1. Hand Detection with a Cascade of Boosted Classifiers Using Haar-like Features Qing Chen Discover Lab, SITE, University of Ottawa May 2, 2006

2. 2 Outline  1. Introduction  2. Haar-like features  3. Adaboost  4. The Cascade of Classifiers  5. Preliminary Results  6. Future Work

3. 3 1. Introduction  Hand-based Human Computer Interface (HCI) should meet the requirements of real-time, accuracy and robustness.  The purpose of Haar-like features is to meet the real-time requirement.  The purpose of the cascade of Adaboosted (Adaptive boost) classifiers is to achieve both accuracy and speed.  The algorithm has been used for face detection which achieved high detection accuracy and approximately 15 times faster than any previous approaches.  The algorithm is a generic objects detection/recognition method.

4. 4 2. Haar-Like Features  Each Haar-like feature consists of two or three jointed “ black ” and “ white ” rectangles:  The value of a Haar-like feature is the difference between the sum of the pixel gray level values within the black and white rectangular regions: f(x)=Sum black rectangle (pixel gray level) – Sum white rectangle (pixel gray level)  Compared with raw pixel values, Haar-like features can reduce/increase the in-class/out-of-class variability, and thus making classification easier. Figure 1: A set of basic Haar-like features. Figure 2: A set of extended Haar-like features.

5. 5 2. Haar-Like Features (cont ’ d)  The rectangle Haar-like features can be computed rapidly using “ integral image ”.  Integral image at location of x, y contains the sum of the pixel values above and left of x, y, inclusive:  The sum of pixel values within “ D ” : AB C D P2P2 P3P3 P4P4 P1P1 P (x, y)

6. 6 2. Haar-Like Features (cont ’ d)  To detect the hand, the image is scanned by a sub-window containing a Haar-like feature.  Based on each Haar-like feature f j, a weak classifier h j (x) is defined as: where x is a sub-window, and θ is a threshold. p j indicating the direction of the inequality sign.

7. 7 3. Adaboost  The computation cost using Haar-like features: Example: original image size: 320X240, sub-window size: 24X24, frame rate: 15 frame/second, The total number of sub-windows with one Haar-like feature per second: (320-24+1)X(240-24+1)X15=966,735 Considering the scaling factor and the total number of Haar-like features, the computation cost is huge.  AdaBoost (Adaptive Boost) is an iterative learning algorithm to construct a “ strong ” classifier using only a training set and a “ weak ” learning algorithm. A “ weak ” classifier with the minimum classification error is selected by the learning algorithm at each iteration.  AdaBoost is adaptive in the sense that later classifiers are tuned up in favor of those sub-windows misclassified by previous classifiers.

8. 8 3. Adaboost (cont ’ d)  The algorithm:

9. 9  Adaboost starts with a uniform distribution of “ weights ” over training examples. The weights tell the learning algorithm the importance of the example.  Obtain a weak classifier from the weak learning algorithm, h j (x).  Increase the weights on the training examples that were misclassified.  (Repeat)  At the end, carefully make a linear combination of the weak classifiers obtained at all iterations. 3. Adaboost (cont ’ d)

10. 10 4. The Cascade of Classifiers  A series of classifiers are applied to every sub-window.  The first classifier eliminates a large number of negative sub-windows and pass almost all positive sub-windows (high false positive rate) with very little processing.  Subsequent layers eliminate additional negatives sub-windows (passed by the first classifier) but require more computation.  After several stages of processing the number of negative sub-windows have been reduced radically.

11. 11 4. The Cascade of Classifiers (cont ’ d)  Negative samples: non-object images. Negative samples are taken from arbitrary images. These images must not contain object representations.  Positive samples: images contain object (hand in our case). The hand in the positive samples must be marked out for classifier training.

12. 12 5. Preliminary Results  Number of pos. samples: 144  Number of neg. samples: 3142  Sample Resolution: 640X480  Initial sub-window size: 15X30  Scale factor: 1.3  Cascade obtained: 12 grades

13. 13 6. Future Work  Extended Haar-like features? Will extended Haar-like features improve the detection accuracy? (Still an Open Problem) The performance tradeoff?  Parallel cascades for multiple hand gestures. How to select the hand gesture configurations which can be detected more effectively with the employed Haar-like feature set?  Improve the robustness against hand rotation.  How much improvement can be achieved with more training samples? Intel face detection classifier: 5000 Pos. 10000 Neg. Accuracy: 98%

14. 14 References:  Wu Bo, et al., “ A Multi-View Face Detection Based on Real Adaboost Algorithm, ” Computer Research and Development, 42 (9)：pp.1612-1621，2005.  Paul Viola and Michael J. Jones, “ Robust Real-time Object Detection, ” Technical Report, Cambridge Research Lab, Compaq. 2001.  Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies, “ Analysis of Boosting Algorithms using the Smooth Margin Function: A Study of Three Algorithms, ” 2004.  Rainer Lienhart, Alexander Kuranov, Vadim Pisarevsky, “ Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection, ” MRL Technical Report, May 2002.  Andre L. C. Barczak, Farhad Dadgostar, “ Real-time Hand Tracking Using a Set of Cooperative Classifiers and Haar-Like Features, ” Research Letters in the Information and Mathematical Sciences, ISSN 1175-2777, Vol. 7, pp 29-42, 2005.  Mathias K ö lsch and Matthew Turk, “ Robust Hand Detection, ” Proc. IEEE Intl. Conference on Automatic Face and Gesture Recognition, May 2004.  Intel OpenCV Documents.  Acknowledgement goes to Urtho ’ s training data for eye detection and F. Dadgostar ’ s hand palm database.

15. 15 Thank you and Any Questions?