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.
@article{arxiv.2308.04168,
title = {EFaR 2023: Efficient Face Recognition Competition},
author = {Jan Niklas Kolf and Fadi Boutros and Jurek Elliesen and Markus Theuerkauf and Naser Damer and Mohamad Alansari and Oussama Abdul Hay and Sara Alansari and Sajid Javed and Naoufel Werghi and Klemen Grm and Vitomir Štruc and Fernando Alonso-Fernandez and Kevin Hernandez Diaz and Josef Bigun and Anjith George and Christophe Ecabert and Hatef Otroshi Shahreza and Ketan Kotwal and Sébastien Marcel and Iurii Medvedev and Bo Jin and Diogo Nunes and Ahmad Hassanpour and Pankaj Khatiwada and Aafan Ahmad Toor and Bian Yang},
journal= {arXiv preprint arXiv:2308.04168},
year = {2023}
}