ITM Web Conf.
Volume 44, 2022International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
|Number of page(s)||7|
|Published online||05 May 2022|
Face recognition Attendance system using HOG and CNN algorithm
1 Department of Electronics and Telecommunication Engineering, Ramrao Adik Institute of Technology, Nerul
2 Department of Electronics and Telecommunication Engineering, Ramrao Adik Institute of Technology, Nerul
Recognition of faces is one of the most useful applications and has a critical role in the technological field. Recognizing the face is a lively concern for authentication, specifically in the context of taking attendance. Attendance system using face recognition is a process of recognizing the profile of the person by using facial features supported by various computing technology and monitoring. The evolution of this process is focused on achieving the digitizing of the orthodox system of taking manual attendance. Current approaches for taking attendance are monotonous and inefficient. Manual records of attendance can be easily manipulated. The orthodox process of checking attendance such as current fingerprint or card scanning systems are susceptible to proxies. To tackle these issues, this paper has been proposed. The proposed system makes the utilization of various algorithms such as histogram of oriented Gradient (HOG), convolutional neural network (CNN) and support vector machine (SVM). After the face is recognized, the reports of attendance are going to be created, maintained and stored in excel format. The system is examined in various situations like illumination, head movements, and the variation of distance between the face and cameras. The proposed system was found to be efficient and reliable for marking attendance during a classroom with negligible time consumption and no manual work. This system is inexpensive as less installation is required.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.