Springer, Lecture Notes in Computer Science, p. 31-40, 2013
DOI: 10.1007/978-3-642-41184-7_4
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Face recognition in presence of either occlusions, illumination changes or large expression variations is still an open problem. This paper addresses this issue presenting a new local-based face recognition system that combines weak classifiers yielding a strong one. The method relies on sparse approximation using dictionaries built on a pool of local features extracted from automatically cropped images. Experiments on the AR database show the effectiveness of our method, which outperforms current state-of-the art techniques.