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Face Recognition with Independent Component Based Super-resolution
 

Summary: Face Recognition with Independent Component Based
Super-resolution
Osman Gokhan Sezer,a
, Yucel Altunbasakb
, Aytul Ercila
a
Faculty of Engineering and Natural Sciences, Sabanci Univ., Istanbul, Turkiye, 34956
b
School of Elec. and Comp. Eng. , Georgia Inst. of Tech., Atlanta, GA, USA, 30332-0250
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.
Keywords: Face recognition, super resolution, independent component analysis, projection onto convex sets, bayesian
estimation.

  

Source: Altunbasak, Yucel - School of Electrical and Computer Engineering, Georgia Institute of Technology

 

Collections: Engineering