# MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices

arXiv: https://arxiv.org/abs/1811.11080 · HTML: https://mlsanity.com/paper/1811.11080/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2018.

## Built on by (most cited first, 12 of 12)

- [The Right to Talk: An Audio-Visual Transformer Approach](https://mlsanity.com/paper/2108.03256/) (2021)
- [EdgeFace: Efficient Face Recognition Model for Edge Devices](https://mlsanity.com/paper/2307.01838/) (2023)
- [Fast Facial Landmark Detection and Applications: A Survey](https://mlsanity.com/paper/2101.10808/) (2021)
- [NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation](https://mlsanity.com/paper/2305.00097/) (2023)
- [Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images](https://mlsanity.com/paper/2103.16760/) (2021)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification](https://mlsanity.com/paper/2307.13428/) (2023)
- [Deep Network Pruning: A Comparative Study on CNNs in Face Recognition](https://mlsanity.com/paper/2405.18302/) (2024)
- [SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning](https://mlsanity.com/paper/2307.10697/) (2023)
- [Artificial Immune System of Secure Face Recognition Against Adversarial Attacks](https://mlsanity.com/paper/2406.18144/) (2024)
- [CarcassFormer: An End-to-end Transformer-based Framework for Simultaneous Localization, Segmentation and Classification of Poultry Carcass Defect](https://mlsanity.com/paper/2404.11429/) (2024)
- [Lightweight Face Recognition: An Improved MobileFaceNet Model](https://mlsanity.com/paper/2311.15326/) (2023)

## Abstract (arXiv)

Deep neural networks have been widely used in numerous computer vision applications, particularly in face recognition. However, deploying deep neural network face recognition on mobile devices has recently become a trend but still limited since most high-accuracy deep models are both time and GPU consumption in the inference stage. Therefore, developing a lightweight deep neural network is one of the most practical solutions to deploy face recognition on mobile devices. Such the lightweight deep neural network requires efficient memory with small number of weights representation and low cost operators. In this paper, a novel deep neural network named MobiFace, a simple but effective approach, is proposed for productively deploying face recognition on mobile devices. The experimental results have shown that our lightweight MobiFace is able to achieve high performance with 99.73% on LFW database and 91.3% on large-scale challenging Megaface database. It is also eventually competitive against large-scale deep-networks face recognition while significant reducing computational time and memory consumption.