Deep Learning Framework to Detect Face Masks from Video Footage

TL;DR

This paper presents a deep learning framework that combines MTCNN face detection with MobileNetV2-based classification to accurately detect facial masks in video footage, aiding public safety during the COVID-19 pandemic.

Contribution

It introduces a novel combination of face detection and mask classification using MTCNN and MobileNetV2, improving detection accuracy in videos with masked faces.

Findings

Achieved high precision, recall, and accuracy in mask detection.

Effective in real-world public space videos.

Addresses occlusion challenges in face detection.

Abstract

The use of facial masks in public spaces has become a social obligation since the wake of the COVID-19 global pandemic and the identification of facial masks can be imperative to ensure public safety. Detection of facial masks in video footages is a challenging task primarily due to the fact that the masks themselves behave as occlusions to face detection algorithms due to the absence of facial landmarks in the masked regions. In this work, we propose an approach for detecting facial masks in videos using deep learning. The proposed framework capitalizes on the MTCNN face detection model to identify the faces and their corresponding facial landmarks present in the video frame. These facial images and cues are then processed by a neoteric classifier that utilises the MobileNetV2 architecture as an object detector for identifying masked regions. The proposed framework was tested on a dataset which is a collection of videos capturing the movement of people in public spaces while complying with COVID-19 safety protocols. The proposed methodology demonstrated its effectiveness in detecting facial masks by achieving high precision, recall, and accuracy.