Metric Learning in the Dissimilarity Space to Improve Low Resolution Face Recognition
Standard face recognition methods based on a feature representations are not suitable for low resolution environments. Therefore low resolution face recognition is still an unsolved problem where the best approaches still obtain very low recognition rates. In this paper, we propose a low resolution face recognition method using the dissimilarity representation. In addition, we propose the use of metric learning methods to replace the standard Euclidean distance in the dissimilarity space. The effectiveness of our proposal is tested on two different data sets, one of them is the SCface database which is very challenging since the images were collected from surveillance cameras.
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