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A Brief Introduction on Face Detection

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

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A Brief Introduction on Face Detection

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

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

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

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

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

  18. Feature selection • A week classifier h f1 f2 f1 < θ (a threshold) => Face! f2 > θ (a threshold) => Not a Face! 1 if fi < θ h = 0 otherwise

  19. Feature selection • Idea: Combining several weak classifiers to generate a strong classifier …… w1 w3 w2 wn w1h1+w2h2+w3h3+…+wnhn T >< ~performance of the weak classifier on the training set a week classifier h1 = 1 or 0

  20. Feature selection • Training Dataset • 4916 face images • non-face images cropped from 9500 images non-face images positive samples negative 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. Initial weights for each data point AdaBoost -∞ ∞ decreased decreased increased fi 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 the detection window Sub-window n m n m n m n Image m

  26. 1 2 3 Cascade method • Most are non-face images! • Rejects negative sub-windows in an early stage Strong Classifier = (W1xL1+ W2xL2) + (…)+ (…+ WnxLn) T ><

  27. Feature Extraction Cascade Detection Summary 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.

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