# Face Mask Detection using Transfer Learning of InceptionV3

arXiv: https://arxiv.org/abs/2009.08369 · HTML: https://mlsanity.com/paper/2009.08369/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 23 of 23)

- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Detection of a facemask in real-time using deep learning methods: Prevention of Covid 19](https://mlsanity.com/paper/2401.15675/) (2024)
- [Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey](https://mlsanity.com/paper/2203.15203/) (2022)
- [Rethinking Membership Inference Attacks Against Transfer Learning](https://mlsanity.com/paper/2501.11577/) (2025)
- [WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19](https://mlsanity.com/paper/2101.00784/) (2021)
- [Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19](https://mlsanity.com/paper/2103.08773/) (2021)
- [CPPE-5: Medical Personal Protective Equipment Dataset](https://mlsanity.com/paper/2112.09569/) (2021)
- [A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19](https://mlsanity.com/paper/2201.04777/) (2022)
- [Mask or Non-Mask? Robust Face Mask Detector via Triplet-Consistency Representation Learning](https://mlsanity.com/paper/2110.00523/) (2021)
- [A Deep Learning-based Approach for Real-time Facemask Detection](https://mlsanity.com/paper/2110.08732/) (2021)
- [Class dependency based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of Tuberculosis from chest x-rays](https://mlsanity.com/paper/2108.04329/) (2021)
- [Mask Usage Recognition using Vision Transformer with Transfer Learning and Data Augmentation](https://mlsanity.com/paper/2203.11542/) (2022)
- [Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies](https://mlsanity.com/paper/2209.04631/) (2022)
- [A transfer learning approach with convolutional neural network for Face Mask Detection](https://mlsanity.com/paper/2310.18928/) (2023)
- [ARTEMIS: Using GANs with Multiple Discriminators to Generate Art](https://mlsanity.com/paper/2311.08278/) (2023)
- [A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking](https://mlsanity.com/paper/2405.05900/) (2024)
- [A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 Pandemic](https://mlsanity.com/paper/2405.18387/) (2024)
- [A Comparative Analysis of Machine Learning Approaches for Automated Face Mask Detection During COVID-19](https://mlsanity.com/paper/2112.07913/) (2021)
- [Real-Time Mask Detection Based on SSD-MobileNetV2](https://mlsanity.com/paper/2208.13333/) (2022)
- [A Benchmark Study by using various Machine Learning Models for Predicting Covid-19 trends](https://mlsanity.com/paper/2301.11257/) (2023)
- [Wearing face mask detection using deep learning through COVID-19 pandemic](https://mlsanity.com/paper/2305.00068/) (2023)
- [Automated Speaker Independent Visual Speech Recognition: A Comprehensive Survey](https://mlsanity.com/paper/2306.08314/) (2023)
- [Grassroots Operator Search for Model Edge Adaptation](https://mlsanity.com/paper/2309.11246/) (2023)

## Abstract (arXiv)

The world is facing a huge health crisis due to the rapid transmission of coronavirus (COVID-19). Several guidelines were issued by the World Health Organization (WHO) for protection against the spread of coronavirus. According to WHO, the most effective preventive measure against COVID-19 is wearing a mask in public places and crowded areas. It is very difficult to monitor people manually in these areas. In this paper, a transfer learning model is proposed to automate the process of identifying the people who are not wearing mask. The proposed model is built by fine-tuning the pre-trained state-of-the-art deep learning model, InceptionV3. The proposed model is trained and tested on the Simulated Masked Face Dataset (SMFD). Image augmentation technique is adopted to address the limited availability of data for better training and testing of the model. The model outperformed the other recently proposed approaches by achieving an accuracy of 99.9% during training and 100% during testing.