Bachelor Thesis - Computer Graphics Group

The data delivered to the neural network is encoded as a sequence of cosines
and sinuses of the angles between the subsequent segments [7]. Hence, the
number of inputs equals twice the amount of lines in the normalized shape.
Each output corresponds to a single recognizable gesture pattern. Thus, the
number of outputs is the same as the size of the pattern list. Therefore, every
time a new gesture is added to or removed from the list, the neural network
has to retrain from scratch.
To recognize a gesture, we transform the normalized sequence of points into the
proposed input format and propagate it through the neural network. We find
the maximum value, which signals the corresponding gesture pattern, when it
is above a defined threshold value. On overview of the neural network can be
seen in figure 2.4.
cos α
sin α
cos β
sin β
cos γ
sin γ
Figure 2.4: Neural network overview
The training is performed by repeating the standard back-propagation algorithm.
Preprocessed and transformed pattern samples are used as the training
input. Expected output samples are constructed by taking a vector filled with
zeros except for a one on the index, which is assigned to the given gesture
pattern. The training process is finished when either the error rate reaches the
target value, or the number of cycles exceeds the maximum count.
2.3 K-nearest neighbors
The k-nearest neighbors [15] is the second available gesture classifier. Big
advantage over the neural network is the lack of the learning phase. The
idea of the algorithm is very simple. To recognize a gesture, we compare
it with all pattern samples from the list. By applying a specified distance
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