Open AccessMasked Face Detection and Calibration with Deep Learning Models
Author(s) -
Xingze Li
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2196/1/012011
Subject(s) - computer science , face (sociological concept) , artificial intelligence , face detection , covid-19 , machine learning , calibration , deep learning , facial recognition system , pattern recognition (psychology) , mathematics , medicine , social science , statistics , disease , pathology , sociology , infectious disease (medical specialty)
Under the COVID-19 pandemic, the demand that face detection devices should be enhanced to detect masked faces is imperative. In this study, we utilize several state-of-the-art face detection models and compare them on various unmasked and masked human face datasets. Moreover, by analyzing the results we obtain, we evaluate these disparate models and discover some problems. Attempting to overcome the problems discovered, we propose and implement several improvements, and acquire more results for analysis. At length, we propose some ideas for future research directions.