Pose normalization via learned 2D warping for fully automatic face recognition

BMVC 2011 - Proceedings of the British Machine Vision Conference 2011

Volumn , Issue , 2011, Pages

  Asthana, Akshay ^a,b   Jones, Michael J ^a   Marks, Tim K ^a   Tieu, Kinh H ^a,c   Goecke, Roland ^b,d  

^a MITSUBISHI ELECTRIC RESEARCH LABORATORIES   (United States)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c Heartland Robotics ^*  (Australia)

^d UNIVERSITY OF CANBERRA   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DATABASE SYSTEMS; THREE DIMENSIONAL;

AUTOMATIC FACE RECOGNITION; AUTOMATIC SYSTEMS; FACIAL FEATURE POINTS; GAUSSIAN PROCESS REGRESSION; MANUAL INTERVENTION; NONLINEAR MAPPING FUNCTIONS; POSE NORMALIZATION; POSE-INVARIANT FACE RECOGNITION;

FACE RECOGNITION;

EID: 84898468284     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.25.127     Document Type: Conference Paper

References (22)

1. A. A. Amini, T. E. Weymouth, and R. C. Jain. Using dynamic programming for solving variational problems in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 12:855-867, September 1990.
2. A. Asthana, C. Sanderson, T. Gedeon, and R. Goecke. Learning-based face synthesis for pose-robust recognition from single image. In BMVC, 2009.
3. S. Baker, R. Gross, and I. Matthews. Lucas-Kanade 20 years on: A unifying framework: Part 3. Technical report, Robotics Institute, Carnegie Mellon University, USA, 2003.
4. V. Blanz and T. Vetter. A morphable model for the synthesis of 3D faces. In ACM SIGGRAPH. 1999.
5. V. Blanz and T. Vetter. Face Recognition Based on Fitting a 3D Morphable Model. IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI), 25(9):1063-1074, September 2003.
6. X. Chai, S. Shan, X. Chen, and W. Gao. Locally linear regression for pose-invariant face recognition. IEEE Transactions in Image Processing (TIP), 16(7):1716-1725, 2007.
7. T.F. Cootes, K.N. Walker, and C.J. Taylor. View-Based Active Appearance Models. In IEEE FG, 2000.
8. S. Du and R. Ward. Component-wise pose normalization for pose-invariant face recognition. In IEEE ICASSP, 2009.
9. G. Edwards, C.J. Taylor, and T.F. Cootes. Interpreting Face Images Using Active Appearance Models. In IEEE FG, 1998.
10. H. Gao, H. K. Ekenel, and R. Stiefelhagen. Pose normalization for local appearance-based face recognition. In ICB, 2009.
11. G. H. Golub and C. F. Van Loan. Matrix Computations. Johns Hopkins University Press, 1989.
12. R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker. Multi-PIE. In IEEE FG, 2008.
13. T. Kanade and A. Yamada. Multi-Subregion Based Probabilistic Approach Toward Pose-Invariant Face Recognition. In IEEE CIRA, 2003.
14. I. Matthews and S. Baker. Active Appearance Models Revisited. International Journal of Computer Vision (IJCV), 60(2):135-164, 2004.
15. C.E. Rasmussen and C.K.I. Williams. Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA, 2006.
16. O. Rudovic, I. Patras, and M. Pantic. Coupled gaussian process regression for poseinvariant facial expression recognition. In ECCV. 2010.
17. J. M. Saragih and R. Goecke. Learning AAM fitting through simulation. Pattern Recognition (PR), 42(11):2628-2636, November 2009.
18. M. S. Sarfraz. Towards Automatic Face Recognition in Unconstrained Scenarios. PhD thesis, Technische Universität Berlin, 2008.
19. M. S. Sarfraz and O. Hellwich. Probabilistic learning for fully automatic face recognition across pose. Image Vision Computing (IVC), 28:744-753, May 2010.
20. T. Sim, S. Baker, and M. Bsat. The CMU Pose, Illumination, and Expression Database. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 25(12): 1615-1618, 2003.
21. P. Viola and M. J. Jones. Robust real-time face detection. International Journal of Computer Vision (IJCV), 57:137-154, 2004.
22. W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang. Local gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition. IEEE ICCV, 2005.