# CoverTheFace: face covering monitoring and demonstrating using deep learning and statistical shape analysis

arXiv: https://arxiv.org/abs/2108.10430 · HTML: https://mlsanity.com/paper/2108.10430/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Masked Face Recognition for Secure Authentication](https://mlsanity.com/paper/2008.11104/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19](https://mlsanity.com/paper/2201.04777/) (2022)
- [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022)

## Abstract (arXiv)

Wearing a mask is a strong protection against the COVID-19 pandemic, even though the vaccine has been successfully developed and is widely available. However, many people wear them incorrectly. This observation prompts us to devise an automated approach to monitor the condition of people wearing masks. Unlike previous studies, our work goes beyond mask detection; it focuses on generating a personalized demonstration on proper mask-wearing, which helps people use masks better through visual demonstration rather than text explanation. The pipeline starts from the detection of face covering. For images where faces are improperly covered, our mask overlay module incorporates statistical shape analysis (SSA) and dense landmark alignment to approximate the geometry of a face and generates corresponding face-covering examples. Our results show that the proposed system successfully identifies images with faces covered properly. Our ablation study on mask overlay suggests that the SSA model helps to address variations in face shapes, orientations, and scales. The final face-covering examples, especially half profile face images, surpass previous arts by a noticeable margin.