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.

翻译：本文概述了2023年国际生物特征识别联合会议（IJCB 2023）上举办的高效人脸识别竞赛（EFaR 2023）。竞赛共收到来自6个不同团队的17份参赛方案。为推动高效人脸识别模型的进一步发展，参赛方案根据其在多个基准测试集上的验证准确率加权得分，以及由浮点运算次数和模型规模所决定的部署可行性进行排名。评估范围扩展至偏差、跨质量及大规模识别基准测试。总体而言，本文概述了参赛方案所取得的性能指标以及多种基线方案。参赛方案采用小型高效网络架构以降低计算成本，部分方案应用了模型量化。本文还展望了当前解决方案中代表性不足的可能技术方向。