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A Regularized Framework for Feature Selection in Face Detection and Authentication

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Abstract

This paper proposes a general framework for selecting features in the computer vision domain—i.e., learning descriptions from data—where the prior knowledge related to the application is confined in the early stages. The main building block is a regularization algorithm based on a penalty term enforcing sparsity. The overall strategy we propose is also effective for training sets of limited size and reaches competitive performances with respect to the state-of-the-art. To show the versatility of the proposed strategy we apply it to both face detection and authentication, implementing two modules of a monitoring system working in real time in our lab. Aside from the choices of the feature dictionary and the training data, which require prior knowledge on the problem, the proposed method is fully automatic. The very good results obtained in different applications speak for the generality and the robustness of the framework.

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Authors and Affiliations

  1. DISI, Università degli Studi di Genova, Via Dodecaneso 35, 16146, Genova, Italy

    Augusto Destrero, Francesca Odone & Alessandro Verri

  2. Department of Mathematics and ECARES, Universite Libre de Bruxelles, Campus Plaine CP 217, Boulevard du Triomphe, 1050, Brussels, Belgium

    Christine De Mol

  3. Imavis s.r.l., Via Greto di Cornigliano 6R, 16152, Genova, Italy

    Augusto Destrero

Authors
  1. Augusto Destrero
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  2. Christine De Mol
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  3. Francesca Odone
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  4. Alessandro Verri
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Correspondence to Francesca Odone.

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Destrero, A., De Mol, C., Odone, F. et al. A Regularized Framework for Feature Selection in Face Detection and Authentication. Int J Comput Vis 83, 164–177 (2009). https://doi.org/10.1007/s11263-008-0180-2

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  • DOI: https://doi.org/10.1007/s11263-008-0180-2

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