Gesture Modeling and Recognition Using Finite State Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
A fuzzy rule-based approach to spatio-temporal hand gesturerecognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
User adaptive handwriting recognition by self-growing probabilistic decision-based neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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As an emerging human-computer interaction HCI technology, recognition of human hand gesture is considered a very powerful means for human intention reading. To construct a system with a reliable and robust hand gesture recognition algorithm, it is necessary to resolve several major difficulties of hand gesture recognition, such as inter-person variation, intra-person variation, and false positive error caused by meaningless hand gestures. This paper proposes a learning algorithm and also a classification technique, based on multivariate fuzzy decision tree MFDT. Efficient control of a fuzzified decision boundary in the MFDT leads to reduction of intra-person variation, while proper selection of a user dependent UD recognition model contributes to minimization of inter-person variation. The proposed method is tested first by using two benchmark data sets in UCI Machine Learning Repository and then by a hand gesture data set obtained from 10 people for 15 days. The experimental results show a discernibly enhanced classification performance as well as user adaptation capability of the proposed algorithm.