Deep learning for face recognition on mobile devices

Abstract

Mobility implies a great variability of capturing conditions, which is not easy to control and directly affects to face detection and the extraction of facial features. Deep learning solutions seem to be the most interesting choice for automatic face recognition, but they are highly dependent on the model generated during the training stage. In addition, the size of the models makes it difficult for their integration into applications oriented to mobile devices, particularly when the model must be embedded. In this work, a small-size deep-learning model was trained for face recognition on low capacity devices and evaluated in terms of accuracy, size and timings to provide quantitative data. This evaluation is aimed to cover as many scenarios as possible, so different databases were employed, including public and private datasets specifically oriented to recreate the complexity of mobile scenarios. Also, publicly available models and traditional approaches were included in the evaluation to carry out a fair comparison. Moreover, given the relevance of template matching and face detection stages, the assessment is complemented with different classifiers and detectors. Finally, a JAVA-Android implementation of the system was developed and evaluated to obtain performance data of the whole system integrated on a mobile phone.