Abstract : Since the COVID-19 pandemic, it is required to wear a face mask in public areas. A correctly worn mask provides the most protection against the viral transmission of COVID. A person's body temperature has also become crucial in evaluating whether they are healthy or suffering from an issue. In this system, we develop a real-time model to meet the requirement for detecting a person's body temperature and the percentage of masks they are wearing before they reach a public area. With the aid of an Arduino and temperature sensor, we were able to determine the position of the mask by using the face detection approach. To record input photographs and gauge a person's temperature, we employed an ESP camera and temperature sensor, respectively. The result of these tests is a live video feed that provides precise information about whether or not someone is wearing a mask properly and whether or not their body temperature is within the proper range or falls below it. If it is either above or below, the door won't open, and a red indicator light will illuminate while a buzzer beeps; if it is at the proper level, the door will open.

Keyword : Arduino, ESP Camera, Neural network, Object detection.