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2.1 KiB
2.1 KiB
Face Recognition Attendance System with Engagement Detection
Tier: 3-Advanced
Attendance tracking in classrooms is time-consuming and prone to human error. Presenz automates this process using real-time face recognition, detecting not only who is present but also whether students are engaged — flagging behaviors like phone usage and sleeping with snapshot evidence.
This project challenges the developer to combine computer vision, a real-time web interface, and session-based reporting into a single cohesive system.
User Stories
- User can start a live camera session that detects and recognizes faces in real time
- User can register student faces into the system before a session begins
- User views a live dashboard showing attendance status for each recognized student
- User can see flagged events (phone usage, sleeping) with timestamps and snapshot images
- User can export attendance records for a session as a CSV file
- Attendance is automatically marked absent when a flagged behavior is detected
Bonus features
- User can set a minimum attendance threshold and receive an alert when a student falls below it
- User can view session history with per-student engagement scores over time
- User can support multiple classrooms with separate student rosters
- Admin can add or remove students from the system without restarting the server
- System sends an email or SMS notification to flagged students after a session
Useful links and resources
- OpenCV Haar Cascade face detection
- LBPH face recognizer
- YOLOv8 object detection
- Flask-SocketIO for real-time communication
- Head pose estimation with OpenCV