  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.
