A NOVEL IMAGE PROCESSING BASED AUTOMATED ATTENDANCE SYSTEM USING FACE RECOGNITION

Abstract

 Automated face recognition has grown significantly in popularity in recent years, mostly for two reasons: first, it is possible thanks to the availability of modern technologies, and second, it can speed up the process of taking student attendance. Due of the time it saves, its utilisation will increase significantly in the future. Manually taking attendance takes a lot of time, and some people might even fake it. To cut down on both time consumption and attendance fraud, With the use of an image or video frame, face recognition is used to identify the student in the room and record his attendance. We suggested a machine learning approach to managing attendance called the CNN algorithm. The face detection and identification technology will automatically identify the pupils in the room and record their attendance. The faculty has the ability to add student information like name, USN, contact information (phone, email, etc.). The image is then recorded using a high-definition camera during class time. The Convolutional Neural Networks (CNN) approach of machine learning is used to detect, segment, and store the faces of students during lectures for database verification.