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Weighted two-phase supervised sparse representation based on Gaussian for face recognition
1. | College of Computer Science & Technology, Zhejiang University of Technology, Hang Zhou, China, China |
References:
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, Available, from: , (). Google Scholar |
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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.
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M. Debruyne and T. Verdonck, Robust kernel principal component analysis and classification,, Adv. Data Anal. Classification, 4 (2010), 151.
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X. Fan, Learning a Hierarchy of Classifiers for Multi-Class Shape Detection,, Ph.D. dissertation, (2006). Google Scholar |
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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. |
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E. Frenod, An attempt at classifying homogenization-based numerical methods,, Discrete Contin. Dyn. Syst. Ser. S, 8 (2015).
|
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S. Gangaputra and D. Geman, A design principle for coarse-to-fine classification,, in Proc. IEEE Comput. Soc. Conf. CVPR, (2006), 1877.
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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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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 |
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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. |
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P. J. Phillips, The Facial Recognition Technology (FERET) Database [Online]., Available from: , (). Google Scholar |
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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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
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. |
[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. |
[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. |
[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. |
[7] |
E. Frenod, An attempt at classifying homogenization-based numerical methods,, Discrete Contin. Dyn. Syst. Ser. S, 8 (2015).
|
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
[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. |
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