact Binary Face Descriptor for Face Recognition", IEEE T. Pattern Anal., vol. 37, pp. 2041-2056, 2015.
[5]
M. Turk, and A. Pentland, "Eigenfaces for recognition", J. Cogn. Neurosci., vol. 3, pp. 71-86, 1991.
[6]
P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, "Eigenfaces vs. fisherfaces: recognition using class specific linear projection", IEEE T. Pattern Anal., vol. 19, pp. 711-720, 1997.
[7]
D. Zhang, S. Chen, and Z.H. Zhou, "A new face recognition method based on svd perturbation for single example image per person", Appl. Math. Comput., vol. 163, p. 895, 2005.
[8]
S. Chen, J. Liu, and Z.H. Zhou, "Making FLDA applicable to face recognition with one sample per person", Pattern Recognit., vol. 37, pp. 1553-1555, 2004.
[9]
G. Sun, Z. Song, and J. Liu, Feature selection method based on maximum information coefficient and approximate markov blanket. Zidonghua Xuebao/acta Automatica Sinica , vol. 43. pp. 795-805. 2009
[10]
G. Sun, S. Li, and T. Chen, "Active learning method for chinese spam filtering", Int. J. Perform. Eng., vol. 13, pp. 511-518, 2017.
[11]
X.F. He, and P. Niyogi, "Locality preserving projections", Adv. Neural Inf. Process. Syst., vol. 16, pp. 153-160, 2004.
[12]
Z. Xu, and X. Tian, "Locality preserving fisher discriminant analysis for face recognition In ", International Conference on Intelligent Computing Shanghai, China 2009
[13]
J. Wrigh, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma, "Robust face recognition via sparse representation", IEEE T. Pattern Anal., vol. 3, pp. 210-227, 2009.
[14]
L. Zhang, M. Yang, and X. Feng, "Sparse representation or collaborative representation: Which helps face recognition? In ", IEEE International Conference on Computer Vision Barcelona, Spain 2011
[15]
G. Liu, Z. Lin, S. Yan, J. Sun, Y. Yu, and Y. Ma, "Robust recovery of subspace structures by low-rank representation IEEE T", Pattern Anal., vol. 35, pp. 171-184, 2013.
[16]
Y. Ming, S. Cai, and J. Gao, "Robust face recognition via double low-rank matrix recovery for feature extraction In ", Proceedings of the IEEE Conference on image processing Melbourne, Australia 2013
[17]
H. Li, and C.Y. Suen, "Robust face recognition based on dynamic rank representation", Pattern Recognit., vol. 60, pp. 13-24, 2006.
[18]
M. Aharon, M. Elad, and A. Bruckstein, "The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation", IEEE Trans. Signal Process., vol. 54, pp. 4311-4322, 2006.
[19]
T. Jebara, J. Wang, and S.F. Chang, "Graph construction and b-matching for semi-supervised learning In ", Proceedings of 26th Annual International Conference on Machine Learning QC, Canada 2009
[20]
J. Tang, R. Hong, S. Yan, T.S. Chua, G.J. Qi, and R. Jain, "Image annotation by k NN-sparse graph-based label propagation over noisily tagged web images", ACM T. Intel. Syst. Tec., vol. 2, pp. 135-136, 2011.
[21]
G. Liu, Z. Lin, and Y. Yu, "Robust subspace segmentation by low-rank representation In ", International Conference on Machine Learning Haifa, Israel 2010
[22]
X. Ren, and Z. Lin, "Linearized alternating direction method with adaptive penalty and warm starts for fast solving transform invariant low-rank textures", Int. J. Comput. Vis., vol. 104, pp. 1-14, 2013.
[23]
J. Yang, D.L. Chu, L. Zhang, Y. Xu, and J.Y. Yang, "Sparse representation classifier steered discriminative projection with application to face recognition", IEEE T. Neur. Net. Lear., vol. 24, pp. 1023-1035, 2013.
[24]
E. Elhamifar, and R. Vidal, "Sparse subspace clustering In ", IEEE Conference on Vision and Pattern Recognition FL, USA 2009
[25]
G.C. Liu, and S.C. Yan, "Latent low-rank representation for subspace segmentation and feature extraction In ", IEEE International Conference on Computer Vision Barcelona, Spain 2011


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Article Details

VOLUME: 13
ISSUE: 4
Year: 2019
Page: [387 - 394]
Pages: 8
DOI: 10.2174/1872212112666180828124211
Price: $58

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