We present a facial recognition technique based on facial sparse representation. A dictionary is learned from data, and patches\r\nextracted from a face are decomposed in a sparse manner onto this dictionary.We particularly focus on the design of dictionaries\r\nthat play a crucial role in the final identification rates. Applied to various databases and modalities, we show that this approach\r\ngives interesting performances. We propose also a score fusion framework that allows quantifying the saliency classifiers outputs\r\nand merging them according to these saliencies
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