1. A Brief Introduction on Face Detection Mei-Chen Yeh 04/06/2010 P. Viola and M. J. Jones, Robust Real-Time Face Detection, IJCV 2004.

2. Object Detection Find the location of an object if it appear in an image –Does the object appear? –Where is it?

3. Applications

4. George colony

5. Challenges 1: view point variation Michelangelo 1475-1564 Slide (5-10) credit: Fei-Fei Li

6. Challenges 2: illumination slide credit: S. Ullman

7. Challenges 3: occlusion Magritte, 1957

8. Challenges 4: scale

9. Challenges 5: deformation Xu, Beihong 1943

10. Challenges 6: background clutter Klimt, 1913

11. Detection Framework Viola and Jones’ face-detection algorithm –The first object detection framework to provide competitive object detection rates in real-time –Implemented in OpenCV Components –Features Haar-features Integral image (speed up the feature calculation) –Learning AdaBoost algorithm –Cascade method (speed up the detection process)

12. Haar-features The difference between pixels’ sum of the white and black areas Based on the face symmetry

13. Haar-features Too many features! –Different locations –Different scales Speed-up strategy –Fast calculation of haar-features –Selection of good features 24

14. Integral image Sum of pixel values in the blue area Example: 21 2 3 4 3 32 1 2 2 3 42 1 1 1 2 Image 2 3 5 8 12 15 5 8 11 16 22 28 9 14 18 24 31 39 Integral image

15. 1 3 2 ab c d a = sum(1) b = sum(1+2) c = sum(1+3) d = sum(1+2+3+4) Sum(4) = ? 4 d + a – b – c Four-point calculation! A, B: 2 rectangles => C: 3 rectangles => D: 4 rectangles => 6-point 8-point 9-point

16. A 24x24 detection window Four types of haar features Type A The feature pool

17. Feature selection A very small number of features can be combined to from an effective classifier! Example: The 1 st and 2 nd features selected by AdaBoost

18. Feature selection A week classifier h f1f1 f2f2 f 1 Face! f 2 > θ (a threshold) => Not a Face! h = 1 if f i < θ 0 otherwise

19. Feature selection Idea: Combining several weak classifiers to generate a strong classifier w1w1 w2w2 w3w3 wnwn …… w1h1+w2h2+w3h3+…+wnhnw1h1+w2h2+w3h3+…+wnhn ><>< T a week classifier h 1 = 1 or 0 ~ performance of the weak classifier on the training set

20. Feature selection Training Dataset –4916 face images –non-face images cropped from 9500 images non-face images positive samplesnegative samples

21. AdaBoost Each training sample may have different importance! Focuses more on previously misclassified samples –Initially, all samples are assigned equal weights –Weights may change at each boosting round misclassified samples => increase their weights correctly classified samples => decrease their weights

22. AdaBoost decreased increased fifi Initial weights for each data point -∞∞ misclassified

23. AdaBoost ……

24. Learning the classifier Initialize equal weights to training samples For T rounds –normalize the weights –select the best weak classifier in terms of the weighted error –update the weights (set increased weights to misclassified samples) Linearly combine these T week classifiers to form a strong classifier

25. Detection procedure Scans the detector at multiple locations and scales Sub-window m m m m n n n n Image the detection window

26. Cascade method Most are non-face images! Rejects negative sub-windows in an early stage Strong Classifier = (W 1 xL 1 + W 2 xL 2 ) + (…)+ (…+ W n xL n ) 123 ><>< T

27. Summary Feature Extraction Cascade Detection Face Detection

28. Resources OpenCV Library C codes –apps/haartraining –samples/c Detectors –data/haarcascades

29. Assignment #3 Due 04/20 11:59pm Build a face detector using the OpenCV resources Send to TA –A picture of yours with detected faces –Subject: Multimedia System Design-Assignment #3-your student id-your name –File name: Assignment #3-your student id-your name For Windows users, you might want to try an older version of OpenCV

30. Reminder Brainstorm report (a 2-page formal paper) is due on 4/20.