Development of an Improved Convolutional Neural Network for an Automated Face Based University Attendance System

[摘要] Because of the flaws of the present university attendance system, which has always been timeintensive, not accurate, and a hard process to follow. It, therefore, becomes imperative to eradicateor minimize the deficiencies identified in the archaic method. The identification of human facesystems has evolved into a significant element in autonomous attendance-taking systems due totheir ease of adoption and dependable and polite engagement. Face recognition technology hasdrastically altered the field of Convolution Neural Networks (CNN) however it has challenges ofhigh 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 recognitionaccuracy with CNN-GA got 96.49% while the face recognition accuracy with CNN got 92.54%.

[学科分类] 环境工程

[关键词] Attendance System;CNN;Data Mining;Face Recognition;Genetic Algorithm