Team Machine Learners is Tirth G Shah, Mayurkumar Patel, Deep Patel and Anushka Rodi.
Abstract
This project introduces “Attendance Log API” a sophisticated cloud-based attendance management system that leverages the advanced capabilities of Microsoft Azure and the power of artificial intelligence. The system is designed to automate and streamline the process of attendance tracking in real-time.
At the heart of the system are two main components: a front-end user interface and a dedicated face-recognition module. The front-end, developed using React and hosted on an Azure Virtual Machine (VM), provides an interactive platform. It allows users to access live camera feeds for attendance capturing, manage attendance records, and perform administrative tasks such as adding or removing users.
The face-recognition module, also deployed on a separate Azure VM, is the core technological element of this system. It utilizes advanced face recognition algorithms to analyze camera feed frames, thereby identifying individuals in real time. This module is built using Python libraries such as Flask, OpenCV, and Dlib, alongside a PostgreSQL database for storing attendance logs and image data.
The system is designed with a focus on scalability, efficiency, and cost-effectiveness. The daily operational cost is optimized, making it an affordable solution for real-time attendance management. Moreover, future enhancements are planned to include a more structured and segregated architecture for improved scalability, a more intuitive user interface, and the integration of more accurate and lighter models for face recognition.
“Attendance Log API” represents a significant step forward in integrating cloud computing and AI technologies for practical and innovative solutions in attendance management. Its modular architecture and reliance on Azure’s robust cloud services make it an adaptable and efficient tool for modern organizational needs.