Abstract
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.
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Adamo, A., Grossi, G., Lanzarotti, R. (2013). Face Recognition in Uncontrolled Conditions Using Sparse Representation and Local Features. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41184-7_4
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DOI: https://doi.org/10.1007/978-3-642-41184-7_4
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