FaRE: Open Source Face Recognition Performance Evaluation Package

Xiang Xu, Ioannis A. Kakadiaris

Abstract: 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, implement, and evaluate a computationally lightweight, 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 biometricsrelated research. FaRE includes a set of evaluation metrics and provides various APIs for commonly-used face recognition datasets including LFW, CFP, UHDB31, and IJBseries datasets. FaRE can be easily extended to include other datasets. The package is publically available for research use at https://github.com/uh-cbl/FaRE.