Lightweight Face Recognition: An Improved MobileFaceNet Model
This work addresses the need for lightweight face recognition models for mobile and embedded devices, but it is incremental as it builds on existing MobileFaceNet with modifications.
This paper tackled the problem of efficient face recognition on devices with limited resources by improving MobileFaceNet, achieving significant accuracy gains in cross-pose, cross-age, and cross-ethnicity benchmarks through dataset selection and optimization techniques.
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.