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 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

Project Screenshot

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.