SecureFace: Enhancing Student Safety with Face Recognition Attendance Tracking

Abstract

Introduction: Attending low-cost tracking systems Reliable organization of employee holidays and employee absenteeism. Student protection are equally important in academic settings. A pioneering practice of facial recognition technology aptly named SecureFace makes monitoring of attendance, reducing possibility of some mistakes and the general administrative cost while at the same time improving security measures. By employing sophisticated machine learning techniques and findings in current research facial recognition software available, SecureFace instantly and authenticates students, with a view of taking students attendance without physical contact. This method not only enhance the business operation efficiency and at the same time provides security by minimizing access to people without permit to visit the facility. The study analyses the technical aspect of the system. Significant in architecture, with possible challengessuch as the light conditions are variable and recommending remedies that need to be labeled for accuracy guarantee.SecureFace aims at establishing an enclosed, fast and safe, and the type of learning environment of today through advanced technology related solutions.

The primary objective of the SecureFace system is to enhance student safety and automate attendance tracking through facial recognition technology. By implementing a secure and reliable biometric authentication system, the solution aims to eliminate manual attendance errors and prevent fraudulent practices such as proxy attendance. The system is designed to achieve high accuracy and robustness by leveraging deep learning models like CNNs and FaceNet, ensuring reliable identification even under varying lighting conditions and facial orientations. Additionally, SecureFace prioritizes data security and privacy by incorporating encryption techniques and adhering to relevant data protection regulations. The system is also structured for scalability, making it suitable for institutions of different sizes while optimizing performance for real-time processing.