AI Attendance & Attention Detection

An AI-based student attendance and attention monitoring system using face recognition and computer vision techniques.

Overview

This project combines face recognition with attention monitoring to automatically identify students, record attendance, and estimate their attention level during class sessions.

Features

• Student face recognition for attendance tracking
• Automatic attendance recording
• Attention monitoring
• Head Pose Estimation (HPE)
• Eye Aspect Ratio (EAR) analysis
• Attention scoring
• Student database management
• Attendance data export to Excel
• Face image management
• Graphical user interface

AI & Computer Vision

Face Recognition

The system uses FaceNet512 through DeepFace for student face recognition and identification.

Attention Detection

The attention monitoring module combines:

• Head Pose Estimation (HPE) — analyzes the student's head orientation
• Eye Aspect Ratio (EAR) — helps detect eye closure and visual attention
• Attention Scoring — combines visual indicators to estimate an attention score

Tech Stack

• Language: Python
• Face Recognition: DeepFace / FaceNet512
• Computer Vision: OpenCV
• Attention Detection: Head Pose Estimation, Eye Aspect Ratio
• Database: SQLite
• Data Export: Excel
• GUI: Python-based interface

Project Structure

AI Attendance & Attention Detection/
├── app/
├── db/
├── excel_exports/
├── faces/
├── utils/
│   ├── attention_detector.py
│   ├── attention_scorer.py
│   ├── db_utils.py
│   ├── deepface_utils.py
│   ├── eye_detector.py
│   ├── migrate_db.py
│   └── pose_estimation.py
├── add_attention_table.py
├── app.py
├── requirements
├── setup.py
├── run_app
├── run_app.sh
└── .gitignore