Evaluation Method of Real-Time Face Detection

Abstract

Object detection is one of computer technology using image or video. Face detection is closely related Image processing and computer vision are used to detect several objects including human faces, landscapes, cars, etc. Face detection algorithm aims to confirm if an image has a face as the object in it. In this study, face detection uses several methods, namely the Eigenface method, the Fisherface method, and the Local Binary Pattern Histogram (LBPH) method. This study used 10 different subjects. The test was carried out 15 times using each face detection method with constant distance. The face detection process in this study was simulated using JupyterLab. The result showed that LBPH method obtained the highest level of accuracy in between comparison among Fisherface method and the Eigenface method. The accuracy of the LBPH method is 93.90%, while the Eigenface method is 85% and the Fisherface method is 53.33%. Differences in face detection accuracy were found due to the low level of lighting in the room and the use of accessories on the subject.