A novel mechanism for tracking student attendance towards the development of smart classrooms

Abstract

To track the participation of students is mandatory in numerous educational sectors. The manual administration of the participation sheets is difficult for swarmed study halls. Face detection and identification frameworks, as one of the sub-parts of computer vision, were initially intended for public reconnaissance. In this study, a novel mechanism is proposed with a face detection system using Convolutional neural networks (CNN), and Support vector machines (SVM) techniques for tracking the attendance of students. Even though several automated models of attendance are used in schools and universities, the proposed approach uses the effectual machine learning and deep learning techniques to increase the effectiveness of the approaches. In the proposed model, multiple cameras are used to take the photo in 360 degrees (reduce the possibility of missing some faces of students) and then the face detection process will be applied by applying a few machine learning approaches to detect the faces of students. Lightning, context, and pose variation will be extracted using Local Binary Patterns Histograms (LBCH) algorithm, and then, the face recognition process will have conducted by CNN and SVM algorithms. Finally, the attendance report will be generated by the matching process via matching the captured images with stored images after the duplicate removing process by applying the AdaBoost classification algorithm. We believe that our proposed approach based on face detection by applying several machine learning algorithms and deep learning algorithms can be used to track student attendance and prevent fake attendance effectively.