19 January 2006 Face recognition with independent component-based super-resolution
Author Affiliations +
Performance of current face recognition algorithms reduces significantly when they are applied to low-resolution face images. To handle this problem, super-resolution techniques can be applied either in the pixel domain or in the face subspace. Since face images are high dimensional data which are mostly redundant for the face recognition task, feature extraction methods that reduce the dimension of the data are becoming standard for face analysis. Hence, applying super-resolution in this feature domain, in other words in face subspace, rather than in pixel domain, brings many advantages in computation together with robustness against noise and motion estimation errors. Therefore, we propose new super-resolution algorithms using Bayesian estimation and projection onto convex sets methods in feature domain and present a comparative analysis of the proposed algorithms with those already in the literature.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Osman Gokhan Sezer, Osman Gokhan Sezer, Yucel Altunbasak, Yucel Altunbasak, Aytul Ercil, Aytul Ercil, } "Face recognition with independent component-based super-resolution", Proc. SPIE 6077, Visual Communications and Image Processing 2006, 607705 (19 January 2006); doi: 10.1117/12.645868; https://doi.org/10.1117/12.645868


Creation of North East Indian face database for human face...
Proceedings of SPIE (February 19 2013)
A review on face recognition techniques
Proceedings of SPIE (January 28 2013)
Motion adaptive Kalman filter for super-resolution
Proceedings of SPIE (January 31 2011)
Improvised super-resolution algorithm for face recognition
Proceedings of SPIE (October 30 2009)
Evaluation of face recognition techniques
Proceedings of SPIE (July 10 2009)
Face recognition experiments with random projection
Proceedings of SPIE (March 28 2005)

Back to Top