Hand gesture recognition using morphological principal component analysis and an improved CombNET-II
説明
A new neural network structure dedicated to time series recognition, T-CombNET, is presented. The model is developed from a large scale neural network CombNet-II, designed to deal with a very large vocabulary for character recognition. Our specific modifications of the original CombNet-II model allows it to do temporal analysis, and to be used in a large set of human movement recognition systems. This paper also presents a feature extraction method based on morphological principal component analysis that completely describes a hand gesture in 2-dimensional time varying vector. The proposed feature extraction method along with the T-CombNET structure were then used to develop a complete Japanese Kana hand alphabet recognition system consisting of 42 static postures and 34 hand motions. We obtained a superior recognition rate of 99.4% in the gesture recognition experiments when compared to different neural network structures like multi-layer perceptron, learning vector quantization (LVQ), Elman and Jordan partially recurrent neural networks, CombNET-II and the proposed T-CombNET structure.
収録刊行物
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- IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028)
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IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028) 4 57-62, 2003-01-20
IEEE