VISUAL HAND GESTURES CLASSIFICATION USING WAVELET TRANSFORM AND MOMENT BASED FEATURES
SANJAY KUMAR, Dinesh Kumar · International Journal of Wavelets Multiresolution and Information Processing · 2005
This paper presents a novel technique for classifying human hand gestures based on stationary wavelet transform (SWT) and classification based on geometrical based moments and compares the results with the classification based on Hu-moments and wavelet approximate images. The technique uses view-based approach for representation of hand actions, and uses a cumulative image-difference technique where the time between the sequences of images is implicitly captured in the representation of action resulting in Motion History Images (MHI). These MHIs are decomposed into wavelet sub-images using SWT. Translation and scaling invariant moments are then computed from the resulting high scale low pass residue of the wavelet sub-images. These geometrical moments are used as the global image descriptors and are fed to Artificial Neural Network (ANN) for classification. The recognition criterion is established using backpropagation based multilayer perceptron (MLP). The experiments show that such a system can classify human hand gestures with a classification accuracy of 97%.