Machine Learning based Human Face Recognition for Attendance System

Abstract

Person identification and authentication are crucial in business. Many methods are utilized for this identification process. Especially biometric technologies are popular one because of its hardness about deception. Human Face recognition is one of the most preferred tools that helps authenticating the humans. This method allows us to detect changes in a person's face patterns. This technology can be used to identify perpetrators in crime detection and also for getting an accurate attendance systems both in university and also in companies. The part of a person's head from the forehead to the chin, or the corresponding part of an animal, is defined as a face by the Oxford Dictionary. In human. The face is the most crucial aspect in human interactions, since it contains important information about an individual. All humans will acknowledge people from their faces. The proposed solution is to create a working prototype of a system that can help lecturers monitor class attendance in lecture rooms by identifying students' faces from an image taken in the classroom. The database can hold the faces of students once the individual's face matches one of the faces stored in the database, attendance is recorded. Face recognition and detection technologies have been developed in recent years. A number of these are used on social media platforms like Facebook, banking apps, government offices, etc. © 2022 IEEE.