This project demonstrates a simple face recognition system using OpenCV and the Haar Cascade classifiers. The system captures video from the camera, detects faces, and recognizes them using machine learning algorithms.
- Face detection using Haar Cascade classifiers
- Face recognition using Local Binary Patterns Histograms (LBPH) Face Recognizer
- Training the recognizer with images
- Real-time face recognition from the video stream
- Saving and loading face data using Pickle
- Python 3.6 or later
- OpenCV
- NumPy
- Pillow (PIL)
- Pickle
- Clone the repository or download the source code.
- Install the required packages using the following command:
pip install -r requirements.txt
Run the Python scripts for training the recognizer and performing real-time face recognition.
This project contains three Python scripts:
-
train.py - This script preprocesses the images, detects faces using Haar Cascade classifiers, trains the LBPH face recognizer, and saves the trained data and labels.
-
faces.py- This script captures the video stream from the camera, detects faces, and recognizes them using the trained data. The recognized person's name is displayed on the video stream.
-
detect.py - This script captures the video stream from the camera and detects faces using Haar Cascade classifiers. It demonstrates face detection without the recognition functionality.