CSEM Scientific and Technical Report 2008

Hand Detection Using Boosted Classifiers
D. Hasler, K. Ali
This research explores the potential of boosted classifiers for object detection. These classifiers combine simple decision rules devised “by hand”
and result in a powerful classification scheme. The detection of the presence of hands in an image is chosen to test the classification framework.
Hands have been chosen for two reasons: the system can be applied for security in manufacturing plants, and the problem of hand detection is
sufficiently hard, given the variability in appearance, so as to be confident with respect to the generalization of the algorithm to other objects.
Boosted classifiers [1] have been studied intensively in the past
few years, and are applied, for example, for face detection in
modern digital cameras. Boosted classifiers offer several
advantages:
• The boosting framework provides a theoretical guarantee
that the resulting classification does not overfit the training
data; in other words, there is a good chance that if a
system has been trained to detect hands in a particular
video sequence, it will also be able to detect the hands in
a different setup.
• The detection is based on simple rules that are designed
by hand with a few free (unknown) parameters. Unlike
neural networks, which tend to behave like black boxes,
boosted classifier can take advantage of the knowledge of
a human expert in an easier way: the expert designs the
simple rule, telling the system what kind of information is
relevant to the detection in the image, and the boosting
does the rest.
• The resulting detector can be made computationally very
efficient.
The hand detection is applied on the contrast and orientation
image delivered by the DEVISE [ 2] camera and its vision
sensor. These contrast and orientation values are computed
on the vision sensor chip, and are robust to illumination
changes.
To detect hands, the algorithm starts by pre-processing the
image: the image is thresholded and the orientation is
quantified in 8 values.
The simple decision rule is as follows:
Given the thresholded image and a sliding window,
• Choose a sub-window at coordinate (x,y) with size (w,h),
and an orientation O (O ∈ {0..7}).
• Count the number of pixels with orientation O in the subwindow,
and normalize this number by the sum of the
(contrast) pixels in the sub-window to allow for even
greater robustness to illumination changes.
• Compare this result to a threshold T and a parity P: There
is hand in the image if the result is greater than T and the
parity is greater than zero or if the result is smaller than T
and the parity is smaller than zero.
This simple rule is given to the boosted classifier, which will
choose parameters (x,y,w,h,O,P,T), and sample a number of
these rules. These rules will vote about the presence of the
hand. The detector is nothing but the result of this vote, with
some trivial post-processing that allows for the removal of
repeated hits.
In this particular example, the expert decided that the relevant
information for detecting a hand is the geometrical
configuration of contrast edges of given orientations in the
image.
At a 95% detection rate, the classifier obtained in using this
technique gives one false alarm every 100’000 tests or every
11 images (a sliding window that performs 9’000 tests per
image is used).
On a regular PC, the detection runs in real time at
25 frames/second.
The simplicity of the rule used to detect the hand, compared to
the performance of the resulting detector, gives a hint about
the potential of this technology, and confirms the robustness
of the data representation delivered by the vision sensor.
The algorithm has the potential to be applied in manufacturing
plants, where the position of the hand can be used for security
applications. For example, on a rolling-mill, no security
devices exist that prevent a hand to be processed by the mill.
Using this technology, it is possible to stop the machine if the
hand comes too close to the drum. In general, a vision sensor
could be mounted on any machine, and (with some specific
adaptation) allow the machine to operate only if there are no
hands present at the sensitive locations.
Figure 1: Contrast images with the presence of hands and result of
the detection as green squares
[1] R. E. Schapire, “The boosting approach to machine learning: An
overview”, in Nonlinear Estimation and Classification, Springer,
2003
[2] S. Gyger, et al., “Low-Power Vision Sensors”, CSEM Scientific
Report 2004, page 17
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