This project uses computer vision and text-to-speech capabilities to detect student focus based on face presence. The system uses OpenCV’s Haar Cascade Classifier to detect faces in real-time from a webcam feed and Pyttsx3 for audio alerts if the student appears unfocused.
- Real-time face detection using OpenCV and Haar Cascade Classifier.
- Voice feedback using Pyttsx3, alerting when no face is detected (indicating the student might not be focused).
- Customizable parameters for detection accuracy and alert frequency.
- Python 3.x
- OpenCV
- Pyttsx3
- Numpy
Install the dependencies using:
pip install opencv-python-headless pyttsx3 numpy- Make sure a webcam is connected.
- Run the script:
python focus_detection.py - A window named “Student” will open, showing a rectangle around detected faces. If no face is detected, a voice prompt will alert that the student is not focused.
- Press “q” to quit the application.
- Enhanced Focus Detection: Add eye-tracking functionality to better assess focus by detecting if eyes are closed or averted.
- Data Logging: Implement a logging mechanism to track focus patterns over time, potentially storing timestamps and duration of inattentiveness for further analysis.
- Multi-Student Support: Expand the system to handle multiple faces, useful for group settings or classrooms.
- Improved Voice Feedback: Allow customizable messages or audio tones based on the detected face’s behavior, with configurable intervals for repetitive alerts.
- Emotion Recognition: Use a more advanced model to assess facial expressions, identifying emotions like boredom or distraction.
- GUI Integration: Add a simple graphical interface to make it user-friendly, allowing users to configure settings directly.