Real-Time Facial Recognition Based Student Attendance Management

The focus of this study is mainly on biometrics for face recognition applications in the educational field, to design an automated student attendance management system. The research problem tackled by this work was the inefficacy and potential inaccuracy of conventional attendance procedures including manual roll-calling and fingerprint-based systems, which are very time-consuming, very error-prone, and easily manipulated by proxy attendance. In an effort to contribute solutions, the project adopts a four-phase approach-comprising database creation, face detection, face recognition, and update of attendance. These phases utilize OpenCV for image processing and DLIB algorithm to detect and recognize student faces through webcam integration. Preliminary results demonstrate that the system drastically reduces attendance-marking time and minimizes cases of early leaving or tarantism. The significance of the research is in the accuracy and efficiency it brings to attendance tracking in educational settings, hence providing a robust alternative to many traditional methods and enhancing the large-scale adoption of automated systems in education.