Development of an Improved Convolutional Neural Network for an Automated Face-Based University Attendance System
Loading...
Date
2023-04-27
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Paradigm Plus
Abstract
Because of the flaws of the present university attendance system, which has always been time
intensive, not accurate, and a hard process to follow. It, therefore, becomes imperative to eradicate
or minimize the deficiencies identified in the archaic method. The identification of human face
systems has evolved into a significant element in autonomous attendance-taking systems due to
their ease of adoption and dependable and polite engagement. Face recognition technology has
drastically altered the field of Convolution Neural Networks (CNN) however it has challenges of
high computing costs for analyzing information and determining the best specifications (design)
for each problem. Thus, this study aims to enhance CNN’s performance using Genetic Algorithm
(GA) for an automated face-based University attendance system. The improved face recognition
accuracy with CNN-GA got 96.49% while the face recognition accuracy with CNN got 92.54%.
Description
Keywords
Attendance System, Convolution Neural Networks (CNN), Face Recognition, Genetic Algorithm