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 superresolution 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 superresolution 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
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