This paper presents an extensive exploration and comparative analysis of
lightweight face recognition (FR) models, specifically focusing on
MobileFaceNet and its modified variant, MMobileFaceNet. The need for efficient
FR models on devices with limited computational resources has led to the
development of models with reduced memory footprints and computational demands
without sacrificing accuracy. Our research delves into the impact of dataset
selection, model architecture, and optimization algorithms on the performance
of FR models. We highlight our participation in the EFaR-2023 competition,
where our models showcased exceptional performance, particularly in categories
restricted by the number of parameters. By employing a subset of the Webface42M
dataset and integrating sharpness-aware minimization (SAM) optimization, we
achieved significant improvements in accuracy across various benchmarks,
including those that test for cross-pose, cross-age, and cross-ethnicity
performance. The results underscore the efficacy of our approach in crafting
models that are not only computationally efficient but also maintain high
accuracy in diverse conditions.

本研究详细探讨和比较了轻量级人脸识别模型，特别关注 MobileFaceNet 及其修改版 MMobileFaceNet。我们研究了数据集选择、模型架构和优化算法对人脸识别模型性能的影响。通过在 EFaR-2023 竞赛中的参与，我们的模型在参数受限的类别中表现出色。通过使用 Webface42M 数据集的子集和集成锐度感知最小化（SAM）优化，我们在各种基准测试中实现了显着的精度提升，包括跨姿势、跨年龄和跨种族的性能。结果凸显了我们方法在打造既计算高效又在不同条件下保持高精度的模型方面的有效性。