Transfer learning for mobile real-time face mask detection and localization.
J Am Med Inform Assoc
; 28(7): 1548-1554, 2021 Jul 14.
Article
in English
| MEDLINE | ID: covidwho-1310932
ABSTRACT
OBJECTIVE:
Due to the COVID-19 pandemic, our daily habits have suddenly changed. Gatherings are forbidden and, even when it is possible to leave the home for health or work reasons, it is necessary to wear a face mask to reduce the possibility of contagion. In this context, it is crucial to detect violations by people who do not wear a face mask. MATERIALS ANDMETHODS:
For these reasons, in this article, we introduce a method aimed to automatically detect whether people are wearing a face mask. We design a transfer learning approach by exploiting the MobileNetV2 model to identify face mask violations in images/video streams. Moreover, the proposed approach is able to localize the area related to the face mask detection with relative probability.RESULTS:
To asses the effectiveness of the proposed approach, we evaluate a dataset composed of 4095 images related to people wearing and not wearing face masks, obtaining an accuracy of 0.98 in face mask detection. DISCUSSION ANDCONCLUSION:
The experimental analysis shows that the proposed method can be successfully exploited for face mask violation detection. Moreover, we highlight that it is working also on device with limited computational capability and it is able to process in real time images and video streams, making our proposal applicable in the real world.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Deep Learning
/
Automated Facial Recognition
/
COVID-19
/
Masks
Type of study:
Experimental Studies
Limits:
Female
/
Humans
/
Male
Language:
English
Journal:
J Am Med Inform Assoc
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Jamia
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