# Open Source Face Recognition Performance Evaluation Package

## TL;DR

The paper introduces FaRE, an open-source, scalable evaluation toolbox for face recognition algorithms that supports multiple datasets and evaluation methods to accelerate biometrics research.

## Contribution

It presents FaRE, a lightweight and extendable face recognition evaluation package supporting various datasets and evaluation metrics.

## Key findings

- Provides a comprehensive evaluation framework for face recognition algorithms.
- Supports multiple datasets including LFW, CFP, UHDB31, and IJB-series.
- Facilitates faster development and benchmarking of face recognition models.

## 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 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.