Impact of Facemasks on Face Detection

Abstract

Research suggests that COVID-19 facemasks degrade the performance of face detection technology. The purpose of this study is to quantify this effect and identify the ideal way to train face detection models when facemasks are present. To do this, I trained a machine learning model for face detection with 1,000 regular human faces using the You Only Look Once (YOLOv4) object detection framework. I tested the face detection capabilities of the model on both regular human faces and masked faces, recording the accuracy and recall rate for each group. I then trained a new model, incorporating masked faces into the initial training dataset. The adjusted model was tested to determine if the adjusted training set imporoved performance. This research will benefit machine learning researchers and data scientists who will train and utilize facial recognition models in the midst of COVID-19 and beyond.