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Proceedings Article

Face recognition with independent component-based super-resolution

[+] Author Affiliations
Osman G. Sezer, Aytul Ercil

Sabanci Univ. (Turkey)

Yucel Altunbasak

Georgia Institute of Technology

Proc. SPIE 6077, Visual Communications and Image Processing 2006, 607705 (January 18, 2006); doi:10.1117/12.645868
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From Conference Volume 6077

  • Visual Communications and Image Processing 2006
  • John G. Apostolopoulos; Amir Said
  • San Jose, CA | January 15, 2006

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.

© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Osman G. Sezer ; Yucel Altunbasak and Aytul Ercil
"Face recognition with independent component-based super-resolution", Proc. SPIE 6077, Visual Communications and Image Processing 2006, 607705 (January 18, 2006); doi:10.1117/12.645868; http://dx.doi.org/10.1117/12.645868


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