The project is designed to detect faces in real time, identify them, and display their Name + ID above their detected faces. It combines computer vision, feature extraction, and real time GUI display to create an interactive system for monitoring or attendance purposes.
- Python 3.11 (not tested for other versions)
- A working webcam
- pip (Python package manager)
- Extract the zip file & Navigate to the project's directory
- Create a python virtual environment
- Install the required libraries found in requirements.txt
- Install Visual Studio (preferably 2022) and Desktop Development with C++ workload
- Install CMake and add it to environment variables
- Extract the zip file & Navigate to the project's directory
- Create a python virtual environment and activate it
- Run each one of these commands separately and in the same order:
pip install cmake
pip install dlib
pip install face_recognition
pip install opencv-contrib-python flask
- Run the flask application
python app.py
- After running, open your browser and navigate to:
http://127.0.0.1:5000
- The web interface will open automatically, showing the Live Feed page. From there, you can:
- Start or stop detection
- Register new faces
- View known faces
- Check system statistics
| Member | Role | Responsibilities |
|---|---|---|
| Khaled Mohamed | 🧠 Face Detection | - Implemented real-time face detection. - Extracted facial features and performed recognition. - Optimized accuracy and speed of detection in live video. |
| George Bassem | 💻 GUI (Frontend & Integration) | - Designed and implemented the graphical user interface. - Displayed live camera feed with detected faces and overlays. - Ensured an interactive, clean, and user-friendly experience. |
| Mariam Nabeh | 📊 Data Management | - Prepared and managed the face dataset. - Labeled each face with correct Name and ID. - Preprocessed images (resizing, grayscale, normalization). |