Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition-Reference-Cited by-同舟云学术

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Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition

Published:2008-08-19 Issue:2 Volume:81 Page:191-204
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Harandi Mehrtash T.,Nili Ahmadabadi Majid,Araabi Babak N.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s11263-008-0161-5.pdf

Reference37 articles.

1. Ahonen, T., Hadid, A., & Pietikäinen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037–2041.

2. Bartlett, M. S., Movellan, J. R., & Sejnowski, T. J. (2002). Face recognition by independent component analysis. IEEE Transactions on Neural Network, 13(6), 1450–1464.

3. Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711–720.

4. Bicego, M., Lagorio, A., Grosso, E., & Tistarelli, M. (2006). On the use of SIFT features for face authentication. In IEEE international workshop on biometrics, in association with CVPR.

5. Cai, D., He, X., Han, J., & Zhang, H. (2006). Orthogonal Laplacianfaces for face recognition. IEEE Transactions on Image Processing, 15(11), 3608–3614.

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