Traditional Face Detection With Python: Conclusion
Good work! You are now able to find faces in images. In this course, you learned how to represent regions in an image with Haar-like features. These features can be calculated very quickly using integral images.
You learned how AdaBoost finds the best performing Haar-like features from thousands of available features and turns them into a series of weak classifiers. Finally, you learned how to create a cascade of weak classifiers that can quickly and reliably distinguish faces from non-faces.
These steps illustrate many important elements of computer vision:
- Finding useful features
- Combining them to solve complex problems
- Balancing between performance and managing computational resources
These ideas apply to object detection in general and will help you solve many real-world challenges. Good luck!
Congratulations, you made it to the end of the course! What’s your #1 takeaway or favorite thing you learned? How are you going to put your newfound skills to use? Leave a comment in the discussion section and let us know.
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