Development of an Automatic Class Attendance System using CNN-based Face Recognition

We are living in the 21
^st
century which is the era of modern technology. Many traditional problems are being solved using new innovative technologies. Taking attendance daily is an indispensable part of educational institutions as well as offices. It is both exhausting and time-consuming if done manually. Biometric attendance systems through voice, iris, and fingerprint recognition require complex and expensive hardware support. An auto attendance system using face recognition, which is another biometric trait, can resolve all these problems. This paper represents the development of a face recognition based automatic student attendance system using Convolutional Neural Networks which includes data entry, dataset training, face recognition and attendance entry. The system can detect and recognize multiple person's face from video stream and automatically record daily attendance. The proposed system achieved an average recognition accuracy of about 92 %. Using this system, daily attendance can be recorded effortlessly avoiding the risk of human error.