Class-based Digital Attendance Management System with Computer Vision

Abstract

Attendance taking is an age long practice used to validate a student’s presence in class. The traditional attendance system is done with pen and paper where students write their names on the attendance sheet. This method is marred with a lot of irregularities, inefficiencies and proxy attendance(s) where students record attendance for their friends and classmates who are not present in class. This paper, therefore, develops an attendance management system that leverages on computer vision technology to detect and recognize faces and a file management system to record the recognized faces against a spreadsheet of students present in a class session. It employed a face-recognition library built using dlib’s and a deep learning mechanism with an accuracy of 99.38%, OpenCV, Pandas, and web app technology, namely HTML5, CSS3, JavaScript, and python/flask. The database design was based on the resident computer file system and a CVS file type was used to handle the row-column structure of the attendance records. The panda library was implemented to mimic a structured query language. This research paper has been able to solve the problem of proxy attendance recorded in a manual attendance system by recording the registered students’ attendance automatically as they walk into the class.