note: If you're facing any deprecation issues, consider cloning and installing the DeepFace repository directly from GitHub, and then importing the functions from the local source. You can find the official repository here: github.com/serengil/deepface
This repository contains a comprehensive face recognition system that combines YOLOv8 for face detection and FaceNet for face recognition. Face recognition is a critical technology with applications in security, surveillance, and user authentication. This project leverages state-of-the-art deep learning models to achieve accurate and reliable face recognition.
- Detect faces in images using YOLO.
- Crop and save the detected faces.
- Recognize faces using DeepFace and categorize them based on a pre-trained model.
- Do whatever you feel like from the features extracted.
Experience the functionality of our face recognition model, which has been trained on a dataset featuring faces of few politicians, by visiting Live Demo. Please keep in mind that this deployment is specifically designed for demonstration purposes and may not be fine-tuned for optimal performance in real-world scenarios. The best place to start and learn about the project's evolution is the "o-testing" folder. Here, you'll find different levels of understanding, providing comprehensive insights into the project's development.
testing.mp4
Acknowledgement : For details on the deployment of the model using Streamlit, refer to this repository.
Follow these steps to set up the project locally:
# Clone the repository
git clone https://github.com/sOR-o/Face-Recognition.git
cd Face-Recognition
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
pip install deepface
pip install -e .
# Ready to go!-Can be improved by transfer learning (obviously 😉)
Contributions to this project are welcome! If you'd like to contribute, feel free to submit issues, feature requests, or pull requests.