Lightweight Face Recognition: An Improved MobileFaceNet Model
Ahmad Hassanpour Yasamin Kowsari
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.