  Biometrics-related research has been accelerated significantly by deep
learning technology. However, there are limited open-source resources to help
researchers evaluate their deep learning-based biometrics algorithms
efficiently, especially for the face recognition tasks. In this work, we design
and implement a light-weight, maintainable, scalable, generalizable, and
extendable face recognition evaluation toolbox named FaRE that supports both
online and offline evaluation to provide feedback to algorithm development and
accelerate biometrics-related research. FaRE consists of a set of evaluation
metric functions and provides various APIs for commonly-used face recognition
datasets including LFW, CFP, UHDB31, and IJB-series datasets, which can be
easily extended to include other customized datasets. The package and the
pre-trained baseline models will be released for public academic research use
after obtaining university approval.
