Contactless logging and disinfection solution with automated face mask detection

Dominic Bolima, Christian Albert Huerto


Coronavirus disease-19 (COVID-19) mitigation includes health screening procedures at entrances of public and private establishments. Conventional methods include manual operations on temperature and face mask checking, personal identification, and hand disinfection. In this paper, a gateway kiosk is designed by integrating and automating the primary screening procedures. It uses radio-frequency identification (RFID) for contactless access control and attendance, with an infrared thermometer for body temperature monitoring. Face mask detection is automated through artificial intelligence (AI), while proximity sensors activate the disinfection system. Internet-of-things (IoT) interfaces these subsystems, and local access is available via an Internet browser. RFID overcomes the slower response rate of the quick response (QR) code-based solution. The repeated-measure analyses showed that the system’s thermometer has only +0.28 °C error while its residual neural network-10 (ResNet-10) and MobileNetV2 models for detecting masked faces achieved a 98.2% accuracy. The system reduces the primary key performance indicators–service and queuing times by 56.86% and 79.95%, respectively. Its audio and visual notifications ensure the proper screening implementation, thus reducing unnecessary and risky interactions with entrance personnel. It improves the screening procedures by significantly reducing human interactions and enhancing queuing.


Artificial intelligence; Automation; COVID-19; Deep learning; Internet-of-things

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).