Hand sign recognition system based on hybrid network classifier

This paper discusses a hand posture recognition system with a hybrid network classifier. The hybrid network consists of SOM and Hebbian network. Feature vector is extracted from the input hand posture image and the given feature vector is mapped to a lower-dimensional map by the SOM. Then the supervised Hebbian network performs category acquisition and naming. The feasibility of the system is verified by computer simulations. The results show that the recognition performance of the system is quite good if the number of neurons in the SOM is sufficient. Besides the recognition performance, the advantage of the hybrid classifier is the embedded learning capability. It is also expected that the classifier can be extended to recognize dynamic gesture by employing feedback SOM.