Paper Title :Face Recognition Attendance
Author :Pantula Kartik, Aditya Chakraborty, Muskan Behera, Rakhi Yogma, Soumyadiptya Dutta, Thisara Kaushan
Article Citation :Pantula Kartik ,Aditya Chakraborty ,Muskan Behera ,Rakhi Yogma ,Soumyadiptya Dutta ,Thisara Kaushan ,
(2024 ) " Face Recognition Attendance " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 63-68,
Volume-12,Issue-7
Abstract : This paper introduces a Facial Recognition Attendance System leveraging deep learning techniques, specifically
Residual Networks, to address attendance management challenges in educational institutions. Traditional attendance tracking
methods, such as manual entry or barcode scanning, are often error-prone, time-consuming, and lack efficiency. The
proposed system aims to streamline this process by automating attendance recording through facial recognition technology.
The system's architecture is based on Residual Networks, a class of deep neural networks renowned for their ability to train
deeper models without encountering the vanishing gradient problem. The use of deep learning enables the system to learn
intricate facial features and patterns, resulting in high accuracy and reliability in attendance tracking. The dataset used for
training and validation consists of facial images collected from students and staff, ensuring diversity and representativeness.
The evaluation of the Facial Recognition Attendance System yielded promising results, with an accuracy rate surpassing
97%. The findings of this study hold significant implications for educational institutions seeking to modernize their
attendance tracking systems.
Type : Research paper
Published : Volume-12,Issue-7
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-21099
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Copyright: © Institute of Research and Journals
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Published on 2024-10-17 |
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