Face Detection and Recognition for Automatic Attendance System

Abstract

Human face recognition is an important part of biometric verification. The methods for utilizing physical properties, such as human face have seen a great change since the emergence of image processing techniques. Human face recognition is widely used for verification purposes, especially if individuals attend to lectures. There is a lot of time lost in classical attendance confirmations. In order to solve this time loss, an Attendance System with Face Recognition has been developed which automatically tracks the attendance status of the students. The Attendance System with Face Recognition performs daily activities of the attendance analysis which is an important aspect of face recognition task. By doing this in an automated manner, it saves time and effort in classrooms and meetings. In the scope of the proposed system, a camera attached to the front of the classroom continuously captures the images of the students, detects the faces in the images, compares them with the database, and thus the participation of the student is determined. Haar filtered AdaBoost is used to detect the real-time human face. Principal Component Analysis (PCA) and Local Binary Pattern Histograms (LBPH) algorithms have been used to identify the faces detected. The paired face is then used to mark course attendance. By using the Attendance System with Facial Recognition, the efficiency of lecture times’ utilization will be improved. Additionally, it will be possible to eliminate mistakes on attendance sheets.