Dissimilarity representations for low-resolution face recognition

Low-resolution face recognition is a very difficult problem. In this setup, the training database or gallery contains high-resolution images, but the image to be recognized is of low resolution. Thus we are dealing with a resolution mismatch problem for training and test images. Standard face recognition methods fail in this setting, which suggests that current feature representation approaches are not adequate to cope with this problem. Therefore, we propose the use of dissimilarity representations based on different strategies, which differ in how images with different resolutions are compared, to solve the resolution mismatch problem. Experiments on four standard face datasets demonstrate that a strategy based on first down-scaling and afterwards up-scaling training images while up-scaling test images outperforms all the other approaches.

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