Detect Faces in Videos you own this product

prerequisites
intermediate Python • beginner scikit-learn and scikit-image • basics of OpenCV
skills learned
detect and crop faces • normalize and scale faces • align faces according to the eyes locations
Pavel Korshunov
1 week · 8-10 hours per week · INTERMEDIATE
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Face detection has numerous applications across software and is an essential part of the pipeline for detecting deepfake videos. In this liveProject, you’ll implement a component that detects faces in videos, and normalizes them to the same size and visual appearance. This component is a vital foundation for a deepfake detection system.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Pavel Korshunov
Pavel Korshunov is a researcher at Idiap Research Institute, Switzerland, working on detection of audio-visual inconsistencies and Deepfakes. Previously, he worked on problems related to high dynamic range imaging, crowdsourcing, and visual privacy. He received PhD from National University of Singapore and MSc from St. Petersburg State University, Russia. He has over 70 research papers with several best paper awards and is a co-editor of JPEG XT standard.

prerequisites

This liveProject is for developers who know Python, the basics of machine learning, and the basics of processing image data. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of Jupyter Notebook
  • Basics of OpenCV
  • Basics of scikit-learn
  • Basics of Matplotlib
  • Basics of scikit-image
TECHNIQUES
  • Basic signal and image processing
  • Basic image feature and metrics computation
  • Basic understanding of how classification systems are evaluated
  • Basic understanding of classification with linear support vector machine

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