EFaR 2023: Efficient Face Recognition Competition

TL;DR

The paper summarizes the Efficient Face Recognition Competition at IJCB 2023, highlighting the performance of lightweight models optimized for accuracy, efficiency, and bias mitigation in face recognition tasks.

Contribution

It introduces a competitive benchmark for efficient face recognition models, evaluates diverse submissions, and discusses techniques like model quantization and bias handling.

Findings

Top solutions achieved high verification accuracy on multiple benchmarks.

Efficient models with reduced computational cost were successfully developed.

Bias and cross-quality robustness were evaluated across submissions.

Abstract

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.