This project implements a face recognition system using machine learning techniques. The goal is to detect and recognize faces in images or video streams with high accuracy. The core implementation is provided in the Jupyter Notebook FaceRecognition.ipynb.
- Face Detection: Identifies faces in images using pre-trained models.
- Feature Extraction: Extracts facial embeddings using deep learning models.
- Face Recognition: Matches detected faces against a known database.
- Visualization: Displays detected faces with labels.
To run this project, install the following dependencies:
pip install numpy pandas opencv-python dlib face-recognition matplotlib- Clone the repository:
git clone https://github.com/Mehranalam/FaceRecognition.git cd FaceRecognition - Open the Jupyter Notebook:
jupyter notebook FaceRecognition.ipynb
- Run the cells step by step to detect and recognize faces.
The notebook provides:
- Detected face bounding boxes
- Labeled recognized faces
- Evaluation of recognition accuracy
- Improve recognition accuracy with deep learning techniques.
- Extend to real-time face recognition from live video.
- Implement user-friendly interface for practical applications.
- Mehran Alam
- GitHub: Mehranalam
This project is open-source and available under the MIT License.