Stacked progressive auto-encoders (SPAE) for face recognition across poses

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2014, Pages 1883-1890

  Kan, Meina ^a   Shan, Shiguang ^a   Chang, Hong ^a   Chen, Xilin ^a  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

Author keywords

Deep network; face recognition across pose; Stacked Progressive Auto Encoders

Indexed keywords

FACE RECOGNITION; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; SIGNAL ENCODING;

AUTO ENCODERS; FACE IMAGES; FRONTAL FACES; HIDDEN LAYERS; POSE ESTIMATION; POSE VARIATION; TEST IMAGES; VIRTUAL VIEW;

GESTURE RECOGNITION;

EID: 84911383347     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.243     Document Type: Conference Paper

References (28)

1. S. R. Arashloo and J. Kittler. Energy normalization for pose-invariant face recognition based on mrf model image matching. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33(6):1274-1280, 2011.
2. A. B. Ashraf, S. Lucey, and T. Chen. Learning patch correspondences for improved viewpoint invariant face recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1-8, 2008.
3. A. Asthana, T. K. Marks, M. J. Jones, K. H. Tieu, and M. Ro-hith. Fully automatic pose-invariant face recognition via 3d pose normalization. In IEEE International Conference on Computer Vision (ICCV), pages 937-944, 2011.
4. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman. Eigen-faces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 19(7):711-720, 1997.
5. Y. Bengio. Learning deep architectures for ai. Foundations and trends® in Machine Learning, 2(1):1-127, 2009.
6. Y. Bengio. Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade, pages 437-478, 2012.
7. D. Beymer and T. Poggio. Face recognition from one example view. In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), pages 500-507, 1995.
8. V. Blanz and T. Vetter. Face recognition based on fitting a 3d morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 25(9):1063-1074, 2003.
9. C. D. Castillo and D. W. Jacobs. Wide-baseline stereo for face recognition with large pose variation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 537-544, 2011.
10. X. Chai, S. Shan, X. Chen, and W. Gao. Locally linear regression for pose-invariant face recognition. IEEE Transactions on Image Processing (TIP), 16(7):1716-1725, 2007.
11. R. Gross, I. Matthews, and S. Baker. Eigen light-fields and face recognition across pose. In IEEE International Conference on Automatic Face and Gesture Recognition (FG), pages 1-7, 2002.
12. R. Gross, I. Matthews, J. Cohn, T. kanada, and S. Baker. The cmu multi-pose, illumination, and expression (multi-pie) face database. Technical report, Carnegie Mellon U-niversity Robotics Institute. TR-07-08, 2007.
13. G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507, 2006.
14. H. Hotelling. Relations between two sets of variates. Biometrika, 28(3/4):321-377, 1936.
15. H. Li, G. Hua, Z. Lin, J. Brandt, and J. Yang. Probabilistic elastic matching for pose variant face verification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3499-3506, 2013.
16. S. Li, X. Liu, X. Chai, H. Zhang, S. Lao, and S. Shan. Morphable displacement field based image matching for face recognition across pose. In European Conference on Computer Vision (ECCV), pages 102-115, 2012.
17. P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss. The fer-et evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 22(10):1090-1104, 2000.
18. U. Prabhu, J. Heo, and M. Savvides. Unconstrained poseinvariant face recognition using 3d generic elastic models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33(10):1952-1961, 2011.
19. S. J. Prince, J. H. Elder, J. Warrell, and F. M. Felisberti. Tied factor analysis for face recognition across large pose differences. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 30(6):970-984, 2008.
20. A. Sharma, M. A. Haj, J. Choi, L. S. Davis, and D. W. Jacobs. Robust pose invariant face recognition using coupled latent space discriminant analysis. Computer Vision and Image Understanding (CVIU), pages 1095-1110, 2012.
21. A. Sharma and D. W. Jacobs. Bypassing synthesis: Pls for face recognition with pose, low-resolution and sketch. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 593-600, 2011.
22. A. Sharma, A. Kumar, H. D. III, and D. W. Jacobs. Generalized multiview analysis: A discriminative latent space. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
23. M. A. Turk and A. P. Pentland. Face recognition using eigen-faces. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 591, pages 586-591, 1991.
24. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research (JMLR), 9999:3371-3408, 2010.
25. X. Xiong and F. D. la Torre. Supervised descent method and its applications to face alignment. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 532-539, 2013.
26. X. Zhang and Y. Gao. Face recognition across pose: A review. Pattern Recognition (PR), 42(11):2876-2896, 2009.
27. Y. Zhang, M. Shao, E. K. Wong, and Y. Fu. Random faces guided sparse many-to-one encoder for pose-invariant face recognition. In IEEE International Conference on Computer Vision (ICCV), pages 2416-2423, 2013.
28. Z. Zhu, P. Luo, X. Wang, and X. Tang. Deep learning identity preserving face space. In IEEE International Conference on Computer Vision (ICCV), pages 113-120, 2013.