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Proceedings Paper

Face recognition with L1-norm subspaces
Author(s): Federica Maritato; Ying Liu; Stefania Colonnese; Dimitris A. Pados
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Paper 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.

Paper Details

Date Published: 4 May 2016
PDF: 8 pages
Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 98570L (4 May 2016); doi: 10.1117/12.2224953
Show Author Affiliations
Federica Maritato, La Sapienza Univ. of Rome (Italy)
Ying Liu, The State Univ. of New York at Buffalo (United States)
Stefania Colonnese, La Sapienza Univ. of Rome (Italy)
Dimitris A. Pados, The State Univ. of New York at Buffalo (United States)


Published in SPIE Proceedings Vol. 9857:
Compressive Sensing V: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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