Face-recognition based attendance authentication system
Attendance in a classroom lecture can be monitored as a means of tracking student participation. However, the current method of recording attendance using paper-based systems and QR code scanners has proven to be unstable, inefficient, and time-consuming, especially in large classrooms. Differentiat...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2023
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| _version_ | 1817854154600611840 |
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| author | Mohd. Azli, Amirul Mukhlis Mammi, Hazinah Kutty Mat Din, Mazura Abdul-Samad, Adlina |
| author_facet | Mohd. Azli, Amirul Mukhlis Mammi, Hazinah Kutty Mat Din, Mazura Abdul-Samad, Adlina |
| author_sort | Mohd. Azli, Amirul Mukhlis |
| collection | ePrints |
| description | Attendance in a classroom lecture can be monitored as a means of tracking student participation. However, the current method of recording attendance using paper-based systems and QR code scanners has proven to be unstable, inefficient, and time-consuming, especially in large classrooms. Differentiating between absentees and proxy attendees becomes challenging with the traditional approach. To address these issues, a web-based attendance system with facial recognition capabilities has been developed. This system integrates an existing web camera with a facial recognition system, enabling automatic and efficient attendance tracking with minimal setup requirements. Through this web application, lecturers have the option to reject student attendance if they violate rules and regulations, and they can also generate attendance reports. Similarly, students can view their attendance percentage for each registered course. Additionally, this research proposes the integration of smart contract-based blockchain technology into the system. In conclusion, this system aims to assist lecturers in monitoring student attendance by utilizing facial recognition technology with an accuracy rate of 98%, while the proposed smart contract ensures secure and efficient management of attendance records. |
| first_indexed | 2024-12-08T06:53:38Z |
| format | Conference or Workshop Item |
| id | utm.eprints-107694 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-12-08T06:53:38Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | utm.eprints-1076942024-10-02T06:26:52Z http://eprints.utm.my/107694/ Face-recognition based attendance authentication system Mohd. Azli, Amirul Mukhlis Mammi, Hazinah Kutty Mat Din, Mazura Abdul-Samad, Adlina QA75 Electronic computers. Computer science Attendance in a classroom lecture can be monitored as a means of tracking student participation. However, the current method of recording attendance using paper-based systems and QR code scanners has proven to be unstable, inefficient, and time-consuming, especially in large classrooms. Differentiating between absentees and proxy attendees becomes challenging with the traditional approach. To address these issues, a web-based attendance system with facial recognition capabilities has been developed. This system integrates an existing web camera with a facial recognition system, enabling automatic and efficient attendance tracking with minimal setup requirements. Through this web application, lecturers have the option to reject student attendance if they violate rules and regulations, and they can also generate attendance reports. Similarly, students can view their attendance percentage for each registered course. Additionally, this research proposes the integration of smart contract-based blockchain technology into the system. In conclusion, this system aims to assist lecturers in monitoring student attendance by utilizing facial recognition technology with an accuracy rate of 98%, while the proposed smart contract ensures secure and efficient management of attendance records. 2023 Conference or Workshop Item PeerReviewed Mohd. Azli, Amirul Mukhlis and Mammi, Hazinah Kutty and Mat Din, Mazura and Abdul-Samad, Adlina (2023) Face-recognition based attendance authentication system. In: 2023 International Conference on Data Science and Its Applications (ICoDSA), 9 August 2023-10 August 2023, Bandung, Indonesia. http://dx.doi.org/10.1109/ICoDSA58501.2023.10276698 |
| spellingShingle | QA75 Electronic computers. Computer science Mohd. Azli, Amirul Mukhlis Mammi, Hazinah Kutty Mat Din, Mazura Abdul-Samad, Adlina Face-recognition based attendance authentication system |
| title | Face-recognition based attendance authentication system |
| title_full | Face-recognition based attendance authentication system |
| title_fullStr | Face-recognition based attendance authentication system |
| title_full_unstemmed | Face-recognition based attendance authentication system |
| title_short | Face-recognition based attendance authentication system |
| title_sort | face recognition based attendance authentication system |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT mohdazliamirulmukhlis facerecognitionbasedattendanceauthenticationsystem AT mammihazinahkutty facerecognitionbasedattendanceauthenticationsystem AT matdinmazura facerecognitionbasedattendanceauthenticationsystem AT abdulsamadadlina facerecognitionbasedattendanceauthenticationsystem |