Design and Development of an Intelligent Facial Recognition System for Automated Employee Attendance and Time Tracking

Abstract

In today’s digital world, ensuring secure and reliable authentication has become essential in workplaces and educational institutions. Traditional attendance methods, such as manual registers and card-based systems, often prove to be inefficient, error-prone, and vulnerable to issues like proxy attendance and data manipulation. Although biometric techniques like fingerprint and card-based authentication are widely used, they still face limitations in terms of scalability, speed, and user convenience. Facial recognition has emerged as a contactless and user-friendly biometric solution that provides a more secure and efficient approach to identity verification. Additionally, these systems often lack proper integration of time-based tracking features. This work focuses on the design and implementation of an AI-based facial recognition system for automated employee attendance and time logging. The proposed system utilizes advanced machine learning and computer vision techniques to detect and recognize faces in real time while accurately recording entry and exit times. By incorporating efficient feature extraction and one-shot learning methods, the system is capable of delivering reliable performance even with limited training data. Overall, this approach minimizes manual effort, prevents proxy attendance, and enhances the integrity of attendance records. Keywords— Facial Recognition, Automated Attendance System, Temporal Logging, Machine Learning, Computer Vision, One-Shot Learning, Biometric Authentication, Real-Time Face Detection, Workforce Management, Data Integrity