Inspiration

The idea came from the need to automate traditional attendance systems which are time-consuming and prone to manipulation. We wanted to build a smart system using AI that can recognize faces, detect emotions, and provide a seamless and secure attendance solution.

What it does

This system captures real-time video from a webcam and uses AI to recognize faces. Once a face is recognized, the system marks attendance automatically, detects the person's emotion, provides voice feedback, and sends the data to a backend server. The attendance is stored in a MySQL database and displayed on a live dashboard.

How we built it

We used Python with OpenCV for capturing video and DeepFace for face recognition and emotion detection. A Spring Boot backend was developed to handle REST APIs and store data in a MySQL database. The frontend dashboard was built using HTML, CSS, and JavaScript, which displays attendance records in real-time.

Challenges we ran into

We faced challenges in integrating Python with the Spring Boot backend, handling duplicate attendance entries, and managing real-time performance. Additionally, suppressing unnecessary logs and ensuring smooth emotion detection without delays required optimization.

Accomplishments that we're proud of

We successfully built a complete AI-based system integrating computer vision, backend development, and real-time UI. Features like emotion detection, voice feedback, and image capture make this system more advanced than a basic attendance system.

What we learned

We learned how to integrate AI with backend systems, work with REST APIs, handle real-time data processing, and design a full-stack application. This project also improved our understanding of computer vision and system architecture.

What's next for AI-Powered Face Attendance & Monitoring System

We plan to add face anti-spoofing, cloud deployment, mobile application support, and enhanced security features like role-based access and analytics dashboards.

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