Impact of Facemasks on Face Detection

Date

2020-11

Contributor

Advisor

Department

Instructor

Depositor

Speaker

Researcher

Consultant

Interviewer

Narrator

Transcriber

Annotator

Journal Title

Journal ISSN

Volume Title

Publisher

University of Hawaiʻi — West Oʻahu

Volume

Number/Issue

Starting Page

Ending Page

Alternative Title

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.

Description

A student presentation to the Fall 2020 Student Research and Creative Works Symposium

Keywords

Citation

Extent

1 page

Format

Geographic Location

Time Period

Related To

Related To (URI)

Table of Contents

Rights

Attribution-NonCommercial-NoDerivs 3.0 United States

Rights Holder

Local Contexts

Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.