The development of a face recognition-based mobile application for student attendance recording

Abstract

Attendance-taking has been practised in most learning institutions, schools, colleges, and universities to monitor the students’ commitment towards their studies. The traditional way of attendance-taking is usually performed by passing a piece of paper to be signed by all the students in the class. The main drawback is proxy attendance, whereby a student can sign on behalf of their friend. Students frequently miss classes are at a higher risk of missing important information, discussions, demonstrations, and assignments, leading to knowledge gaps and hindering their overall academic progress. Additionally, traditional attendance-taking takes significant time for the paper to circulate among the students, especially in larger classrooms. This can result in wasted instructional time and disrupt the class flow. The students might be distracted, and their focus on the class material might also be disrupted. Regarding record management, paper-based attendance records can easily get misplaced, damaged, or lost. Retrieving and analysing attendance data becomes time-consuming when papers must be manually sorted. Not only that, considering the post-Covid pandemic nowadays, everyone is still expected to minimise physical contact to reduce the possibility of infection. This, however, is quite impossible if manual attendance-taking is practised because everyone needs to touch the same paper to sign the attendance. Therefore, in this project, a face recognition-based mobile application is proposed to facilitate attendance-taking while minimising the drawbacks of the traditional attendance-taking approach. The main content of this article will be based on the phases involved in the development process. The output interface of the proposed system will be presented from both lecturers’ and students’ views. Finally, this paper is ended with the system limitations and recommendations for future work.

Keywords

• attendance-taking
• face recognition
• post-covid