Hybrid architecture for gesture recognition: Integrating fuzzy-connectionist and heuristic classifiers using fuzzy syntactical strategy

This paper describes a hybrid architecture that provides automatic classification for a set of gestures. Such architecture combines fuzzy-connectionist, heuristic and syntactical pattern recognition approaches, and deals with gesture recognition based on primitives. The modeling with primitives allows the use of multiples classifiers in order to achieve high classification accuracy. The heuristic classifier and the fuzzy syntactical integrating strategy are described in this paper. The fuzzy-connectionist classifiers were discussed in previous works and they are now revisited just to present the set of parameters that solves the current proof of concept, in the scope of Brazilian Sign Language Manual Alphabet. The fuzzy syntactical strategy coupled with the modeling with primitives has improved the pattern recognition results, enabling the design of architecture for classification with high flexibility and scalability to development of applications in different signed communication contexts. The experimental results show that the proposed approach is valid and has promising application.