FACIAL RECOGNITION ATTENDANCE SYSTEM USING CONVOLUTIONAL NEURAL NETWORK
Abstract
This project presents the development of a Facial Recognition Attendance System using Convolutional Neural Networks (CNN). Traditional attendance systems are time-consuming, prone to manipulation, and inefficient. The proposed system utilizes deep learning and computer vision techniques to automatically detect and recognize faces in real time. A CNN model is trained using a labeled facial dataset to accurately identify individuals. The system captures live video through a camera, processes facial features, and marks attendance automatically in a database. Experimental results show high accuracy and reliability under controlled lighting conditions. The system reduces manual effort, eliminates proxy attendance, and enhances security and record management in educational institutions and organizations.
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