%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{Gunther_Idiap-RR-36-2013,
         author = {G{\"{u}}nther, Manuel and Costa-Pazo, Artur and Ding, Changxing and Boutellaa, Elhocine and Chiachia, Giovani and Zhang, Honglei and de Assis Angeloni, Marcus and Struc, Vitomir and Khoury, Elie and Vazquez-Fernandez, Esteban and Tao, Dacheng and Bengherabi, Messaoud and Cox, David and Kiranyaz, Serkan and de Freitas Pereira, Tiago and Zganec-Gros, Jerneja and Argones-R{\'{u}}a, Enrique and Pinto, Nicolas and Gabbouj, Moncef and Sim{\~{o}}es, Fl{\'{a}}vio and Dobrisek, Simon and Gonz{\'{a}}lez-Jim{\'{e}}nez, Daniel and Rocha, Anderson and Uliani Neto, M{\'{a}}rio and Pavesic, Nikola and Falc{\~{a}}o, Alexandre and Violato, Ricardo and Marcel, S{\'{e}}bastien},
       projects = {Idiap, BBfor2, BEAT},
          month = {11},
          title = {The 2013 Face Recognition Evaluation in Mobile Environment},
           type = {Idiap-RR},
         number = {Idiap-RR-36-2013},
           year = {2013},
    institution = {Idiap},
       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 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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2013/Gunther_Idiap-RR-36-2013.pdf}
}