# Lightweight Face Recognition: An Improved MobileFaceNet Model

arXiv: https://arxiv.org/abs/2311.15326 · HTML: https://mlsanity.com/paper/2311.15326/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 26 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better](https://mlsanity.com/paper/2106.08962/) (2021)
- [MixFaceNets: Extremely Efficient Face Recognition Networks](https://mlsanity.com/paper/2107.13046/) (2021)
- [VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition](https://mlsanity.com/paper/1910.04985/) (2019)
- [MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices](https://mlsanity.com/paper/1811.11080/) (2018)
- [EFaR 2023: Efficient Face Recognition Competition](https://mlsanity.com/paper/2308.04168/) (2023)
- [Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups](https://mlsanity.com/paper/2309.08442/) (2023)
- [Software Defect Prediction Based On Deep Learning Models: Performance Study](https://mlsanity.com/paper/2004.02589/) (2020)

## Abstract (arXiv)

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.