American Institute of Mathematical Sciences

Advanced
December  2015, 8(6): 1385-1400. doi: 10.3934/dcdss.2015.8.1385

Weighted two-phase supervised sparse representation based on Gaussian for face recognition

Shuhua Xu 1, and  Fei Gao 1,

1. 

College of Computer Science & Technology, Zhejiang University of Technology, Hang Zhou, China, China

Received  April 2015 Revised  September 2015 Published  December 2015

As recently newly techniques, two-phase sparse representation algorithms have been presented, which achieve an excellent performance in face recognition via different phase sparse representation, capturing more local structural information of samples. However, there are some defects in these algorithms:1) The Euclidean distance metric applied in these algorithms fails to capture nonlinear structural information, leading to that the performance of these algorithms is sensitive to the geometric structure of facial images. 2) To select the m nearest neighbors of the test sample is achieved directly by applying sparse representation in training samples, which ignores prior information to construct the sparse representation model. In order to solve these problems, a Weighted Two-Phase Supervised Sparse Representation based on Gaussian (GWTPSSR) algorithm is proposed on basic of existing two-phase sparse representation algorithm, in which the nonlinear local information of samples is captured by exploiting effectively the Gaussian distance metric instead of the Euclidean distance metric. Besides, GWTPSSR recreates reconstruction set from training samples in the sparse representation model for each test sample, making full use of prior information to eliminate some training samples far from the test sample. Compared with existing two-phase sparse representation algorithms, experimental results on standard face datasets show that GWTPSSR has better robustness and classification performance.
Keywords: face recognition., Gaussian kernel distance, prior information, local structure, Sparse representation.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C3.
Citation: Shuhua Xu, Fei Gao. Weighted two-phase supervised sparse representation based on Gaussian for face recognition. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1385-1400. doi: 10.3934/dcdss.2015.8.1385
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X. Luan, B. Fang, L. Liu, W. Yang and J. Qian, Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusion,, Pattern Recognition, 47 (2014), 495.  doi: 10.1016/j.patcog.2013.06.031.  Google Scholar

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M. Sugiyama, Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis,, J. Mach. Learn. Res., 8 (2007), 1027.   Google Scholar

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D. Tao , X. Li , X. Wu and S. J. Maybank, Geometric mean for subspace selection,, IEEE Trans. Pattern Anal. Mach. Intell., 31 (2009), 260.   Google Scholar

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D. Tao and X. Tang, Kernel full-space biased discriminant analysis,, in 2004 IEEE International Conference on Multimedia and Expo, (2004), 1287.  doi: 10.1109/ICME.2004.1394460.  Google Scholar

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X. Xiang, J. Yang and Q. Chen, Color face recognition by PCA-like approach,, Neurocomputing, 152 (2015), 231.  doi: 10.1016/j.neucom.2014.10.074.  Google Scholar

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Y. Xu and D. Zhang, Represent and fuse bimodal biometric images at the feature level: Complex-matrix-based fusion scheme,, Opt. Eng., 49 (2010).   Google Scholar

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Y. Xu, D. Zhang, F. Song, J.-Y. Yang, Z. Jing and M. Li, A method for speeding up feature extraction based on KPCA,, Neurocomputing, 70 (2007), 1056.  doi: 10.1016/j.neucom.2006.09.005.  Google Scholar

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Y. Xu, D. Zhang, J. Yang and J.-Y.Yang, A two-phase test sample sparse representation method for use with face recognition,, IEEE Trans. Circuits Syst. Video Technol., 21 (2011), 1255.   Google Scholar

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Y. Xu and Q. Zhu, A simple and fast representation-based face recognition method,, Neural Comput Appl., 22 (2013), 1543.   Google Scholar

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Y. Xu, W. Zuo and Z. Fan, Supervised sparse presentation method with a heuristic strategy and face recognition experiments,, Neurocomputing, 79 (2011), 125.   Google Scholar

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H. Yan, J. Lu, X. Zhou and Y. Shang, Multi-feature multi-manifold learning for single-sample face recognition,, Neurocomputing, 143 (2014), 134.  doi: 10.1016/j.neucom.2014.06.012.  Google Scholar

[43]

J. Yang, D. Zhang, A. F. Frangi and J.-Y. Yang, Two-dimensional PCA: A new approach to appearance-based face representation and recognition,, IEEE Trans. Pattern Anal. Mach. Intell., 26 (2004), 131.   Google Scholar

[44]

M. Yang, L. Zhang, J. Yang and D. Zhang, Robust sparse coding for face recognition,, in IEEE International Conference Computer Vision and Pattern Recognition, (2011), 625.   Google Scholar

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K. Yu and T. Zhang, Improved local coordinate coding using local tangents,, in International Conference on Machine Learning, (2010), 1215.   Google Scholar

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K. Yu, T. Zhang and Y. Gong, Nonlinear learning using local coordinate coding,, Adv. Neural Inf. Process. Syst., 22 (2009), 2223.   Google Scholar

[47]

Y. Zeng, Y. Yang and L. Zhao, Nonparametric classification based on local mean and class statistics,, Expert Syst. Appl., 36 (2009), 8443.  doi: 10.1016/j.eswa.2008.10.041.  Google Scholar

[48]

L. Zhang, et al., Sparse representation or collaborative representation: Which helps face recognition?,, in 2011 IEEE International Conference on Computer Vision (ICCV), (2011), 471.  doi: 10.1109/ICCV.2011.6126277.  Google Scholar

[49]

C. Zhou, L. Wang, Q. Zhang and X. Wei, Face recognition based on PCA and logistic regression analysis,, Optik., 125 (2014), 5916.  doi: 10.1016/j.ijleo.2014.07.080.  Google Scholar

show all references

References:
[1]

, Available, from: , ().   Google Scholar

[2]

A. Back and E. Frenod, Geometric two-scale convergence on manifold and applications to the vlasov equation,, Discrete Contin. Dyn. Syst. Ser. S, 8 (2015), 223.  doi: 10.3934/dcdss.2015.8.223.  Google Scholar

[3]

H. Beirao da Veiga and F. Crispo, On the global regularity for nonlinear systems of the p-laplacian type,, Discrete Contin. Dyn. Syst. Ser. S, 6 (2013), 1173.  doi: 10.3934/dcdss.2013.6.1173.  Google Scholar

[4]

M. Debruyne and T. Verdonck, Robust kernel principal component analysis and classification,, Adv. Data Anal. Classification, 4 (2010), 151.  doi: 10.1007/s11634-010-0068-1.  Google Scholar

[5]

X. Fan, Learning a Hierarchy of Classifiers for Multi-Class Shape Detection,, Ph.D. dissertation, (2006).   Google Scholar

[6]

Z. Fan, M. Ni, Q. Zhu and E. Liu, Weighted sparse representation for face recognition,, Neurocomputing, 151 (2015), 304.  doi: 10.1016/j.neucom.2014.09.035.  Google Scholar

[7]

E. Frenod, An attempt at classifying homogenization-based numerical methods,, Discrete Contin. Dyn. Syst. Ser. S, 8 (2015).   Google Scholar

[8]

S. Gangaputra and D. Geman, A design principle for coarse-to-fine classification,, in Proc. IEEE Comput. Soc. Conf. CVPR, (2006), 1877.  doi: 10.1109/CVPR.2006.21.  Google Scholar

[9]

S. Gao, I. Tsang and L.-T. Chia, Kernel sparse representation for image classification and face recognition,, in Computer Vision ECCV (eds. K. Daniilidis, (2010), 1.   Google Scholar

[10]

J. Huang, K. Su, J. El-Den, T. Hu and J. Li, An MPCA/LDA based dimensionality reduction algorithm for face recognition,, Mathematical Problems in Engineering, 2014 (2014), 1.  doi: 10.1155/2014/393265.  Google Scholar

[11]

S. Huang, J. Ye, T. Wang, L. Jiang, X. Wu and Y. Li, Extracting refined low-rank features of robust pca for human action recognition,, Computer Engineering and Computer Science, 40 (2015), 1427.  doi: 10.1007/s13369-015-1635-8.  Google Scholar

[12]

M. Kirby and L. Sirovich, Application of the KL phase for the characterization of human faces,, IEEE Trans. Pattern Anal. Mach. Intell., 12 (1990), 103.   Google Scholar

[13]

Z. Lai, Z. Jin, J. Yang and W. K. Wong, Sparse local discriminant projections for feature extraction,, in Proceedings of ICPR, (2010), 926.   Google Scholar

[14]

Z. Y. Liu, K. C. Chiu and L. Xu, Improved system for object detection and star/galaxy classification via local subspace analysis,, Neural Netw., 16 (2003), 437.  doi: 10.1016/S0893-6080(03)00015-7.  Google Scholar

[15]

Z. Liu, J. Pu, M. Xu and Y. Qiu, Face recognition via weighted two phase test sample sparse representation,, Neural Process. Lett., 41 (2015), 43.  doi: 10.1007/s11063-013-9333-6.  Google Scholar

[16]

C.-Y. Lu, H. Min, J. Gui, L. Zhu and Y.-K. Lei, Face recognition via weighted sparse representation,, J. Vis. Commun. Image Represent., 24 (2013), 111.   Google Scholar

[17]

X. Luan, B. Fang, L. Liu, W. Yang and J. Qian, Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusion,, Pattern Recognition, 47 (2014), 495.  doi: 10.1016/j.patcog.2013.06.031.  Google Scholar

[18]

K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda and B. Scholkopf, An introduction to kernel-based learning algorithms,, IEEE Trans. Neural Netw., 12 (2001), 181.  doi: 10.1109/72.914517.  Google Scholar

[19]

M. Murtaza, M. Sharif, M. Raza and J. Hussain Shah, Face Recognition Using Adaptive Margin Fisher's Criterion and Linear Discriminant Analysis (AMFC-LDA),, The International Arab Journal of Information Technology, 11 (2014), 149.   Google Scholar

[20]

S. W. Park and M. Savvides, A multifactor extension of linear discriminant analysis for face recognition under varying pose and illumination,, EURASIP J. Adv. Signal Process., 2010 (2010).  doi: 10.1155/2010/158395.  Google Scholar

[21]

P. J. Phillips, The Facial Recognition Technology (FERET) Database [Online]., Available from: , ().   Google Scholar

[22]

P. J. Phillips, H. Moon, S. A. Rizvi and P. J. Rauss, The FERET evaluation methodology for face-recognition algorithms,, in 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (1997), 137.  doi: 10.1109/CVPR.1997.609311.  Google Scholar

[23]

H. Ryu, J.-C. Yoon, S. S. Chun and S. Sull, Coarse-to-fine classification for image-based face detection,, in Image and Video Retrieval, (2006), 291.  doi: 10.1007/11788034_30.  Google Scholar

[24]

F. Samaria and A. Harter, Parameterisation of a stochastic model for human face identification,, in Second IEEE workshop on Applications of Computer Vision, (1994), 138.  doi: 10.1109/ACV.1994.341300.  Google Scholar

[25]

B. Scholkopf and A. Smola, Learning with Kernels,, MIT Press, (2002).   Google Scholar

[26]

Q. Shi, A. Eriksson, A. Hengel and C. Shen, Is face recognition really a compressive sensing problem?,, in 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2011), 553.  doi: 10.1109/CVPR.2011.5995556.  Google Scholar

[27]

M. Sugiyama, Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis,, J. Mach. Learn. Res., 8 (2007), 1027.   Google Scholar

[28]

D. Tao , X. Li , X. Wu and S. J. Maybank, Geometric mean for subspace selection,, IEEE Trans. Pattern Anal. Mach. Intell., 31 (2009), 260.   Google Scholar

[29]

D. Tao and X. Tang, Kernel full-space biased discriminant analysis,, in 2004 IEEE International Conference on Multimedia and Expo, (2004), 1287.  doi: 10.1109/ICME.2004.1394460.  Google Scholar

[30]

M. E. Tipping, Sparse kernel principal component analysis,, in in Neural Information Processing Systems (eds. T. K. Leen, (2000), 633.   Google Scholar

[31]

V. Vural, G. Fung, B. Krishnapuram, J. G. Dy and B. Rao, Using local dependencies within batches to improve large margin classifiers,, J. Mach. Learn. Res., 10 (2009), 183.   Google Scholar

[32]

L. Wang, H. Wu and C. Pan, Manifold regularized local sparse representation for face recognition,, Ieee Transactions on Circuits and Systems for Video Technology, 25 (2015), 651.  doi: 10.1109/TCSVT.2014.2335851.  Google Scholar

[33]

J. Wright, Y. Ma and J. Mairal, et al., Sparse representation for computer vision and pattern recognition,, in Proceedings of IEEE, (2009), 1.   Google Scholar

[34]

J. Wright, A. Y. Yang and A. Ganesh, et al., Robust face recognition via sparse representation,, IEEE Trans. Pattern Anal. Mach. Intell., 31 (2009), 210.   Google Scholar

[35]

X. Xiang, J. Yang and Q. Chen, Color face recognition by PCA-like approach,, Neurocomputing, 152 (2015), 231.  doi: 10.1016/j.neucom.2014.10.074.  Google Scholar

[36]

Y. Xu and D. Zhang, Represent and fuse bimodal biometric images at the feature level: Complex-matrix-based fusion scheme,, Opt. Eng., 49 (2010).   Google Scholar

[37]

Y. Xu, D. Zhang, F. Song, J.-Y. Yang, Z. Jing and M. Li, A method for speeding up feature extraction based on KPCA,, Neurocomputing, 70 (2007), 1056.  doi: 10.1016/j.neucom.2006.09.005.  Google Scholar

[38]

Y. Xu, D. Zhang, J. Yang and J.-Y. Yang, An approach for directly extracting features from matrix data and its application in face recognition,, Neurocomputing, 71 (2008), 1857.  doi: 10.1016/j.neucom.2007.09.021.  Google Scholar

[39]

Y. Xu, D. Zhang, J. Yang and J.-Y.Yang, A two-phase test sample sparse representation method for use with face recognition,, IEEE Trans. Circuits Syst. Video Technol., 21 (2011), 1255.   Google Scholar

[40]

Y. Xu and Q. Zhu, A simple and fast representation-based face recognition method,, Neural Comput Appl., 22 (2013), 1543.   Google Scholar

[41]

Y. Xu, W. Zuo and Z. Fan, Supervised sparse presentation method with a heuristic strategy and face recognition experiments,, Neurocomputing, 79 (2011), 125.   Google Scholar

[42]

H. Yan, J. Lu, X. Zhou and Y. Shang, Multi-feature multi-manifold learning for single-sample face recognition,, Neurocomputing, 143 (2014), 134.  doi: 10.1016/j.neucom.2014.06.012.  Google Scholar

[43]

J. Yang, D. Zhang, A. F. Frangi and J.-Y. Yang, Two-dimensional PCA: A new approach to appearance-based face representation and recognition,, IEEE Trans. Pattern Anal. Mach. Intell., 26 (2004), 131.   Google Scholar

[44]

M. Yang, L. Zhang, J. Yang and D. Zhang, Robust sparse coding for face recognition,, in IEEE International Conference Computer Vision and Pattern Recognition, (2011), 625.   Google Scholar

[45]

K. Yu and T. Zhang, Improved local coordinate coding using local tangents,, in International Conference on Machine Learning, (2010), 1215.   Google Scholar

[46]

K. Yu, T. Zhang and Y. Gong, Nonlinear learning using local coordinate coding,, Adv. Neural Inf. Process. Syst., 22 (2009), 2223.   Google Scholar

[47]

Y. Zeng, Y. Yang and L. Zhao, Nonparametric classification based on local mean and class statistics,, Expert Syst. Appl., 36 (2009), 8443.  doi: 10.1016/j.eswa.2008.10.041.  Google Scholar

[48]

L. Zhang, et al., Sparse representation or collaborative representation: Which helps face recognition?,, in 2011 IEEE International Conference on Computer Vision (ICCV), (2011), 471.  doi: 10.1109/ICCV.2011.6126277.  Google Scholar

[49]

C. Zhou, L. Wang, Q. Zhang and X. Wei, Face recognition based on PCA and logistic regression analysis,, Optik., 125 (2014), 5916.  doi: 10.1016/j.ijleo.2014.07.080.  Google Scholar

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