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.
00:10 You’ve also learned how AdaBoost finds the best-performing Haar-like features from thousands of available features and turns them into a series of weak classifiers, which are then used to rapidly distinguish faces from non-faces through cascading.
00:29 These steps illustrate many important elements of computer vision such as finding useful features, combining them to solve complex problems like face detection, and balancing between performance and managing computational resources so that our algorithm is accurate, but not too slow.
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