2021 1st International Conference on Emerging Smart Technologies and Applications (eSmarTA) | 2021
Smart Attendance System Based On Face Recognition Techniques
Abstract
Most academic facilities and institutions still use the traditional methods of employee attendance recording. This is currently done manually which may be a burden on employees and takes a long time to do. Manipulation may also occur with the manual process; thus, it loses its credibility. But now, with the advent of many deep learning algorithms, we propose in this paper the use of an automated system to implement and manage the attendance recording process automatically by a face recognition technique using convolutional neural networks. Our structure is based on a modern high-precision face detection algorithm using the YOLO v4 (You Only Look Once). The Yolo v4 superior using a single GPU achieving a high speed in detecting objects comparing with other models that require to use many GPUs. Using the Darknet-53 layers for face detection we get an accuracy of 100%. The system was designed using PyQt5; an easy-use and high accuracy system which also displays attendance information in the main interface and stores it automatically in an excel file so that it can be reviewed periodically.