# HiMFR: A Hybrid Masked Face Recognition Through Face Inpainting

arXiv: https://arxiv.org/abs/2209.08930 · HTML: https://mlsanity.com/paper/2209.08930/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Pluralistic Image Completion](https://mlsanity.com/paper/1903.04227/) (2019)
- [Masked Face Recognition Dataset and Application](https://mlsanity.com/paper/2003.09093/) (2020)
- [Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network](https://mlsanity.com/paper/1908.06290/) (2019)
- [Masked Face Recognition for Secure Authentication](https://mlsanity.com/paper/2008.11104/) (2020)
- [Face Transformer for Recognition](https://mlsanity.com/paper/2103.14803/) (2021)
- [Efficient Masked Face Recognition Method during the COVID-19 Pandemic](https://mlsanity.com/paper/2105.03026/) (2021)
- [Boosting Masked Face Recognition with Multi-Task ArcFace](https://mlsanity.com/paper/2104.09874/) (2021)

## Built on by (most cited first, 3 of 3)

- [Transformer-based Image and Video Inpainting: Current Challenges and Future Directions](https://mlsanity.com/paper/2407.00226/) (2024)
- [ViT-FREE: Efficient Face Recognition via Early Exiting and Synthetic Adaptation](https://mlsanity.com/paper/2606.12023/) (2026)
- [Vision Transformers for Face Recognition Need More Registers](https://mlsanity.com/paper/2606.12036/) (2026)

## Abstract (arXiv)

To recognize the masked face, one of the possible solutions could be to restore the occluded part of the face first and then apply the face recognition method. Inspired by the recent image inpainting methods, we propose an end-to-end hybrid masked face recognition system, namely HiMFR, consisting of three significant parts: masked face detector, face inpainting, and face recognition. The masked face detector module applies a pretrained Vision Transformer (ViTb32) to detect whether faces are covered with masked or not. The inpainting module uses a fine-tune image inpainting model based on a Generative Adversarial Network (GAN) to restore faces. Finally, the hybrid face recognition module based on ViT with an EfficientNetB3 backbone recognizes the faces. We have implemented and evaluated our proposed method on four different publicly available datasets: CelebA, SSDMNV2, MAFA, Pubfig83 with our locally collected small dataset, namely Face5. Comprehensive experimental results show the efficacy of the proposed HiMFR method with competitive performance. Code is available at https://github.com/mdhosen/HiMFR