MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices
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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.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Nov 2018.
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Citing papers per year: 2021: 4; 2022: 0; 2023: 5; 2024: 3.
12 papers.
- 2021The Right to Talk: An Audio-Visual Transformer Approachcited by 33
- 2023EdgeFace: Efficient Face Recognition Model for Edge Devicescited by 33
- 2021Fast Facial Landmark Detection and Applications: A Surveycited by 18
- 2023NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscationcited by 16
- 2021Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Imagescited by 8
- 2021Recent Advances in Deep Learning Techniques for Face Recognitioncited by 5
- 2023An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verificationcited by 4
- 2024Deep Network Pruning: A Comparative Study on CNNs in Face Recognitioncited by 3
- 2023SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruningcited by 2
- 2024Artificial Immune System of Secure Face Recognition Against Adversarial Attackscited by 2
- 2024CarcassFormer: An End-to-end Transformer-based Framework for Simultaneous Localization, Segmentation and Classification of Poultry Carcass Defectcited by 1
- 2023Lightweight Face Recognition: An Improved MobileFaceNet Modelcited by 0
- 2024Artificial Immune System of Secure Face Recognition Against Adversarial Attacks26 Jun 2024
- 2024Deep Network Pruning: A Comparative Study on CNNs in Face Recognition28 May 2024
- 2024CarcassFormer: An End-to-end Transformer-based Framework for Simultaneous Localization, Segmentation and Classification of Poultry Carcass Defect17 Apr 2024
- 2023Lightweight Face Recognition: An Improved MobileFaceNet Model26 Nov 2023
- 2023An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification25 Jul 2023
- 2023SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning20 Jul 2023
- 2023EdgeFace: Efficient Face Recognition Model for Edge Devices4 Jul 2023
- 2023NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation28 Apr 2023
- 2021The Right to Talk: An Audio-Visual Transformer Approach6 Aug 2021
- 2021Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images31 Mar 2021
- 2021Recent Advances in Deep Learning Techniques for Face Recognition18 Mar 2021
- 2021Fast Facial Landmark Detection and Applications: A Survey12 Jan 2021