Employee Presence Using Body Temperature Detection and Face Recognition

Abstract

Employee performance can be measured by their presence or attendance, which applies to both civil servants and non-civil servants. Because the attendance system still uses the manual technique, it is considered inefficient due to the potential for data fraud and attendance problems. In addition, the government is adopting precautions against viruses in office buildings to maintain business continuity while the pandemic is being addressed. The goal of this study was to employ a facial recognition system and temperature measurement to lower the danger of COVID 19 transmission while also minimizing paper use by using a facial recognition system as a substitute for presences. It has so far permitted the digitization of formerly manual sights. The OpenCV library allows computers to detect faces using the haar cascade classifier approach and Python as a programming language. A Logitech C930e webcam with a resolution of 1080p at 30fps was used to capture facial data, which was then processed on a Raspberry Pi 4 microprocessor. It uses an MLX90614 sensor to monitor body temperature, which is controlled by anArduino Uno microcontroller. It is well integrated into the database based on body temperature testing and facial recognition. The development of a more accurate temperature sensor reading method for distance and employee body temperature is a priority for future research.