Face Recognition Using TensorFlow Lite

Overview

This project implements real-time face recognition using a TensorFlow Lite-based model for efficient, on-device processing. It ensures low latency and high accuracy while running seamlessly on both Android and iOS platforms. The frontend is developed using React Native, leveraging HTML, JavaScript, and React.js to provide a responsive and consistent user experience across devices.

Features

• On-Device Face Recognition: Uses TensorFlow Lite for fast and efficient processing without relying on cloud services.
• Cross-Platform Compatibility: Works on both Android and iOS devices.
• Optimized Performance: Low-latency face detection with high accuracy.
• React Native Frontend: Provides an interactive and mobile-friendly interface.

Technologies Used

• Frontend: React Native (HTML, JavaScript, React.js)
• Backend: TensorFlow Lite (for on-device face recognition)

Installation & Setup

1. Clone the repository:

   git clone https://github.com/your-username/face-recognition-tflite.git
   cd face-recognition-tflite

2. Install dependencies:

   npm install

3. Ensure your mobile device has the required permissions for camera access.
4. Run the application:

   npx react-native run-android  # For Android
   npx react-native run-ios      # For iOS

Replace image-path.png with the actual path of your uploaded image in the repository.

Contributing

Feel free to submit issues or pull requests to enhance the project.

License

This project is open-source and available under the MIT License.