An Enhanced Student Attendance Monitoring System at Low Resolution and Various Angled Face Positions based on LBPH and CLAHE

This study offers an efficient real-time face recognition system with low image resolution variations and diverse angular positions of face. We have generated our own dataset for training and classification. The facial photos are first taken using an HD 1080p camera, and after preprocessing using CLAHE, a noise reduction is done where median filtering is used. Gaussian filtering also been used while capturing face in recognition process. This technology also effectively recognizes faces in varying lightning conditions. According to the study, accuracy rises as pixel resolution rises and declines when face deviation angle rises. For feature extraction, we have utilized the LBPH method here. If attendance is collected by hand, teachers may find it difficult to keep track of their students. Further system is improvised to collect attendance of students. The system that is being used creates an excel sheet in addition to recording attendance. This approach will be a successful technique for keeping track of students’ attendance.