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Proceedings Paper

Sparse representation based face recognition using weighted regions
Author(s): Emil Bilgazyev; E. Yeniaras; I. Uyanik; Mahmut Unan; E. L. Leiss
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Paper Abstract

Face recognition is a challenging research topic, especially when the training (gallery) and recognition (probe) images are acquired using different cameras under varying conditions. Even a small noise or occlusion in the images can compromise the accuracy of recognition. Lately, sparse encoding based classification algorithms gave promising results for such uncontrollable scenarios. In this paper, we introduce a novel methodology by modeling the sparse encoding with weighted patches to increase the robustness of face recognition even further. In the training phase, we define a mask (i.e., weight matrix) using a sparse representation selecting the facial regions, and in the recognition phase, we perform comparison on selected facial regions. The algorithm was evaluated both quantitatively and qualitatively using two comprehensive surveillance facial image databases, i.e., SCfaceandMFPV, with the results clearly superior to common state-of-the-art methodologies in different scenarios. Publisher’s Note: This paper, originally published on 24 December 2013, was replaced with a revised version on 11 June 2014. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance.

Paper Details

Date Published: 24 December 2013
PDF: 6 pages
Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90671K (24 December 2013); doi: 10.1117/12.2051683
Show Author Affiliations
Emil Bilgazyev, Univ. of Houston (United States)
E. Yeniaras, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
I. Uyanik, Univ. of Houston (United States)
Mahmut Unan, Univ. of Houston (United States)
E. L. Leiss, Univ. of Houston (United States)

Published in SPIE Proceedings Vol. 9067:
Sixth International Conference on Machine Vision (ICMV 2013)
Branislav Vuksanovic; Antanas Verikas; Jianhong Zhou, Editor(s)

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