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Real-time Face Recognition System using Python OpenCV

Original price was: ₹199.00.Current price is: ₹55.00.

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Real-time face recognition in Python using OpenCV and deep learning models without external libraries. Ideal for security and access control applications

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This Python script enables real-time face recognition leveraging OpenCV for video capture and processing, paired with deep learning models MTCNN and InceptionResnetV1 from PyTorch’s facenet_pytorch library. The system processes live video streams, detects faces, extracts facial embeddings, and compares them against known faces stored locally. This solution is designed for applications requiring robust security measures and access control, ensuring reliable identification without relying on external face recognition libraries. It serves as an educational tool or as a foundation for integrating facial recognition into broader software systems

Installation Guide:

To use this Face Recognition System, follow these steps:

  1. Install Dependencies:
    • Python 3.x (recommended)
    • OpenCV (opencv-python package)
    • NumPy (numpy package)
    • PyTorch (torch package)
    • facenet_pytorch (facenet-pytorch package)

    Install these dependencies using pip

    pip install opencv-python numpy torch facenet-pytorch
  2. Download the Source Code: Download the provided Python script for the Face Recognition System.
  3. Prepare Your Environment:
    • Organize your face images into a folder (images by default). Please add meaningful images to this folder according to person. example Asif.jpg. Now you are ready to for face recognition .
    • Ensure your webcam or video source is correctly connected and accessible.
  4. Run the Script:
    • Open a terminal or command prompt.
    • Navigate to the directory where you saved the script.
    • Run the script using Python:
      python face_recognition_system.py
  5. Usage:
    • The script will open a live video feed from your webcam.
    • It will detect and recognize faces in real-time against the stored face images.
    • Detected faces will be annotated with their names if recognized.
  6. Interacting with the System:
    • Press q to exit the application.

This guide provides a structured approach to install and utilize the Face Recognition System using Python and OpenCV, ensuring seamless integration and operational efficiency. Subscribe to our YouTube channel and be the first to know about advanced Python projects!

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