Make sure you have Python installed on your machine.
- Create a virtual environment:
python -m venv venv
Activate the virtual environment:
On Windows:
.\venv\Scripts\activate
On macOS/Linux:
source venv/bin/activate
-
Install required libraries:
pip install opencv-python numpy dlib imutils scikit-learn
-
Real Face Dataset:
Collect a dataset of real faces for training. You can use a dataset like Labeled Faces in the Wild (LFW) or create your own.
-
Fake Face Dataset:
Collect a dataset of fake faces (spoofed faces). This can include images of printed photos, digital screens, or other methods that can be used to fool facial recognition.
-
Image Preprocessing:
Resize and normalize the images to prepare them for training.
-
Model Training:
Train a machine learning model using a library like Dlib or OpenCV. You can use a Convolutional Neural Network (CNN) for better accuracy.
-
Model Evaluation:
Evaluate the model on a separate set of real and fake face images to assess its accuracy.
-
Integration with Webcam:
Use the trained model to process live video feed from a webcam and detect whether the face is real or fake.
-
Implement Alert System:
If a fake face is detected, implement an alert system, such as a message or sound notification.