Share Email Print

Proceedings Paper

Face recognition with independent component-based super-resolution
Author(s): Osman Gokhan Sezer; Yucel Altunbasak; Aytul Ercil
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 19 January 2006
PDF: 15 pages
Proc. SPIE 6077, Visual Communications and Image Processing 2006, 607705 (19 January 2006); doi: 10.1117/12.645868
Show Author Affiliations
Osman Gokhan Sezer, Sabanci Univ. (Turkey)
Yucel Altunbasak, Georgia Institute of Technology (United States)
Aytul Ercil, Sabanci Univ. (Turkey)

Published in SPIE Proceedings Vol. 6077:
Visual Communications and Image Processing 2006
John G. Apostolopoulos; Amir Said, Editor(s)

© SPIE. Terms of Use
Back to Top