Face recognition with independent component based super-resolution

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 6077, Issue , 2006, Pages

  Sezer, Osman Gokhan ^a,c   Altunbasak, Yucel ^b   Erci, Aytul ^a  

^a SABANCI UNIVERSITY   (Turkey)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^c BROWN UNIVERSITY   (United States)

Author keywords

Bayesian estimation; Face recognition; Independent component analysis; Projection onto convex sets; Super resolution

Indexed keywords

BAYESIAN ESTIMATION; FEATURE EXTRACTION METHODS; PROJECTION ONTO CONVEX SETS; SUPER RESOLUTION;

ERROR ANALYSIS; ESTIMATION; FEATURE EXTRACTION; IMAGE ANALYSIS; INDEPENDENT COMPONENT ANALYSIS; OPTICAL RESOLVING POWER;

FACE RECOGNITION;

EID: 33645997018     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.645868     Document Type: Conference Paper

References (28)

1. T. E. Boult, M.-C. Chiang, and R. J. Micheals, Super-Resolution via Image Warping, Super-Resolution Imaging, Ed. S. Chaudhuri, Boston: Kluwer Academic Publishers, 2002, pp. 131-169.
2. S. Baker and T. Kanade, Hallucinating Faces, Fourth International Conf. Automatic Face and Gesture Recognition, March 2000.
3. C. Liu, H.-Y. Shum, and C.-S. Zhang, A Two-Step Approach to Hallucinating Faces: Global Parametric Model and Local Nonparametric Model, Proc. IEEE Conf. Computer Vision and Pattern Recognition, December 2001.
4. D. Capel and A. Zisserman, Super-Resolution from Multiple Views Using Learnt Image Models, Proc. IEEE Conf. Computer Vision and Pattern Recognition, December 2001.
5. B. K. Gunturk, A.U. Batur, Y. Altunbasak, M. H. Hayes III, and R. M. Mersereau, Eigenface-based Super-resolution for Face Recognition, Proc. International Conf. on Image Processing, 2002
6. Makeig, S., Bell, A. J., Jung, T., & Sejnowski, T. J. (1996). Independent component analysis of electroencephalographic data. In D. Touretzky, M. Moser, & M. Hasselmo (Eds.), Advances in neural information processing systems, 8(pp. 145-151). Cambridge, MA: MIT Press.
7. P. O. Hoyer and A. Hyvarinen, Feature Extraction from Colour and Stereo Images, Network: Computation in Neural Systems 11: 191-210,August, 2000
8. J. Lee, H. Jung, T. Lee, and S. Lee, Speech feature extraction using independent component analysis, IEEE ICASSP, June 2000, vol. 3, pp. 1631-1634.
9. M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, Face Recognition by Independent Component Analysis, IEEE Trans. On Neural Networks, vol. 13, no. 6, pp. 1450-1464, 2002
10. C. Liu, and H. Wechsler, Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition, Proc. Second International Conf. on Audio- and Video-based Biometric Person authentication, March 1999.
11. J.-P. Nadal and N. Parga, Non-linear neurons in the low noise limit: A factorial code maximizes information transfer, Network, vol. 5, pp.565-581, 1994.
12. M. Turk and A. Pentland, Eigenfaces for Recognition, J. Cognitive Neuroscience, vol. 3, no. 1, 1991
13. Sirovich, L. and Kirby, M., Low dimensional procedure for the characterization of human faces, J. of the Optical Society of America, vol. 4, no. 3, pp. 519-524, 1987.
14. A. Hyvarinen, Survey on independent component analysis, Neural Computing Surveys, 2, (1999), 94-128.
15. T. Sim, S. Baker, and M. Bsat, The CMU Pose, Illumination, and Expression (PIE) Database, Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, 2002.
16. A.J. Patti, M. I. Sezan, and A. M. Tekalp, Super-resolution video reconstruction with arbitrary sampling lattice and nonzero aperture time, IEEE Trans. Image Processing, vol. 6, Aug. 1997, pp:1064-1076.
17. Schultz, R. R. and Stevenson, R. L., "Extraction of high-resolution frames from video sequences," IEEE Trans. Image Processing, vol. 5, pp. 996-1011, June 1996.
18. Elad, M. and Feuer, A., "Restoration of a single superresolution image from several blurred, noisy and undersampled measured images," IEEE Trans. Image Processing, vol. 6, pp. 1646-1658, December 1997.
19. M.I. Sezan, An overview of convex projections theory and its application to image recovery problem, Ultramicroscopy, vol. 40, 1992, pp:55-67.
20. H. Stark and Y. Yang, Vector Space Projections: A numerical Approach to Signal and Image Processing, Neural Nets, and Optics, Wiley series in Telecommunications and Signal Processing, 1998
21. H. J. Trussell and Mehmet R. Civanlar, The feasible solution in signal processing, IEEE Trans. Acous., Speech, and Signal Process., vol. ASSP-32, no. 2, April 1984, pp:201-212.
22. M2VTS Multimodal Face Database Release 1.00, http://www.tele.ucl.ac.be/ M2VTS
23. M. Bierling and R. Thoma, Motion Compensating Field Interpolation Using A Hierarchically Structured Displacement Estimator, Signal Processing, 11, 1986, pp: 387-404.
24. P.E. Eren, M. I. Sezan, and A. M. Tekalp, Robust, Object-Based High-Resolution Image Reconstruction from Low-Resolution Video, IEEE Trans. on Image Processing, vol.6, no. 10, October 1997, pp: 1446-1452.
25. M. Irani, and S. Peleg, Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency, J. Visual Commun. Image Represent., vol. 4, Dec. 1993, pp:324-335.
26. O. G. Sezer, Superresolution Techniques for Face Recognition from Video, M.Sc. Thesis, Sabanci University, Istanbul, Turkiye, 2005
27. H. B. Barlow, Unsupervised learning, Neural Comput., vol. 1,1989, pp.295-311.
28. J. J. Atick, Could information theory provide an ecological theory of sensory processing?,Network, vol. 3, 1992, pp. 213-251.