Face Detection in Live Video Using Python and OpenCV
https://github.com/olaiyayomi/Face-Detection-in-Live-Video-using-Python-computer-vision-OpenCv-
This project implements face detection in live video streams using the OpenCV library in Python. By utilizing template matching, the script identifies and highlights the presence of a predefined face template in real-time.

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Objective

The aim of this project is to detect specific faces in live video feeds using a face template image. This can serve as the foundation for more advanced facial recognition or tracking systems.

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Features

• Real-Time Face Detection: Detects faces in a live video stream captured from your computer's webcam.
• Template Matching: Uses a predefined face template to identify matching areas in each video frame.
• User Interaction: Press the 'q' key to exit the program.

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Prerequisites

• Python 3.x
• OpenCV library

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Setup and Installation

1. Make sure Python is installed on your computer.
2. Install OpenCV by running:

   pip install opencv-python

3. Download the live_face_detection.py script and a predefined face template image.
4. Ensure that your webcam is functional.

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How It Works

1. Video Capture: The program captures video from the default webcam on your system.
2. Template Loading: A predefined face template image is read into the program.
3. Template Matching: Each frame is compared to the template using OpenCV’s template matching function.
4. Detection and Marking: If a match is found, the detected face is marked in the video feed.
5. User Interaction: Pressing 'q' exits the program.

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Project Files

• live_face_detection.py: The main script for face detection in live video.
• Screenshot 2024-01-20 180909.png: Sample output showing the face detection in action.
• README.md: Detailed documentation for the project.

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Tags

#Python #OpenCV #ComputerVision #ArtificialIntelligence #MachineLearning #RealTimeProcessing #FaceDetection #Programming #Coding #TemplateMatching

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Applications

This project demonstrates the foundational principles of facial detection and can be extended to:

• Build advanced facial recognition systems.
• Create automated video surveillance tools.
• Develop real-time video analytics solutions.