Performance analysis of different classification methods for hand gesture recognition using range cameras

Abstract

Most of the methods described in the literature for automatic hand gesture recognition make use of classification techniques with a variety of features and classifiers. This research focuses on the frequently-used ones by performing a comparative analysis using datasets collected with a range camera. Eight different gestures were considered in this research. The features include Hu-moments, orientation histograms and hand shape associated with its distance transformation image. As classifiers, the k-nearest neighbor algorithm and the chamfer distance have been chosen. For an extensive comparison, four different databases have been collected with variation in translation, orientation and scale. The evaluation has been performed by measuring the separability of classes, and by analyzing the overall recognition rates as well as the processing times. The best result is obtained from the combination of the chamfer distance classifier and hand shape and distance transformation image, but the time analysis reveals that the corresponding processing time is not adequate for a real-time recognition.