  The current state-of-the-art hand gesture recognition methodologies heavily
rely in the use of machine learning. However there are scenarios that machine
learning cannot be applied successfully, for example in situations where data
is scarce. This is the case when one-to-one matching is required between a
query and a dataset of hand gestures where each gesture represents a unique
class. In situations where learning algorithms cannot be trained, classic
computer vision techniques such as feature extraction can be used to identify
similarities between objects. Shape is one of the most important features that
can be extracted from images, however the most accurate shape matching
algorithms tend to be computationally inefficient for real-time applications.
In this work we present a novel shape matching methodology for real-time hand
gesture recognition. Extensive experiments were carried out comparing our
method with other shape matching methods with respect to accuracy and
computational complexity using our own collected hand gesture dataset and a
modified version of the MPEG-7 dataset.%that is widely used for comparing 2D
shape matching algorithms. Our method outperforms the other methods and
provides a good combination of accuracy and computational efficiency for
real-time applications.
