Face Recognition Based Attendance System Using Haar Cascade Algorithm with Histogram Equalization and Median Blur Filter

Abstract

The attendance system is one of the mandatory activities in teaching and learning activities in an educational environment. Unfortunately, many still use a manual attendance system, which is inefficient, and fraud often occurs by manipulating attendance data. In this research, a face recognition-based attendance system was created. The face detection and recognition system use the haar cascade method with image preprocessing, namely histogram equalization and median blur filter. This system can provide output in the form of CSV documents containing attendance data that has been done in real time so that attendance data recording becomes more efficient than before. There is a significant difference in face recognition without and with image preprocessing. Without image preprocessing, the average face recognition accuracy rate is 71.2%. In face recognition with image preprocessing, the face recognition accuracy rate is 91.5%. Therefore, the use of image preprocessing can improve image quality and significantly increase the accuracy of face recognition. In addition, the CSV document is successfully generated automatically after the attendance process is carried out, which is equipped with user data, attendance date, and time when the user makes attendance to avoid fraud in the form of manipulation of attendance data by users.