This paper describes a method for the recognition of dynamic gestures using a combination Neural Network/ discrete Hideen Markov Model. This work deals with four topics. First a reliable and robust person localization task is presented. Then we focus on the view-based recognition of the user's static gestural instructions from a predefined vocabulary based on both a skin color model and statistical normalized moment invariants. The segmentation of the postures occurs by means of the skin color model based on the Mahalanobis metric. From the resulting binary image containing only regions which have been classified as skin candidates we extract translation and scale invariant moments. Further a Kohonen Self Organizing Map (SOM) is used to cluster the feature space. After the self-organizing process we modify the SOM weight vectors using the Learning Vector Quantization (LVQ) method causing the weights to approach the decision boundaries and we quantize each of them into a symbol. Finally, the symbol sequence extracted from time-sequential images is used as input for a system of discrete Hidden Markov Models (DHMMs).
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