We consider the problem of representing individual faces by maximum L1-norm projection subspaces calculated from available face-image ensembles. In contrast to conventional L2-norm subspaces, L1-norm subspaces are seen to offer significant robustness to image variations, disturbances, and rank selection. Face recognition becomes then the problem of associating a new unknown face image to the “closest,” in some sense, L1 subspace in the database. In this work, we also introduce the concept of adaptively allocating the available number of principal components to different face image classes, subject to a given total number/budget of principal components. Experimental studies included in this paper illustrate and support the theoretical developments.

Face recognition with L1-norm subspaces / Maritato, Federica; Liu, Ying; Colonnese, Stefania; Pados, Dimitris A.. - STAMPA. - 9857(2016), p. 98570L. ((Intervento presentato al convegno Conference on Compressive Sensing V - From Diverse Modalities to Big Data Analytics tenutosi a Baltimore, Maryland, United States nel April 17, 2016 [10.1117/12.2224953].

Face recognition with L1-norm subspaces

COLONNESE, Stefania;
2016

Abstract

We consider the problem of representing individual faces by maximum L1-norm projection subspaces calculated from available face-image ensembles. In contrast to conventional L2-norm subspaces, L1-norm subspaces are seen to offer significant robustness to image variations, disturbances, and rank selection. Face recognition becomes then the problem of associating a new unknown face image to the “closest,” in some sense, L1 subspace in the database. In this work, we also introduce the concept of adaptively allocating the available number of principal components to different face image classes, subject to a given total number/budget of principal components. Experimental studies included in this paper illustrate and support the theoretical developments.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/876111
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