Face.evoLVe: A High-Performance Face Recognition Library

While face recognition has drawn much attention, a large number of algorithms and models have been proposed with applications to daily life, such as authentication for mobile payments, etc. Recently, deep learning methods have dominated in the field of face recognition with advantages in comparisons to conventional approaches and even the human perception. Despite the popular adoption of deep learning-based methods to the field, researchers and engineers frequently need to reproduce existing algorithms with unified implementations (i.e., the identical deep learning framework with standard implementations of operators and trainers) and compare the performance of face recognition methods under fair settings (i.e., the same set of evaluation metrics and preparation of datasets with tricks on/off), so as to ensure the reproducibility of experiments. To the end, we develop face.evoLVe