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 — a comprehensive library that collects and implements a wide range of popular deep learningbased methods for face recognition. First of all, face.evoLVe is composed of key components that cover the full process of face analytics, including face alignment, data processing, various backbones, losses, and alternatives with bags of tricks for improving performance. Later, face.evoLVe supports multi-GPU training on top of different deep learning platforms, such as PyTorch and PaddlePaddle, which facilitates researchers to work on both large-scale datasets with millions of images and and low-shot counterparts with limited well-annotated data. More importantly, along with face.evoLVe, images before & after alignment in the common benchmark datasets are released with source codes and trained models provided. All these efforts lower the technical burdens in reproducing the existing methods for comparison, while users of our library could focus on developing advanced approaches more efficiently. Last but not least, face.evoLVe is well designed and vibrantly evolving, so that new face recognition approaches can be easily plugged into our framework. Note that we have used face.evoLVe to participate in a number of face recognition competitions and secured the first place. The version that supports PyTorch [51] is publicly available at https://github.com/ZhaoJ9014/face.evoLVe.PyTorch and the PaddlePaddle [45] version is available at https://github.com/ ZhaoJ9014/face.evoLVe.PyTorch/tree/master/paddle. Face.evoLVe has been widely used for face analytics, receiving 2.4K stars and 622 forks. ∗Equally contribute to this work. †Corresponding authors: Jian Zhao (Homepage: https://zhaoj9014.github.io) and Haoyi Xiong. Figure 1: Face analytics from a scene of “The Big Bang The-