Subspace-based Super-resolution for Face Recognition from Video

Performance of current face recognition algorithms reduces significantly when they are applied to low-resolution face images. To handle this problem, superresolution techniques can be applied either in the pixel domain or in the face subspace. Since face images are high dimensional data which are mo...

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Bibliographic Details
Main Authors: Sezer, O.G., Altunbasak, Y., Ercil, A.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Bayesian methods
Data mining
Face recognition
Motion estimation
Noise robustness
Online Access:Request full text
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Summary:Performance of current face recognition algorithms reduces significantly when they are applied to low-resolution face images. To handle this problem, superresolution 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 and those already in the literature
ISSN:2165-0608
2693-3616
DOI:10.1109/SIU.2006.1659905