Smart Attendance Monitoring System for Online Classes Using Facial Recognition

Abstract

One of the most important activities in a classroom is the process of marking attendance. It is a universally accepted process to measure the punctuality of a student. However, there are certain drawbacks to the existing system where a teacher has to physically mark, whether a student is present or not. The main drawbacks of taking physical attendance are time-consuming while marking the attendance and there is a possibility of proxy attendance. The existing systems like fingerprint scanning and RFID are not completely proof worthy and can be easily tampered with. Kee** this view in mind, we implement a face recognition algorithm to mark the attendance of a student. The main aim of the proposed system is to make the process efficient and save time. The proposed system recognizes the student’s face from the images stored in the database and updates it in the attendance sheet automatically. To implement the online attendance system, we use popular algorithms like Haar-Cascade and HOG algorithm which is provided by the face_recognition library. As most of the face recognition algorithms work on 2D frames, they are unable to overcome the problem of spoofing, where the person’s face gets recognized from a photo. This problem of spoofing is dealt with the help of the eye-blinking detection CNN model and trained using Keras. The proposed system uses OpenCV and Machine Learning techniques to perform the complete process.