Design and Build of Masked Face Identification System and IoT-Based Body Temperature Measurement

I P.A. Bayupati, Aditya Ersapramana, I Putu Arya Dharmaadi


The new normal is an era in the behavior changed to obstruct the spread of COVID-19, such as decreasing people's mobility, body temperature measuring, mandatory masking, and getting a COVID-19 vaccine regularly. This study develops an identification system based on the Internet of Things through facial biometrics and temperature measurement. Face identification is divided into two main steps: face detection and identification. Face detections used the Framework YOLOv5, in which the systems can detect masked and without masked faces. Pre-trained VGG-face is used for face identification for feature extraction and produces a 2622-dimensional vector. The feature extraction result is calculated as the distance similarity with the features stored in the Database using Euclidean distance. Temperature measurement utilizes IoT by using the NodeMCU ESP8266 and the MLX90614 sensor. NodeMCU ESP8266 is a microcontroller equipped with a WI-FI module to send temperature data so measurements can be delivered wirelessly. The MLX90614 sensor measures body temperature at a 40 – 60 cm distance from the Sensor. Calibration of the sensor used Two-point Calibration, so a trim error rate level is produced. The result successfully identified the face with the F1 score of 92% without a masked face and 73% for a masked face. The body temperature was measured using the MLX90614 sensor produced an error rate of 0.1°C after calibration. In the future, this system can be further developed and utilized for other sectors, such as the medical and security sectors.


Face identification; feature extraction; internet of things; MLX90614; temperature measurement; YOLO; VGG-Face

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