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.