Efficient Attendance Monitoring System (EAMS) Using Haar Cascade and Local Binary Pattern Histogram Algorithm

This article proposes an innovative approach to streamline student attendance verification through the application of Artificial Intelligence (AI). The primary objective is to equip teachers with a fast and convenient face authentication method, implemented using the Python programming language. The proposed system incorporates two key processes: face detection using Haar Cascades, and face recognition via Local Binary Pattern Histograms. The system's efficacy was evaluated through an experiment involving students from the Faculty of Education at Nakhon Ratchasima Rajabhat University, using video clips from surveillance cameras. The experimental results revealed an impressive identification accuracy of 94.40% for a sample of 15 students. These findings confirm the suitability of Haar Cascades and Local Binary Pattern Histograms in developing an efficient student attendance verification system that employs AI technology.