  Deep learning based face recognition has achieved significant progress in
recent years. Yet, the practical model production and further research of deep
face recognition are in great need of corresponding public support. For
example, the production of face representation network desires a modular
training scheme to consider the proper choice from various candidates of
state-of-the-art backbone and training supervision subject to the real-world
face recognition demand; for performance analysis and comparison, the standard
and automatic evaluation with a bunch of models on multiple benchmarks will be
a desired tool as well; besides, a public groundwork is welcomed for deploying
the face recognition in the shape of holistic pipeline. Furthermore, there are
some newly-emerged challenges, such as the masked face recognition caused by
the recent world-wide COVID-19 pandemic, which draws increasing attention in
practical applications. A feasible and elegant solution is to build an
easy-to-use unified framework to meet the above demands. To this end, we
introduce a novel open-source framework, named FaceX-Zoo, which is oriented to
the research-development community of face recognition. Resorting to the highly
modular and scalable design, FaceX-Zoo provides a training module with various
supervisory heads and backbones towards state-of-the-art face recognition, as
well as a standardized evaluation module which enables to evaluate the models
in most of the popular benchmarks just by editing a simple configuration. Also,
a simple yet fully functional face SDK is provided for the validation and
primary application of the trained models. Rather than including as many as
possible of the prior techniques, we enable FaceX-Zoo to easily upgrade and
extend along with the development of face related domains. The source code and
models are available at https://github.com/JDAI-CV/FaceX-Zoo.
