The 2013 face recognition evaluation in mobile environment

Gunther, M; Costa-Pazo, A; Ding, C; Boutellaa, E; Chiachia, G; Zhang, H; De Assis Angeloni, M; Struc, V; Khoury, E; Vazquez-Fernandez, E; Tao, D; Bengherabi, M; Cox, D; Kiranyaz, S; De Freitas Pereira, T; Zganec-Gros, J; Argones-Rua, E; Pinto, N; Gabbouj, M; Simoes, F; Dobrisek, S; Gonzalez-Jimenez, D; Rocha, A; Neto, MU; Pavesic, N; Falcao, A; Violato, R; Marcel, S

The 2013 face recognition evaluation in mobile environment

Gunther, M Costa-Pazo, A Ding, C Boutellaa, E Chiachia, G Zhang, H De Assis Angeloni, M Struc, V Khoury, E Vazquez-Fernandez, E Tao, D

Bengherabi, M Cox, D Kiranyaz, S De Freitas Pereira, T Zganec-Gros, J Argones-Rua, E Pinto, N Gabbouj, M Simoes, F Dobrisek, S Gonzalez-Jimenez, D Rocha, A Neto, MU Pavesic, N Falcao, A Violato, R Marcel, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2013 International Conference on Biometrics, ICB 2013, 2013
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Gunther, M	en_US
dc.contributor.author	Costa-Pazo, A	en_US
dc.contributor.author	Ding, C	en_US
dc.contributor.author	Boutellaa, E	en_US
dc.contributor.author	Chiachia, G	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	De Assis Angeloni, M	en_US
dc.contributor.author	Struc, V	en_US
dc.contributor.author	Khoury, E	en_US
dc.contributor.author	Vazquez-Fernandez, E	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Bengherabi, M	en_US
dc.contributor.author	Cox, D	en_US
dc.contributor.author	Kiranyaz, S	en_US
dc.contributor.author	De Freitas Pereira, T	en_US
dc.contributor.author	Zganec-Gros, J	en_US
dc.contributor.author	Argones-Rua, E	en_US
dc.contributor.author	Pinto, N	en_US
dc.contributor.author	Gabbouj, M	en_US
dc.contributor.author	Simoes, F	en_US
dc.contributor.author	Dobrisek, S	en_US
dc.contributor.author	Gonzalez-Jimenez, D	en_US
dc.contributor.author	Rocha, A	en_US
dc.contributor.author	Neto, MU	en_US
dc.contributor.author	Pavesic, N	en_US
dc.contributor.author	Falcao, A	en_US
dc.contributor.author	Violato, R	en_US
dc.contributor.author	Marcel, S	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Proceedings - 2013 International Conference on Biometrics, ICB 2013, 2013	en_US
dc.identifier.isbn	9781479903108	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43299
dc.description.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 UC-HU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2013 International Conference on Biometrics, ICB 2013	en_US
dc.relation.isbasedon	10.1109/ICB.2013.6613024	en_US
dc.title	The 2013 face recognition evaluation in mobile environment	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

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 UC-HU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources. © 2013 IEEE.

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http://hdl.handle.net/10453/43299