David Haar

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Hand Detection with a Cascade of BoostedClassifiers Using Haar-like Features

Qing ChenDiscover Lab, SITE, University of Ottawa

May 2, 2006


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Outline 1. Introduction

2. Haar-like features

3. Adaboost

4. The Cascade of Classifiers

5. Preliminary Results

6. Future Work


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1. Introduction Hand-based Human Computer Interface (HCI) should

meet the requirements of real-time, accuracy androbustness.

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 whichachieved high detection accuracy and approximately 15

times faster than any previous approaches. The algorithm is a generic objects detection/recognition

method.


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2. Haar-Like Features Each Haar-like feature consists of two or three jointed black and white

rectangles:

The value of a Haar-like featureis the difference between the sum of thepixel gray level values within the black and white rectangular regions:

f(x)=Sumblack rectangle (pixel gray level)Sumwhite rectangle (pixel gray level)

Compared with raw pixel values, Haar-like features can reduce/increasethe 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.


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2. Haar-Like Features (contd) The rectangle Haar-like features can be computed rapidly using

integral image.

Integral image at location ofx, y contains the sum of the pixel

values above and left ofx,y, inclusive:

The sum of pixel values within D:

yyxx

yxiyxP','

)','(),(

A B

C D

P2

P3 P4

P1

P (x, y)

DCABADCBAAPPPP

DCBAPCAPBAPAP

3241

4321 ,,,


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2. Haar-Like Features (contd) To detect the hand, the image is scanned by a sub-window containing a

Haar-like feature.

Based on each Haar-like feature fj, a weak classifierhj(x)is defined as:

where xis a sub-window, and is a threshold. pjindicating the direction

of the inequality sign.


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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 constructa strong classifier using only a training set and a weak learningalgorithm. A weak classifier with the minimum classification error isselected by the learning algorithm at each iteration.

AdaBoost is adaptive in the sense that later classifiers are tuned up infavor of those sub-windows misclassified by previous classifiers.


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3. Adaboost (contd) The algorithm:


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Adaboost starts with a uniformdistribution of weights over trainingexamples. The weights tell the learningalgorithm the importance of the example.

Obtain a weak classifier from the weak

learning algorithm, hj(x).

Increase the weights on the trainingexamples that were misclassified.

(Repeat)

At the end, carefully make a linearcombination of the weak classifiersobtained at all iterations.

)()()( ,11,final xxx nnfinalfinal hhf

3. Adaboost (contd)


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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.


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4. The Cascade of Classifiers (contd) Negative samples: non-object

images. Negative samples aretaken from arbitrary images.These images must not containobject representations.

Positive samples: images containobject (hand in our case). Thehand in the positive samples mustbe marked out for classifiertraining.


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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


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6. Future Work Extended Haar-like features? Will

extended Haar-like features improvethe detection accuracy? (Still an OpenProblem) The performance tradeoff?

Parallel cascades for multiple hand

gestures. How to select the handgesture configurations which can bedetected more effectively with theemployed Haar-like feature set?

Improve the robustness against handrotation.

How much improvement can beachieved with more training samples?Intel face detection classifier: 5000 Pos.10000 Neg. Accuracy: 98%


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References: Wu Bo, et al., A Multi-View Face Detection Based on Real Adaboost Algorithm,Computer

Research and Development, 42 (9)pp.1612-16212005.

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 Klsch and Matthew Turk, Robust Hand Detection,Proc.IEEE Intl. Conference on

Automatic Face and Gesture Recognition, May 2004.

Intel OpenCV Documents.

Acknowledgement goes to Urthos training data for eye detection and F. Dadgostars hand

palm database.


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Thank you and Any Questions?