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Attendance System in Third -Level Irish Institutions and Colleges Using Face Recognition Approach

Rangarajan, Rajalakshmi (2023) Attendance System in Third -Level Irish Institutions and Colleges Using Face Recognition Approach. Masters thesis, Dublin, National College of Ireland.

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The generic system of taking attendance at universities by calling out the roll number of the students or calling out the name to mark as present or absent based on the responses from the students. This attendance system in third-level Irish institutions was an indirect way to encourage inaccuracy. Students often use the technique of proxy to help out their friends or copy the signatures of their classmates to mark the present. This being the general system of taking attendance of students has been the basis that leads to crop up of issues in the accuracy. This research presents a face detection-based automated attendance system that leverages the abilities of machine learning techniques. This research implements a system of attendance using convolutional neural networks (CNN) and Histogram of Oriented Gradients (HOG) with SVM to detect faces and identify the face present. The system developed in the research was found to achieve 90% accuracy obtained through visual verification. The models implemented in the study outperformed the models implemented in previous literature that mostly involved Viola-Jones Face Detector along with a classifier, Eigen feature extractions, and principal component analysis. The models presented overcame the limitations of accuracy and speed that affected these models.

Item Type: Thesis (Masters)
Mulwa, Catherine
Subjects: L Education > LB Theory and practice of education > LB2300 Higher Education
L Education > LF Individual institutions (Europe)
Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 24 May 2023 18:44
Last Modified: 24 May 2023 18:44

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