Open Source Face Recognition Performance Evaluation Package
This work addresses a practical need for researchers in biometrics by offering an accessible tool to accelerate algorithm development, though it is incremental as it builds on existing evaluation methods.
The authors tackled the lack of open-source tools for evaluating deep learning-based face recognition algorithms by developing FaRE, a lightweight and scalable evaluation toolbox that supports multiple datasets and provides APIs for efficient assessment.
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.