Twice_Face_Recognition

🌟 TWICE Face Recognition with Haar Cascade - Embrace the Future of K-Pop Computer Vision! 🌟
📌 Project Description
Embark on an innovative journey with our pioneering project, where I merge the realms of K-pop and advanced computer vision. This endeavor is dedicated to creating a highly sophisticated face recognition system for the iconic K-pop girl group, TWICE. By integrating the power of the Haar Cascade algorithm with the cutting-edge capabilities of TensorFlow and Keras, I'm setting a new standard in fan engagement and idol recognition technology.

🚀 Project Overview
Goal: To build a nuanced and highly efficient face recognition system tailored for the enchanting members of TWICE. Method: A synergetic approach utilizing Haar Cascade classifiers alongside the deep learning prowess of TensorFlow and Keras. Tech Toolkit: A harmonious blend of Python, OpenCV, TensorFlow, TensorFlow Keras, and HaarCascade Classifiers.

🌈 Features
Advanced Recognition Capabilities: Harnessing deep learning for enhanced accuracy and speed in identifying TWICE members. Robust and Versatile: Excelling under varied conditions, capturing the unique essence of each member in any scenario. Rich Dataset: An extensive and diverse collection of TWICE images, optimized for deep learning models.

💡 Applications
Next-Level Fan Experience: Elevate how TWICE enthusiasts interact and recognize their favorite idols through photos. Seamless App Integration: Ideal for integrating into fan-based apps and interactive K-pop platforms. Frontier in Idol Recognition Research: A stride forward in the field of K-pop idol recognition, blending computer vision with deep learning. 🌟 Getting Started
Follow our comprehensive guide for a smooth setup. Whether for development or fandom purposes, this project is an exciting venture! 🖼️

🤝 Contributions
Be a part of this groundbreaking project.

👏 Acknowledgments
A special thank you to TWICE and JYP Entertainment for inspiring this project. Gratitude to the OpenCV and TensorFlow communities for their invaluable resources and support.

🔥 Awesome Model Repository
Welcome to our cutting-edge model repository! 🚀 In this repository, we house a powerful and sophisticated model that's too large to be uploaded directly to GitHub. But worry not! We've made it available for download through the link below: Download Our State-of-the-Art Model

🌐 How to Use
To integrate our model into your project, follow these simple steps:

Download the model using the provided link.
Download or Clone the Repository: Start by downloading the repository using the following command or by cloning it:
```
git clone https://github.com/your-username/your-repository.git
```
Install Dependencies: Navigate to the project directory and install the required libraries mentioned in requirements.txt:
```
cd your-repository
pip install -r requirements.txt
```
Run Webcam App or Image Input App: If you want to experience the real-time power of our model with a webcam feed, run the following command:

python Twice_streamlit_webcam_app.py

This will launch the webcam app, showcasing the model's capabilities on live video input. If you prefer using static images as input, you can run the image input app with the following command:

python streamlit_image.py

This will allow you to input image data and observe the model's predictions.

Feel free to customize the apps according to your needs, and don't hesitate to explore the codebase. If you encounter any issues or have suggestions, feel free to open an issue or submit a pull request.

Happy experimenting! 🚀

B0ndan/Twice_Face_Recognition

Twice_Face_Recognition