Paper
4 May 2016 Face recognition with L1-norm subspaces
Author Affiliations +
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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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CITATIONS
Cited by 11 scholarly publications.
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KEYWORDS
Facial recognition systems

Databases

Algorithm development

Detection and tracking algorithms

Resistance

Feature extraction

Principal component analysis

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