4 May 2016 Face recognition with L1-norm subspaces
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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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Federica Maritato, Federica Maritato, Ying Liu, Ying Liu, Stefania Colonnese, Stefania Colonnese, Dimitris A. Pados, 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); doi: 10.1117/12.2224953; https://doi.org/10.1117/12.2224953
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