Robust and Real-Time Deep Learning System for Checking Student Attendance

A face detection and identification algorithm is an interesting research topic. The performance of existing face detection and identification systems works well under normal lighting conditions, while their performance is not stable under difficult conditions due to noise and illumination changes. Therefore, this research aims to develop a robust and real-time deep learning system for student face detection and identification to overcome these current limitations. The proposed method investigates both benefits of state-of-the-art deep learning models and local patterns to create a robust frame-work for detecting and checking student attendance. Comprehensive experimental results show that the proposed method obtained stable results under various normal and difficult indoor conditions. The proposed method obtains the detection rate of 93.55% and 89.25% under normal and difficult indoor conditions, respectively. The proposed method obtains the identification rate of 87.79% and 85.19% under normal and difficult indoor conditions, respectively.