Automated Attendance System Using Opencv With Face And Iris Detection

Abstract

The "Automated Attendance System Using OpenCV, Face and Iris Detection" is an advanced solution designed to replace outdated and insecure traditional attendance systems. By leveraging modern technologies such as OpenCV, artificial intelligence (AI), and Internet of Things (IoT) integration, the system ensures accurate, secure, and user-friendly attendance tracking. It utilizes real-time face detection and recognition to identify individuals, while incorporating eye-blink detection as a liveness check to prevent spoofing attempts using photos or videos.

OpenCV serves as the core computer vision engine for detecting facial features, while AI-driven models improve recognition accuracy and adaptability. Eye aspect ratio (EAR) calculations help confirm the user’s physical presence by detecting natural blinking patterns. In secure environments, iris detection adds an extra biometric layer for identity verification. The system can be implemented on IoT-enabled devices such as Raspberry Pi for portability and real-time processing.

This solution is ideal for educational institutions, corporate offices, and smart environments. It automates attendance logging, generates reports, and synchronizes data with centralized systems. The integration of computer vision and AI not only improves efficiency but also ensures security and scalability, making this system a powerful step forward in smart attendance technology.