Face- Recognition Based Attendance System Using Machine Learning

Abstract

In The rapid advancement of digital technology and the growing need for automation have led to the increased adoption of biometric systems for tasks such as identity verification and attendance tracking. Among these, facial recognition has emerged as a non-intrusive and efficient method. This paper presents a facial recognition-based automatic attendance system that eliminates the need for manual processes. The system uses Haar Cascade Classifier for face detection and a trained classifier model for recognizing individuals from real-time video input. It integrates image acquisition, feature extraction, face recognition, and automated attendance logging into a unified framework. A performance evaluation of the system is conducted using metrics such as accuracy, processing efficiency, and robustness under varying conditions. The proposed system demonstrates high accuracy and reliability, offering a practical solution to automate attendance systems while reducing manual intervention and improving data management.