Structured multi-class feature selection with an application to face recognition

In this paper we address the problem of structured feature selection in a multi-class classification setting. Our goal is to select groups of features meaningful to all classes simultaneously, and to this purpose we derive a new formulation of Group LASSO -- the MC-GrpLASSO -- and a solution of the obtained functional based on proximal methods. We then apply the algorithm to a typical multi-class problem -- face recognition. On this respect we focus on finding an effective and fast to compute (that is, sparse) representation of faces, detected in low quality videos of unconstrained environments. We start from a classical over-complete representation based on Local Binary Patterns (LBPs), descriptors endowed with a characteristic internal structure that can be preserved by selecting features in groups. We present an extensive experimental analysis on two benchmark datasets, MOBO and Choke Point, and on a more complex set of data 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.