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 Conclusion

3. Outline Introduction Features Learning classification functions The attentional cascade Result Conclusion

4. Introduction New object detection framework  Motive Face recognition  Characteristics Robust Rapid

5. Contributions 1. New image representation  Integral image 2. Method for constructing a classifier  Selecting a small number of important features using AdaBoost 3. Method for combining classifiers  in a cascade structure

6. Application Rapid face detector can be used in  User interfaces  Image databases  Teleconferencing Especially, …  Allow for post-processing When rapid frame-rates are not necessary  Can be implemented on small low power devices Handhelds, embedded processors

7. Outline Introduction Features Learning classification functions The attentional cascade Result Conclusion

8. Features Why not pixels?  The most common reason Features can encode ad-hoc domain knowledge  The critical reason for this system Feature based system operates much faster 3 kind of features used  Two-rectangle feature  Three-rectangle feature  Four-rectangle feature

9. Integral Image ( x,y ) ( 0,0 ) integral image original image

10. Rectangular sum Location A1 B2-1 C3-1 D4+1-(2+3)

11. Outline Introduction Features Learning classification functions The attentional cascade Result Conclusion

12. Learning classification functions Hypothesis  Very small number of features can form an effective classifier How to find  Select the single rectangle feature which best separates the positive and negative examples Weak classifier Result  Features selected in early round Error rate: 0.1~0.3  Features selected in later round Error rate: 0.4~0.5 threshold featurepolarity

13. AdaBoost algorithm

14. 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  Not sufficient 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

15. Outline Introduction Features Learning classification functions The attentional cascade Result Conclusion

16. The attentional cascade Constructing goal Reject many of the negative sub-window Detect almost all positive instances  False negative rate → 0 Cascade

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

18. Outline Introduction Features Learning classification functions The attentional cascade Result Conclusion

19. Result Face training set  4916 hand labeled faces  Resolution: 24*24 pixels  Source: random crawl of the WWW  9544 manually inspected image  350 million sub-windows The complete face detection cascade has  38 stages  6061 features  15 times faster than current system Layer 12345 features 11025 50

20. Performance Receiver operating characteristic (ROC) What’s ROC? (please reference http://www.geocities.com/shinyuanclub/update97/lucm0115.html )http://www.geocities.com/shinyuanclub/update97/lucm0115.html

21. Performance comparison Detection rates for various numbers of false positives on the MIT+CMU test set containing 130 images and 507faces

22. Outline Introduction Features Learning classification functions The attentional cascade Result Conclusion

23. Conclusions An approach for object detection  Minimize computation time 15 times faster than any previous approach  Achieve high detection accuracy false negative false positive