Face Attendance System Using Machine Learning

The adoption of artificial intelligence (AI) and computer vision has transformed how organizations handle routine administrative processes. Attendance management, which once relied on manual registers or card-based systems, continues to be a bottleneck in many institutions due to inefficiency, human error, and security vulnerabilities. This study proposes a machine learning–based Face Attendance System that offers a contactless, accurate, and real-time attendance solution. By leveraging advanced face detection and recognition algorithms, the system automates the entire attendance cycle—from user registration to secure storage of attendance logs. The system architecture integrates Python’s face_recognition library for encoding, FastAPI for backend communication, and pickle for database persistence. Results indicate that the model can recognize and authenticate individuals in real time under varying environmental conditions. The proposed framework not only reduces administrative overhead but also minimizes proxy attendance (buddy punching), enhances hygiene by eliminating contact-based inputs, and provides scalability for institutions ranging from small offices to large universities. Furthermore, the modular architecture enables future integrations such as cloud storage, mobile-based accessibility, and advanced analytics dashboards, positioning it as a next-generation solution for digital transformation in organizational workflows.