Student Attendance Monitoring System using Facial Recognition

Abstract

In this age of rapidly evolving technologies and trends, performing routine tasks like Attendance Recording should no longer follow the manual or semi-manual tiresome methods. Taking students’ attendance records in the classroom during a lecture period is now a common practice in the University systems especially in most private institutions in Nigeria; a case study employed is Babcock University, Ilisan Remo, Ogun State, Nigeria. This helps to account for where a student is per time, increases student’s learning focus and helps the institution to make the right decision for instance whether to allow a student to take the final exam or not. Attendance marking using conventional methods such as calling students’ names one by one or having them to write it could be quite tedious and time wasting. It becomes more difficult to manage when the class size is large. Another challenge includes the possibility of capturing a proxy attendance such as students writing for their absent colleagues. This study proposes to solve this problem using a biometric information processing known as the facial recognition system. Its application is easier with working range larger than others such as fingerprint, iris scanning and signature. Many algorithms and techniques have been developed to improve facial recognition performance, but the proposed model employs Cascade Classifier which breaks the problem of detecting faces into multiple stages. For each stage, the algorithm performs a very rough and quick test, and if this current stage passes, it does a slightly more detailed test. It eventually detects a face if all stages are passed. The implementation tools include Python, HTML, MySQL, PyCharm, XAMP server as the local host server and a web browser to register and display results. As a result, this development makes attendance monitoring simple, efficient, and time saving. Keywords: Attendance, Monitoring, Biometric, Facial Recognition, Cascade Classifier.