CONF Gunther_ICB2013_2013/IDIAP The 2013 Face Recognition Evaluation in Mobile Environment Günther, Manuel Costa-Pazo, Artur Ding, Changxing Boutellaa, Elhocine Chiachia, Giovani Zhang, Honglei de Assis Angeloni, Marcus Struc, Vitomir Khoury, Elie Vazquez-Fernandez, Esteban Tao, Dacheng Bengherabi, Messaoud Cox, David Kiranyaz, Serkan de Freitas Pereira, Tiago Zganec-Gros, Jerneja Argones-Rúa, Enrique Pinto, Nicolas Gabbouj, Moncef Simões, Flávio Dobrisek, Simon González-Jiménez, Daniel Rocha, Anderson Uliani Neto, Mário Pavesic, Nikola Falcão, Alexandre Violato, Ricardo Marcel, Sébastien EXTERNAL http://publications.idiap.ch/attachments/papers/2013/Gunther_ICB2013_2013.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/Gunther_Idiap-RR-36-2013 Related documents The 6th IAPR International Conference on Biometrics 2013 REPORT Gunther_Idiap-RR-36-2013/IDIAP The 2013 Face Recognition Evaluation in Mobile Environment Günther, Manuel Costa-Pazo, Artur Ding, Changxing Boutellaa, Elhocine Chiachia, Giovani Zhang, Honglei de Assis Angeloni, Marcus Struc, Vitomir Khoury, Elie Vazquez-Fernandez, Esteban Tao, Dacheng Bengherabi, Messaoud Cox, David Kiranyaz, Serkan de Freitas Pereira, Tiago Zganec-Gros, Jerneja Argones-Rúa, Enrique Pinto, Nicolas Gabbouj, Moncef Simões, Flávio Dobrisek, Simon González-Jiménez, Daniel Rocha, Anderson Uliani Neto, Mário Pavesic, Nikola Falcão, Alexandre Violato, Ricardo Marcel, Sébastien EXTERNAL http://publications.idiap.ch/attachments/reports/2013/Gunther_Idiap-RR-36-2013.pdf PUBLIC Idiap-RR-36-2013 2013 Idiap November 2013 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 eight different participants using two verification metrics. Most submitted algorithms rely on on 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 ptimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources.