Fusion of local normalization and Gabor entropy weighted features for face identification
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Face recognition is one of the most extensively studied topics in image analysis because of its wide range of possible applications such as in surveillance, access control, content-based video search, human–computer interaction, electronic advertisement and more. Face identification is a one-to-n matching problem where a captured face is compared to n samples in a database. In this work we propose two new methods for face identification. The first one combines entropy-like weighted Gabor features with the local normalization of Gabor features. The second fuses the entropy-like weighted Gabor features at the score level with the local binary pattern (LBP) applied to the magnitude (LGBP) and phase (LGXP) components of the Gabor features. We used the FERET, AR, and FRGC 2.0 databases to test and compare our results with those previously published. Results on these databases show significant improvement relative to previously published results, reaching the best performance on the FERET and AR databases. Our methods also showed significant robustness to slight pose variations. We tested the proposed methods assuming noisy eye detection to check their robustness to inexact face alignment. Results show that the proposed methods are robust to errors of up to 3 pixels in eye detection.
Artículo de publicación ISI
This research was funded in part by FONDECYT1120613, FONDEFD08I-1060 and the Department of Electrical Engineering, University of Chile (Universidad de Chile).
DOI: DOI: 10.1016/j.patcog.2013.09.003
Quote ItemPattern Recognition Volume 47, Issue 2, February 2014, Pages 568–577