Visual hand gestures classification using wavelet transforms

Kumar, D, Kumar, S, Sharma, A and McLachlan, N 2003, 'Visual hand gestures classification using wavelet transforms', International Journal of Wavelets, Multiresolution and Information Processing, vol. 1, no. 4, pp. 373-392.


Document type: Journal Article
Collection: Journal Articles

Title Visual hand gestures classification using wavelet transforms
Author(s) Kumar, D
Kumar, S
Sharma, A
McLachlan, N
Year 2003
Journal name International Journal of Wavelets, Multiresolution and Information Processing
Volume number 1
Issue number 4
Start page 373
End page 392
Total pages 20
Publisher World Scientific Publishing
Abstract This paper presents a novel technique for classifying human hand gestures based on stationary wavelet transform (SWT) and compares the results with classification based on Hu moments. The technique uses view-based approach for representation of hand actions, and artificial neural networks (ANN) for classification. This approach uses a cumulative image-difference technique where the time between the sequences of images is implicitly captured in the representation of action. This results in the construction of Motion History Images (MHI). These MHI's are decomposed into four sub-images using SWT. The average image (fll) is fed as the global image descriptors to the ANN for classification. The recognition criterion is established using backpropagation based multilayer perceptron (MLP). The preliminary experiments show that such a system can classify human hand gestures with a classification accuracy of 97%. The work is motivated by the previous research in appearance-based motion recognition of human hand actions. The overall goal of our research is to determine the reliability of using this wavelet based computationally inexpensive gesture classification technique that may be used for helping disabled or aged people interact with computers.
Subject Information and Computing Sciences not elsewhere classified
DOI - identifier 10.1142/S0219691303000232
ISSN 0219-6913
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