The Student Attendance System is an innovative solution designed to automate the attendance process in educational institutions, specifically tailored for the Faculty of Computing and Technology. Utilizing advanced face recognition technology, this system aims to streamline attendance tracking in real-time, thereby enhancing the accuracy and efficiency of this routine yet critical academic operation.
- Automated Attendance Tracking: Leverages AI-powered face recognition to automate student attendance during lectures.
- Real-Time Database Updates: Seamlessly updates attendance records in a real-time database, ensuring timely and accurate attendance data.
- Front-End Functionality: Provides a user-friendly interface for managing attendance records, generating reports, and accessing lecture timetables.
- Report Generation: Enables faculty members to easily generate attendance reports for individual students, classes, or custom date ranges.
- Lecture Timetable Integration: Displays up-to-date lecture timetables, facilitating better planning and scheduling for students and lecturers.
Our project addresses several challenges faced by the Faculty of Computing and Technology in managing attendance:
- Reduces Administrative Workload: By automating attendance recording, our system significantly reduces the administrative burden on faculty members, allowing them to focus more on teaching and less on paperwork.
- Improves Accuracy: The use of face recognition minimizes errors associated with traditional attendance methods, such as buddy punching or manual roll calls.
- Enhances Student Accountability: With automated tracking, students are encouraged to attend classes regularly, positively impacting their academic performance.
- Facilitates Better Resource Management: Real-time attendance data can help in analyzing class participation trends, assisting in better resource allocation and planning.
- Supports Remote Learning: Our system is adaptable to various teaching modalities, including hybrid and remote learning environments, ensuring consistent attendance tracking.
- Front-End: C#
- Back-End: Python, with AI algorithms for face recognition
- Database: Real-time database solution for immediate data reflection and access
(Here you can include steps to clone the repo, install dependencies, and any initial setup required.)
(Provide instructions on how to use the system, from launching the front-end application to performing common tasks such as viewing attendance reports or updating timetables.)
We welcome contributions from students and faculty members. Whether it's feature suggestions, bug reports, or code contributions, please feel free to make your impact.
- MIT License
Our project is the result of the hard work and dedication of a talented group of individuals. Meet the team behind the Student Attendance System using Face Recognition:
- Pawan Perera - CS/2020/005
- Y.P.Viduruwan- CS/2020/006
- Oshada Weerasiri - CS/2020/071
- [Name] - stu_no
- [Name] - stu_no
- [Name] - stu_no
We are students from the Faculty of Computing and Technology university of Kelaniya, passionate about leveraging technology to solve real-world problems. Our diverse backgrounds and skills have contributed to the development of a robust and efficient attendance system.
We would like to extend our gratitude to our faculty mentors and professors who guided us through the process, providing invaluable feedback and support. Their expertise has been instrumental in shaping this project.