mlsanityData to 7 Oct 2026

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

arXiv 1804.07573 ↗First posted 20 Apr 2018cs.CV cs.LGcited by 180

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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.

Key facts. Cited by 180 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 35; 2022: 29; 2023: 38; 2024: 30; 2025: 27; 2026: 21.

180 papers.

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  1. 2021WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognitioncited by 221
  2. 2021FaceX-Zoo: A PyTorch Toolbox for Face Recognitioncited by 49
  3. 2022Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognitioncited by 42
  4. 2023CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Searchcited by 42
  5. 2022WebFace260M: A Benchmark for Million-Scale Deep Face Recognitioncited by 41
  6. 2021RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognitioncited by 40
  7. 2023From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videoscited by 39
  8. 2021Learning Compatible Embeddingscited by 37
  9. 2021MixFaceNets: Extremely Efficient Face Recognition Networkscited by 36
  10. 2021PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillationcited by 36
  11. 2021Py-Feat: Python Facial Expression Analysis Toolboxcited by 33
  12. 2023Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognitioncited by 33
38 more
  1. 2023EdgeFace: Efficient Face Recognition Model for Edge Devicescited by 33
  2. 2021Self-restrained Triplet Loss for Accurate Masked Face Recognitioncited by 32
  3. 2023Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognitioncited by 32
  4. 2021Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networkscited by 31
  5. 2024DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protectioncited by 31
  6. 2021Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devicescited by 30
  7. 2024Transferable Adversarial Facial Images for Privacy Protectioncited by 26
  8. 2021MFR 2021: Masked Face Recognition Competitioncited by 23
  9. 2021Iterative Distillation for Better Uncertainty Estimates in Multitask Emotion Recognitioncited by 20
  10. 2022Evaluation-oriented Knowledge Distillation for Deep Face Recognitioncited by 19
  11. 2021Fast Facial Landmark Detection and Applications: A Surveycited by 18
  12. 2022QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantizationcited by 18
  13. 2022DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domaincited by 18
  14. 2021Towards Accurate and Compact Architectures via Neural Architecture Transformercited by 17
  15. 2023Recognizability Embedding Enhancement for Very Low-Resolution Face Recognition and Quality Estimationcited by 16
  16. 2021On Recognizing Occluded Faces in the Wildcited by 15
  17. 2021OneFlow: Redesign the Distributed Deep Learning Framework from Scratchcited by 15
  18. 2024Instant Adversarial Purification with Adversarial Consistency Distillationcited by 14
  19. 2021Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregationcited by 13
  20. 2023TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Carscited by 13
  21. 2024TopoFR: A Closer Look at Topology Alignment on Face Recognitioncited by 13
  22. 202550 Years of Automated Face Recognitioncited by 13
  23. 2021An Efficient Training Approach for Very Large Scale Face Recognitioncited by 12
  24. 2022Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Modelcited by 12
  25. 2021Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniquescited by 11
  26. 2022A Deep Dive into Dataset Imbalance and Bias in Face Identificationcited by 11
  27. 2025RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguitycited by 11
  28. 2021On Brightness Agnostic Adversarial Examples Against Face Recognition Systemscited by 9
  29. 2023ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learningcited by 9
  30. 2023Cross Architecture Distillation for Face Recognitioncited by 9
  31. 2023Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognitioncited by 9
  32. 2023SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Datacited by 9
  33. 2024ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognitioncited by 9
  34. 2021Comparing Human and Machine Bias in Face Recognitioncited by 8
  35. 2022Knowledge Distillation Meets Open-Set Semi-Supervised Learningcited by 8
  36. 2024HSEmotion Team at the 6th ABAW Competition: Facial Expressions, Valence-Arousal and Emotion Intensity Predictioncited by 8
  37. 2024Enhancing Facial Expression Recognition through Dual-Direction Attention Mixed Feature Networks: Application to 7th ABAW Challengecited by 8
  38. 2021Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Imagescited by 7

50 most cited of 180 All 180 (JSON)