Abstract
In a conventional attendance monitoring system, the concerned teacher takes attendance manually in a classroom. In general, it is a time-consuming and very difficult task to take attendance of a huge number of students in a short period of time and involves proxy attendance. To overcome these issues, we proposed a face recognition-based student attendance monitoring system in a classroom environment. The proposed method uses the Histogram of Oriented Gradients (HOG) as features extractor, Convolutional Neural Network (CNN) as face encoding and Support Vector Machine (SVM) as a classifier. The proposed system recognizes the face in real-time using a webcam and generates attendance report automatically without any human intervention. Our face recognition method accomplished 99.5% accuracy on Labeled Faces in the Wild (LFW) database and 97.83% accuracy in real-time inside the classroom for the case of attendance monitoring. Finally, we tested our system to validate its effectiveness.
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Roy, B.C., Hossen, I., Golam Rashed, M., Das, D. (2021). Automated Student Attendance Monitoring System Using Face Recognition. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_54
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