ONE-SHOT LEARNING FOR FACE RECOGNITION ATTENDANCE USING DEEP LEARNING

WONG, MUN WAI (2019) ONE-SHOT LEARNING FOR FACE RECOGNITION ATTENDANCE USING DEEP LEARNING. [Final Year Project Report] (Unpublished)

[img] PDF
Wong Mun Wai - 24 pgs.pdf

Download (1MB)
[img] PDF (Please get the password from TECHNICAL & DIGITIZATION MANAGEMENT UNIT, ext: 082-583913/ 082-583914)
Wong Mun Wai.pdf
Restricted to Registered users only

Download (2MB)

Abstract

Class Attendance is very important as it may directly affect the performance of students. Hence, a lot of universities and institutions require their students to attend at least 80% of each subject in order to pass or sit for final exam of that corresponding subject. There are a lot of ways usedby universities and institutions to record students’ attendance and each way has its own pros and cons. In this project, questionnaire is used to know whether the proposed system would be better compared to current QR code attendance system using in UNIMAS. The purpose of this project is to develop a face recognition for UNIMAS to replace the QR code which may consider more flaw compared to face recognition attendance system. Besides, an investigation and possible improvement on face recognition accuracy and detection speed will be done during this project to increase the overall performance of the face recognition.

Item Type: Final Year Project Report
Additional Information: Project Report (BSc.) -- Universiti Malaysia Sarawak, 2019.
Uncontrolled Keywords: QR code attendance system, face recognition, universities and institutions.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Gani
Date Deposited: 21 Jan 2021 06:50
Last Modified: 21 Jan 2021 06:50
URI: http://ir.unimas.my/id/eprint/33972

Actions (For repository members only: login required)

View Item View Item