1. Rapid Object Detection using a Boosted Cascade of Simple Features Paul Viola, Michael Jones Conference on Computer Vision and Pattern Recognition 2001 ( CVPR 2001 )

2. Outline Introduction Features Learning Classification Functions The Attentional Cascade Result

3. Introduction

4. Three Contribution New image representation - Integral image Method for constructing a classifier - Selecting a small number of important features using AdaBoost Method for combining classifiers - In a cascade structure

5. Features

6. Three Kind of Features Two-rectangle Three-rectangle Four-rectangle Feature value = sum of pixel value in white area - sum of pixel value in black area

7. Integral Image

8. Rectangular Sum RectangularSum Location A1 B2-1 C3-1 D4+1-(2+3)

9. Learning Classification Function

10. Very small number of features can form an effective classifier Select best classifier feature Weak classifier

11. AdaBoost algorithm

13. Learning Result A frontal face classifier - 200 features (among 180,000) - Detection rate: 95% - False positive rate: 1/14084 - 0.7s to scan an 384*288 pixel image First feature selected - The eyes is often darker than the nose and cheeks Second feature selected - The eyes are darker than the bridge of the nose

14. The Attentional Cascade

15. Cascade

16. Training a cascade of classifiers Tradeoffs o Features↑ ↔ detection rates ↑ o Features↑ ↔ computational time ↓ Constructing stages o Training classifiers using AdaBoost o Adjust the threshold to minimize false negative

17. Result

18. Result Face training set o 4916 faces image o 24*24 pixels o 9544 image o 350 million sub-windows The complete face detection cascade has o 38 stages o 6061 features o 15 times faster than current system

19. Performance

20. Performance

21. Result

22. Thank you for your attention!