Fuzzy analysis of classifier handshapes from 3D sign language data

Abstract

In this paper, we present a novel technique to track and recognize both classic handshapes and descriptive classifier handshapes inside 3D sign language sequences. Our approach is able to evaluate the intensity of CL-C, CL-L and the CL-G classifiers which are used to specify sizes or amounts of objects. Our method combines Minkowski similarity measures to match the shape of the hand and a fuzzy inference system (FIS) to quantify the classifier's intensity. We implemented and tested our framework on a set of 3D sign language data. The membership functions as well as the rules of the designed FIS were optimized by 12 participants. The system generates evaluations which are very close to human perception of the iconic information conveyed by the classifier handshapes. The correlation of results generated by our system with those awarded by 12 participants is about 0.936 which can be considered as satisfactory. © 2013 Springer-Verlag.