AI-Powered Face Recognition-Based Attendance System Using Computer Vision and Machine Learning

Conventional attendance management systems, including manual registers and identity cardbased verification methods, are increasingly inadequate in meeting the demands of modern educational institutions and professional environments. These traditional approaches are inherently susceptible to proxy attendance, human error, and administrative inefficiencies that compromise the integrity of attendance records. This paper proposes an AI-powered Face Recognition-Based Attendance System that leverages the capabilities of computer vision and machine learning to fully automate and secure the attendance tracking process. The system utilizes a real-time camera interface to continuously capture facial images, which are subsequently processed through advanced face detection algorithms to identify and isolate individual facial regions. Extracted facial features are then compared against a pre-trained database using deep learning-based recognition models to accurately verify individual identities. Upon successful identification, attendance records are automatically updated in a centralized digital database with precise Time stamps, eliminating the need for manual intervention. The proposed system demonstrates significant improvements in accuracy, operational efficiency, and resistance to fraudulent practices such as buddy punching and proxy attendance. Designed with scalability and adaptability in mind, the system is suitable for deployment across diverse environments including universities, corporate offices, and high- security facilities.