Improving Face Attendance Checking System with Ensemble Learning

An accurate and efficient attendance system is essential in modern industrial and educational settings. However, traditional methods often suffer from inaccuracies, inefficiencies, and vulnerability to fraud. Recent work usually utilizes a single model for employing the face recognition system, leading to many failures due to the model's low performance. This paper presents an advanced face attendance system using ensemble learning to overcome the limitations of single-model approaches. The proposed system achieves near-perfect accuracy under optimal conditions by integrating the strengths of multiple deep learning models, including ResNet, VGGFace, and FaceNet. The ensemble approach boosts the robustness and reliability of the attendance system, making it a promising solution for real-world deployment in educational and workplace environments. The key contribution of this work is the development of a face attendance system that utilizes the complementary capabilities of different models to deliver significantly improved accuracy and resilience compared to standalone methods.