Visual speech recognition using wavelet transform and moment based features

This paper presents a novel vision based approach to identify utterances consisting of consonants. A view based method is adopted to represent the 3-D image sequence of the mouth movement in a 2-D space using grayscale images named as motion history image (MHI). MHI is produced by applying accumulative image differencing technique on the sequence of images to implicitly capture the temporal information of the mouth movement. The proposed technique combines Discrete Stationary Wavelet Transform (SWT) and image moments to classify the MHI. A 2-D SWT at level 1 is applied to decompose MHI to produce one approximate and three detail sub images. The paper reports on the testing of the classification accuracy of three different moment-based features, namely Zernike moments, geometric moments and Hu moments computed from the approximate representation of MHI. Supervised feed forward multilayer perceptron (MLP) type artificial neural network (ANN) with back propagation learning algorithm is used to classify the moment-based features. The performance and image representation ability of the three moments features are compared in this paper. The preliminary results show that all these moments can achieve high recognition rate in classification of 3 consonants.