Ensemble to improve gesture recognition

Automatic hand gesture recognition plays a fundamental role in current research with the aim of empowering a natural communication between users and virtual reality systems. Starting from an existing work, based on the extraction of two different descriptors from the depth maps followed by their classification with a stand-alone multi SVM classifier, in this paper we improve the gesture recognition system performances and reliability and we evaluate different classification approaches. To this purpose, we first compare the performance of different descriptors and analyze their correlation for assessing their complementarity, and then we demonstrate the advantage gained by their fusion by the Wilcoxon Signed-Rank test. In particular, the novelties of this paper are a new method for extracting features from the curvature image and the design of a very effective ensemble of classifiers to solve the problem.