# MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices

arXiv: https://arxiv.org/abs/1804.07573 · HTML: https://mlsanity.com/paper/1804.07573/

## Key facts

- Cited by 182 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 30 of 182)

- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [FaceX-Zoo: A PyTorch Toolbox for Face Recognition](https://mlsanity.com/paper/2101.04407/) (2021)
- [Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition](https://mlsanity.com/paper/2210.06871/) (2022)
- [CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search](https://mlsanity.com/paper/2306.10008/) (2023)
- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition](https://mlsanity.com/paper/2105.01883/) (2021)
- [From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos](https://mlsanity.com/paper/2312.05447/) (2023)
- [Learning Compatible Embeddings](https://mlsanity.com/paper/2108.01958/) (2021)
- [MixFaceNets: Extremely Efficient Face Recognition Networks](https://mlsanity.com/paper/2107.13046/) (2021)
- [PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation](https://mlsanity.com/paper/2108.10710/) (2021)
- [Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition](https://mlsanity.com/paper/2303.12512/) (2023)
- [Py-Feat: Python Facial Expression Analysis Toolbox](https://mlsanity.com/paper/2104.03509/) (2021)
- [Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition](https://mlsanity.com/paper/2303.15818/) (2023)
- [EdgeFace: Efficient Face Recognition Model for Edge Devices](https://mlsanity.com/paper/2307.01838/) (2023)
- [Self-restrained Triplet Loss for Accurate Masked Face Recognition](https://mlsanity.com/paper/2103.01716/) (2021)
- [Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks](https://mlsanity.com/paper/2107.01809/) (2021)
- [DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection](https://mlsanity.com/paper/2405.09882/) (2024)
- [Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices](https://mlsanity.com/paper/2105.10288/) (2021)
- [Transferable Adversarial Facial Images for Privacy Protection](https://mlsanity.com/paper/2408.01428/) (2024)
- [MFR 2021: Masked Face Recognition Competition](https://mlsanity.com/paper/2106.15288/) (2021)
- [Iterative Distillation for Better Uncertainty Estimates in Multitask Emotion Recognition](https://mlsanity.com/paper/2108.04228/) (2021)
- [Evaluation-oriented Knowledge Distillation for Deep Face Recognition](https://mlsanity.com/paper/2206.02325/) (2022)
- [Fast Facial Landmark Detection and Applications: A Survey](https://mlsanity.com/paper/2101.10808/) (2021)
- [QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization](https://mlsanity.com/paper/2206.10526/) (2022)
- [DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain](https://mlsanity.com/paper/2207.07340/) (2022)
- [Towards Accurate and Compact Architectures via Neural Architecture Transformer](https://mlsanity.com/paper/2102.10301/) (2021)
- [Recognizability Embedding Enhancement for Very Low-Resolution Face Recognition and Quality Estimation](https://mlsanity.com/paper/2304.10066/) (2023)
- [On Recognizing Occluded Faces in the Wild](https://mlsanity.com/paper/2109.03672/) (2021)
- [OneFlow: Redesign the Distributed Deep Learning Framework from Scratch](https://mlsanity.com/paper/2110.15032/) (2021)
- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)

## Abstract (arXiv)

We present a class of extremely efficient CNN models, MobileFaceNets, which use less than 1 million parameters and are specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. We first make a simple analysis on the weakness of common mobile networks for face verification. The weakness has been well overcome by our specifically designed MobileFaceNets. Under the same experimental conditions, our MobileFaceNets achieve significantly superior accuracy as well as more than 2 times actual speedup over MobileNetV2. After trained by ArcFace loss on the refined MS-Celeb-1M, our single MobileFaceNet of 4.0MB size achieves 99.55% accuracy on LFW and 92.59% TAR@FAR1e-6 on MegaFace, which is even comparable to state-of-the-art big CNN models of hundreds MB size. The fastest one of MobileFaceNets has an actual inference time of 18 milliseconds on a mobile phone. For face verification, MobileFaceNets achieve significantly improved efficiency over previous state-of-the-art mobile CNNs.