Face Identification System and Temperature Detection as A Control to Enter the Building

Abstract

The purpose of this study was to develop an access control system for the identification of face and body temperature during the pandemic. Access control is granted after system tests and recognizes a person's face according to the information already stored. Facial identification system developed using the CNN method as a facial recognition process. While the Euclidean distance to calculate the pattern of the face extraction point. The sensor (DS18B20) is used for temperature detection and activating the LED if the temperature criteria and face identification are correct. The access control system is written with the Python programming system and uses the OpenCV module. Based on the results of the calculations from 5 datasets, the system has an accuracy of 75.31% with an average detectable temperature of 36.21 Celsius. This access control system can operate for 24 hours so that it is more effective and efficient in controlling entry into the building.