Open Access
Issue
MATEC Web of Conferences
Volume 44, 2016
2016 International Conference on Electronic, Information and Computer Engineering
Article Number 01039
Number of page(s) 4
Section Computer, Algorithm, Control and Application Engineering
DOI https://doi.org/10.1051/matecconf/20164401039
Published online 08 March 2016
  1. Jafri, R., Arabnia, H. R. A survey of face recognitiontechniques. Jips, 11 (19) (2009) 29–33 [Google Scholar]
  2. Turk, M., Pentland, A, Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3 (1) (2007) 71–86 [CrossRef] [Google Scholar]
  3. Belhumeur, P. N., Hespanha, J. P., Kriegman, D. J. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 19 (7) (1997) 711–720 [CrossRef] [Google Scholar]
  4. Chien, J. T., Wu, C. C, Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24 (12) (2002) 1644–1649 [CrossRef] [Google Scholar]
  5. Fidler, S., Sko aj, D., Leonardis, A. Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling. IEEE Trans. on Pattern Analysis and Machine Intelligence, 28 (3) (2006) 337–350 [CrossRef] [Google Scholar]
  6. J.Wright, A.Y.Yang, A.Ganesh, S.S. Sastry, Y. Ma, Robust face recognition via sparse representation, IEEE Trans. on Pattern Analysis and Machine Intelligence, 31 (2) (2009) 210–227 [CrossRef] [Google Scholar]
  7. B. Cheng, J. Yang, S. Yan, Y. Fu, and T. Huang. Learning with 11-graph for image analysis. IEEE Trans. Image Processing, 19 (4) (2010) 858–866 [CrossRef] [Google Scholar]
  8. S. Gao, I. Tsang, L. Chia, Kernel sparse representation for image classification and face recognition. In ECCV, 6314 (2010) 1–14 [Google Scholar]
  9. M. Yang, L. Zhang. Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary. In ECCV, 6316 (2010) 448–461. [Google Scholar]
  10. M. Yang, L. Zhang, J. Yang, D. Zhang. Robust sparse coding for face recognition. IEEE, 42 (7) (2011) 625–632 [Google Scholar]
  11. Naseem, I., Togneri, R., Bennamoun, M. Linear regression for face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 32 (11) (2010) 2106–2112. [CrossRef] [Google Scholar]
  12. X. Luan, B. Fang, Face recognition with contiguous occlusion using linear regression and level set method. Neurocomputing, 122 (2013) 386–397 [CrossRef] [Google Scholar]
  13. X. Luan, B. Fang, Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusion. Pattern Recognition, 47 (2) (2014) 495–508 [CrossRef] [Google Scholar]
  14. L. Zhang, M. Yang, X. Feng, Sparse representation or collaborative representation: Which helps face recognition? Proceedings of the 2011 International Conference on Computer Vision IEEE Computer Society, (2011) 471–478 [Google Scholar]
  15. A. Georghiades, P. Belhumeur, D. Kriegman, From few to many: Illumination cone models for face recognition under variable lighting and pose, IEEE Trans. on Pattern Analysis and Machine Intelligence, 23 (2001) 643–660 [CrossRef] [Google Scholar]
  16. A. Martinez, R. Benavente, The AR face database, CVC Tech. Report. 24, 1998. [Google Scholar]

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