Face recognition with L1-norm subspaces

Federica Maritato; Ying Liu; Stefania Colonnese; Dimitris A. Pados

doi:10.1117/12.2224953

4 May 2016 Face recognition with L1-norm subspaces

Federica Maritato, Ying Liu, Stefania Colonnese, Dimitris A. Pados

Proceedings Volume 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics; 98570L (2016) https://doi.org/10.1117/12.2224953
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Federica Maritato, Ying Liu, Stefania Colonnese, and Dimitris A. Pados "Face recognition with L1-norm subspaces", Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 98570L (4 May 2016); https://doi.org/10.1117/12.2224953

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