Smart School Attendance System using Face Recognition with Near Optimal Imaging

This work aimed to develop a students' school attendance system with a web application and IoT devices as the two main parts. The web application was designed to manipulate the student data acquisition while IoT was used for face recognition to detect student attendance. This process applied Haar cascade and EBGM approaches to classify and distinguish faces for the web application. The web application was implemented with PHP language and a MySQL database provided for usage by three user groups: teachers, students and staff. The face recognition system was implemented on Raspberry Pi to connect with web application and record the student time attendance data. This work improves the accuracy of face detection by use of physical factors. We found that the current face recognition algorithm is highly efficient if the face position is arranged for clear image capture. Thus, this work also focuses on finding the right position to capture the user’s face. The experimental test provided 100% accuracy of student classifier when they stand in a suitable position for imaging. The results show that the suitable position depends on the student face training count. In addition, the distance between camera and standing student affects the accuracy of face recognition.