Lecture 29: Face Detection Revisited

1. CS4670 / 5670: Computer Vision Noah Snavely Lecture 29: Face Detection Revisited

2. Remember eigenfaces? • They don’t work very well for detection

3. Issues: speed, features • Case study: Viola Jones face detector • Exploits two key strategies: • simple, super-efficient, but useful features • pruning (cascaded classifiers) • Next few slides adapted Grauman & Liebe’s tutorial • http://www.vision.ee.ethz.ch/~bleibe/teaching/tutorial-aaai08/ • Also see Paul Viola’s talk (video) • http://www.cs.washington.edu/education/courses/577/04sp/contents.html#DM

4. Feature extraction “Rectangular” filters Feature output is difference between adjacent regions Value at (x,y) is sum of pixels above and to the left of (x,y) Efficiently computable with integral image: any sum can be computed in constant time Avoid scaling images  scale features directly for same cost Integral image Viola & Jones, CVPR 2001 4 K. Grauman, B. Leibe K. Grauman, B. Leibe

5. Integral Image Weights on Integral Image Original Image Weight on Original Image IIR Filter Integral Image Slide courtesy of Andrew Gallagher

6. Integral Image Weights on Integral Image Original Image Weight on Original Image IIR Filter Integral Image Slide courtesy of Andrew Gallagher

7. Integral Image Weights on Integral Image Original Image Weight on Original Image IIR Filter Integral Image Slide courtesy of Andrew Gallagher

8. Integral Image Weights on Integral Image Original Image Weight on Original Image IIR Filter Integral Image Slide courtesy of Andrew Gallagher

9. Integral Image Weights on Integral Image Original Image Weight on Original Image IIR Filter Integral Image Slide courtesy of Andrew Gallagher

10. Integral Image Weights on Integral Image Original Image Weight on Original Image IIR Filter Integral Image Slide courtesy of Andrew Gallagher

11. Integral Image Weights on Integral Image Original Image Weight on Original Image IIR Filter Integral Image Slide courtesy of Andrew Gallagher

12. Integral Image Weights on Integral Image Original Image Weight on Original Image IIR Filter O(N) Operations! ~400 in this case Integral Image Slide courtesy of Andrew Gallagher

13. Integral Image Sum Cost Total Cost SS 69 6 6 69 4 4 Integral Original Image SS: 17+4+6+15+22+5= 69 Integral way: 313+87-194-137= 69 Integral Image Slide courtesy of Andrew Gallagher

14. Integral Image Sum Cost Total Cost SS 352 24 30 352 4 8 Integral Original Image Integral way: 911+156-247-468= 352 Integral Image Slide courtesy of Andrew Gallagher

15. Integral Image Sum Cost Total Cost SS 141 9 39 141 4 12 Integral Original Image Integral way: 762-621= 141 Integral Image Slide courtesy of Andrew Gallagher

16. Large library of filters Considering all possible filter parameters: position, scale, and type: 180,000+ possible features associated with each 24 x 24 window Use AdaBoost both to select the informative features and to form the classifier Viola & Jones, CVPR 2001 K. Grauman, B. Leibe

17. AdaBoost for feature+classifier selection Want to select the single rectangle feature and threshold that best separates positive (faces) and negative (non-faces) training examples, in terms of weighted error. Resulting weak classifier: For next round, reweight the examples according to errors, choose another filter/threshold combo. … Outputs of a possible rectangle feature on faces and non-faces. Viola & Jones, CVPR 2001 K. Grauman, B. Leibe

18. AdaBoost: Intuition Consider a 2-d feature space with positive and negative examples. Each weak classifier splits the training examples with at least 50% accuracy. Examples misclassified by a previous weak learner are given more emphasis at future rounds. Figure adapted from Freund and Schapire 18 K. Grauman, B. Leibe K. Grauman, B. Leibe

19. AdaBoost: Intuition 19 K. Grauman, B. Leibe K. Grauman, B. Leibe

20. AdaBoost: Intuition Final classifier is combination of the weak classifiers 20 K. Grauman, B. Leibe K. Grauman, B. Leibe

21. AdaBoost Algorithm Start with uniform weights on training examples {x1,…xn} For T rounds Evaluate weighted error for each feature, pick best. Re-weight the examples: Incorrectly classified -> more weight Correctly classified -> less weight Final classifier is combination of the weak ones, weighted according to error they had. Freund & Schapire 1995 K. Grauman, B. Leibe

22. Cascading classifiers for detection Fleuret & Geman, IJCV 2001 Rowley et al., PAMI 1998 Viola & Jones, CVPR 2001 For efficiency, apply less accurate but faster classifiers first to immediately discard windows that clearly appear to be negative; e.g., • Filter for promising regions with an initial inexpensive classifier • Build a chain of classifiers, choosing cheap ones with low false negative rates early in the chain 22 Figure from Viola & Jones CVPR 2001 K. Grauman, B. Leibe K. Grauman, B. Leibe

23. Viola-Jones Face Detector: Summary Train cascade of classifiers with AdaBoost Apply to each subwindow Faces New image Selected features, thresholds, and weights Non-faces Train with 5K positives, 350M negatives Real-time detector using 38 layer cascade 6061 features in total throughout layers [Implementation available in OpenCV: http://www.intel.com/technology/computing/opencv/] 23 K. Grauman, B. Leibe

24. Viola-Jones Face Detector: Results First two features selected 24 K. Grauman, B. Leibe K. Grauman, B. Leibe

25. Viola-Jones Face Detector: Results K. Grauman, B. Leibe

26. Viola-Jones Face Detector: Results K. Grauman, B. Leibe

27. Viola-Jones Face Detector: Results K. Grauman, B. Leibe

28. Detecting profile faces? Detecting profile faces requires training separate detector with profile examples. K. Grauman, B. Leibe

29. Viola-Jones Face Detector: Results Paul Viola, ICCV tutorial K. Grauman, B. Leibe

30. Questions?