The 2013 face recognition evaluation in mobile environment. Proceedings of the IAPR International Conference on Biometrics (ICB), IAPR 2013.
Abstract
Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UCHU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources.
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@conference{gunther20132013,
title = {The 2013 face recognition evaluation in mobile environment},
author = {Manuel G\"{u}nther and Artur Costa-Pazo and Changxing Ding and Elhocine Boutellaa and Giovani Chiachia and Honglei Zhang and Marcus de Assis Angeloni and Vitomir \v{S}truc and Elie Khoury and Esteban Vazquez-Fernandez and others},
url = {http://luks.fe.uni-lj.si/nluks/wp-content/uploads/2016/09/Gunther_ICB2013_2013.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of the IAPR International Conference on Biometrics (ICB)},
pages = {1--7},
organization = {IAPR},
abstract = {Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UCHU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources.},
keywords = {biometrics, competition, face recognition, face verification, group evaluation, mobile biometrics, MOBIO, performance evaluation},
pubstate = {published},
tppubtype = {conference}
}