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Structured multi-class feature selection for effective face recognition
Year: 2013 Keywords: Regularized feature selection, Group LASSO, multi-class feature selection, face recognition
Authors: Giovanni Fusco, Luca Zini, Nicoletta Noceti, Francesca Odone  
Book title: IEEE Int. Conference on Image Analysis and Processing
Series: LNCS
(Winner of the IAPR Best Paper Award at ICIAP 2013)
This paper addresses the problem of real time face recogni- tion in unconstrained environments from the analysis of low quality video frames. It focuses in particular on finding an effective and fast to com- pute (that is, sparse) representation of faces, starting from classical Local Binary Patterns (LBPs). The two contributions of the paper are a new formulation of Group LASSO for structured feature selection (MCGroup LASSO) to cope directly with multi-class settings, and a face recogni- tion pipeline based on a representation derived from MC-GrpLASSO. We present an extensive experimental analysis on two benchmark datasets, MOBO and Choke Point, and on a more complex dataset acquired in- house over a large temporal span. We compare our results with state-of- the-art approaches and show the superiority of our method in terms of both performances and sparseness of the obtained solution.
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