Hand Detection with a Cascade of Boosted Classifiers Using Haarlike Features

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Hand Detection with a Cascade of Boosted
Classifiers Using Haar-like Features

• Qing Chen
• Discover 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 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.

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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 feature is the
  difference between the sum of the pixel 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/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.
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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 of x, y contains the
  sum of the pixel values above and left of x, y,
  inclusive
• The sum of pixel values within D

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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
  classifier hj(x) is defined as where x is a
  sub-window, and ? is a threshold. pj indicating
  the direction of the inequality sign.

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3. Adaboost

• The computation cost using Haar-like
  featuresExample 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
  (320-241)X(240-241)X15966,735
  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.

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3. Adaboost (contd)

• The algorithm

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3. Adaboost (contd)

• 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, hj(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.

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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 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.

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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 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

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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.1612-1621,2005.
• 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 Kölsch 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?