Vision Based Hand Gesture Interfaces for Wearable Computing and ...

Chapter 5. Hand Detection
The data in Figure 5.8 stems from a very similar experiment, only different in
that the detectors were evaluated on a test set whose examples exhibited rotations
by discrete amounts, not random on a continuous scale. The graph shows that
smaller deviations in rotation from the training data achieve better detection
rates: detectors trained for angles “in the middle” of the rotation spectrum, 6 and
9 degrees in particular, fare better than those trained on angles 0 and 15.
5.5.3 Rotation bounds for undiminished performance
The objective of this set of experiments was to determine the angles that the
training examples could be rotated and still achieve good detection performance
on the equally-rotated test set. Four repetitions of the original training set for the
closed posture were rotated by 0, 15, 30, and 45 degrees, respectively, and joined
into one large training set. The Viola-Jones detection method over time keeps the
positive examples that are reliably detectable, while it successively ignores those
that would require an unacceptably high false positive rate. The experiment’s as-
sumption is that well-detectable examples will be retained and all others sacrificed
in order to achieve a low false positive rate. The evaluation in Figure 5.9 shows
this effect. It suggests that the examples with 0 degrees and 15 degrees of rotation
are more consistently recognizable than those with 30 degrees and 45 degrees, the
136