The Indian Institute of Information Technology, Kottayam (IIITK)

Brain Station 23 has developed a machine learning-powered face recognition system to automate the attendance tracking. The system was initially trained on a large dataset of over 2,500 student’s faces and after refining the training data for a month, the final product achieved a 95.6% accuracy rate in identifying individual students. The system also allows for real-time training to improve accuracy.

To utilize the system, students simply need to login to their LMS via mobile phone and select a specific class in that LMS. The system provides the option to activate the camera, which captures an image of the student. The image is then processed through the face recognition model, and if the student is successfully identified, their attendance for that class is recorded automatically. The system streamlines the attendance tracking process and eliminates the need for manual efforts.

The face recognition system has been highly effective with minimal false identification and high accuracy. The system has received positive feedback from students who appreciate its convenience, while faculty no longer need to spend time manually taking attendance.

Furthermore, with the help of its attendance tracking capabilities, the system has also improved overall class attendance by allowing faculty to set specific times for attendance system where students just need to submit their attendance on that specific time. This reduces the occurrence of proxy attendance. The system’s capacity to accurately identify individual students also open up the possibility for future usage in restricting access to campus facilities, such as dormitories or research labs, to only authorized individuals.