Abstract
Manually recording attendance of a mass gathering like a classroom or seminar is very difficult and error-prone. To make the attendance process trouble-free, in this paper, we have explored the application of face recognition in the automated attendance system that can be used for educational and organizational purposes. Face recognition is a computer vision technology that can be applied for different purposes in various practical applications. The first task of face recognition is to detect faces from an image or real-time video input. Different state-of-the-art Deep Neural Network-based face detection and face recognition models are available in computer vision. We have studied popular face detection models like HaarCascade, SSD, MTCNN, and YOLOv5. We have also analyzed LBPH, FaceNet, ArcFace, and DeepFace models for the face recognition tasks. For this study, we have collected an image dataset of faces using a real-life classroom environment. Based on the result of our study we have proposed an automated attendance system using the YOLOv5 and ArcFace model for mass attendance, that is, recording the attendance of all persons in a room at a time.
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