This project implements a simple and efficient face recognition system using Python. The system is capable of detecting and recognizing faces from images or real-time video streams. It leverages modern libraries such as OpenCV for image processing and face_recognition for face detection and recognition.
- Face Detection: Detects faces in images or video streams in real-time using OpenCV.
- Face Recognition: Matches detected faces with known faces from a pre-trained database.
- Real-time Performance: Processes live video feed and recognizes faces in real-time.
- Scalable: Easily add or remove people from the face database.
- High Accuracy: Utilizes the dlib library's highly accurate face recognition models.
- Python 3.x
- OpenCV
- dlib
- face_recognition (uses dlib under the hood)
- NumPy
- Clone this repository:
git clone https://github.com/aary20/face-recognition-using-python.git
- Install required dependencies:
pip install -r requirements.txt- To recognize faces from an image:
python program.py --image <path_to_image>
- For real-time face recognition using a webcam:
python program.py --webcam- Place the images of new faces in the
known_faces/ directory. - Run the encoding script to add new faces to the database:
python encode_faces.py- known_faces/ # Directory containing images of known individuals
- face_encodings/ # Stored encodings of known faces
- recognize_faces.py # Main script for face recognition
- encode_faces.py # Script for encoding known faces
- requirements.txt # List of dependencies- face_recognition library by Adam Geitgey for providing an easy-to-use and powerful face recognition API.
- OpenCV for its extensive library of computer vision functions.
- dlib for its robust machine learning models, particularly in face detection and facial landmark detection.
- The open-source community for their helpful tutorials and discussions on face recognition and computer vision.