VISUAL HAND GESTURES CLASSIFICATION USING WAVELET TRANSFORMS
SANJAY KUMAR, Dinesh Kumar, Arun Kumar Sharma, Neil M. McLachlan · International Journal of Wavelets Multiresolution and Information Processing · 2003
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.