Face And Eye Biometric Attendance Recognition

Abstract

In the modern digital era, attendance management systems play a significant role in educational institutions, corporate offices, healthcare organizations, industries, and government sectors. Traditional attendance systems such as manual registers, ID card systems, and fingerprint-based attendance methods suffer from several drawbacks including time consumption, proxy attendance, human errors, lack of automation, and hygiene concerns. To overcome these issues, biometric attendance systems based on face and eye recognition technologies have gained popularity due to their contactless, secure, and automated nature. The Face and Eye Biometric Attendance Recognition System combine computer vision, image processing, machine learning, and biometric authentication techniques to automatically identify individuals and mark attendance in real time. The system uses face recognition algorithms to identify users based on facial features and integrates eye detection mechanisms for liveness verification and improved security. Eye detection helps prevent spoofing attacks using photographs or videos and ensures that the attendance is marked only for a real person. Thisresearch survey presents a detailed study of face and eye biometric attendance systems, including their architecture, methodologies, algorithms, implementation techniques, hardware and software technologies, performance analysis, advantages, limitations, applications, and future developments. The survey also reviews the evolution of biometric systems and highlights recent advancements in deep learning-based recognition techniques. The findings indicate that integrating face recognition with eye biometric verification significantly improves attendance accuracy, security, automation, and reliability compared to traditional attendance methods.