User adaptive hand gesture recognition using multivariate fuzzy decision tree and fuzzy garbage model

The natural signal of human such as voice or gesture has been applied to the system for assisting disabled and the elderly people. As an example of such kind of system, the soft remote control system has been developed by HWRS-ERC in KAIST. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and wrong recognition of similar gestures. In this paper, we propose multivariate fuzzy decision tree (MFDT) learning and classification algorithm for hand gesture recognition. The similar meaningless gestures are rejected using fuzzy garbage model. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user´s hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. The experimental results show the classification and user adaptation performance of proposed algorithm is better than traditional fuzzy decision tree. Also the meaningless gestures are well rejected.