A Deep Learning-Based Face Recognition Model for Comprehensive Student Logging Mechanism Using Tkinter

In a student’s academic journey, one of the paramount factors is attendance. Attending classes is of utmost importance as it directly correlates with a student’s ability to grasp and comprehend the material presented by educators. To maintain satisfactory academic progress, students are typically required to achieve a minimum attendance threshold, often set at 75%, failure to meet which may result in fines or even the loss of an entire academic year. In some cases, students resort to various methods, including employing proxies, to manipulate their attendance records. To streamline this process, educational institutions have started implementing innovative attendance tracking systems, such as the one discussed in this study. This system utilizes a Convolutional Neural Network (CNN) algorithm, integrated with a Tkinter graphical user interface (GUI) and the Python-based face recognition package. The implementation of this technology not only enhances efficiency but also reduces the time and effort previously spent on manual attendance taking. In this system, a group photograph of students serves as input, and the output provides an accurate count of the classes attended by each student, simplifying attendance monitoring for both students and educators.