The 2013 face recognition evaluation in mobile environment
Manuel Günther, Artur Costa‐Pazo, C. Ding, Elhocine Boutellaa, Giovani Chiachia, Honglei Zhang, Marcus de Assis Angeloni, Vitomir Štruc, E. Khoury, Esteban Vázquez-Fernández, D. Tao, Messaoud Bengherabi, Dennis D. Cox, Serkan Kıranyaz, Tiago de Freitas Pereira, Jerneja Žganec Gros, Enrique Argones-Rúa, Nicolas Pinto, Moncef Gabbouj, Flávio O. Simões · 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 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.