Abstract: The world that we live in has been subjected to major changes since the outbreak of the COVID-19 pandemic. One of the biggest problems brought upon our generation due to this virus is the inability to go about with normal life. Fundamental aspects of our lives like going to in-person classes, attending programs and going to jobs have become impossible without a large amount of risk being involved. In order to make sure educational institutes, businesses and programs can function efficiently and effectively over an internet connection, we have devised a system titled “Attendance Management System Based on Face Recognition”. 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.

Keywords: Face recognition, Histogram oriented gradients.

| DOI: 10.17148/IARJSET.2022.9707