In this paper we present a new local-based face recognition system that combines weak classifiers to create a robust system able to recognize faces in presence of either occlusions or large expression variations. The method relies on sparse approximation using dictionaries built on local features. Experiments on the AR database show the effectiveness of our method, which achieves better performance than those obtained by the state-of-the-art ℓ1 norm-based sparse representation classifier (SRC).
Local features and sparse representation for face recognition with partial occlusions / A. Adamo, G. Grossi, R. Lanzarotti (PROCEEDINGS - INTERNATIONAL CONFERENCE ON IMAGE PROCESSING). - In: 2013 IEEE International Conference on Image ProcessingPiscataway : IEEE, 2013. - ISBN 9781479923410. - pp. 3008-3012 (( Intervento presentato al 20. convegno International Conference on Image Processing (ICIP) tenutosi a Melbourne nel 2013.
Local features and sparse representation for face recognition with partial occlusions
G. Grossi;R. Lanzarotti
2013
Abstract
In this paper we present a new local-based face recognition system that combines weak classifiers to create a robust system able to recognize faces in presence of either occlusions or large expression variations. The method relies on sparse approximation using dictionaries built on local features. Experiments on the AR database show the effectiveness of our method, which achieves better performance than those obtained by the state-of-the-art ℓ1 norm-based sparse representation classifier (SRC).File | Dimensione | Formato | |
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