Driven by graphics processing units (GPUs), massive amounts of annotated data and more advanced algorithms, deep learning has recently taken the computer vision community by storm and has benefited real-world applications, including face recognition (FR). Deep FR methods leverage deep networks to learn more discriminative representations, significantly improving the state of the art and surpassing human performance (97.53%). In this paper, we provide a comprehensive survey of deep FR methods, including data, algorithms and scenes. First, we summarize the commonly used datasets for training and testing. Then, the data preprocessing methods are categorized into two classes: "one-to-many augmentation" and "many-to-one normalization". Second, for algorithms, we summarize different network architectures and loss functions used in the state-of-the art methods. Third, we review several scenes in deep FR, such as video FR, 3D FR and cross-age FR. Finally, some potential deficiencies of the current methods and several future directions are highlighted.
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