Abstract
lips movement is an important parameter in speech recognition. The Lip image segmentation has a significant role in lips movement analysis. Major problems that any vowel recognition (especially Persian) method is faced are low chromatic in lip region, low contrast luminance, overlap between the lip and facial skin color, and similarity between lips movement in some vowels after detecting lips. In this paper, a new automatic and quick approach for the lip extraction based on using the Haar wavelet is proposed. The output of this proposed approach is used as a feature vector for a hybrid neural network. The proposed algorithm for lip image segmentation uses the color space CIE L*U*V* and CIE L*a*b* in order to improve the contrast between the lip and the other face regions. After this step, the lips are modeled and a feature vector with longitudinal and angular parameters is extracted from the proposed lips model.
This feature vector has been used as an input for a feedforward backpropagation hybrid neural network. The proposed method has been applied to 2200 tested images and the accuracy is about 79% that shows about 15% enhancement in compare with similar methods.
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Hosseini, M.M., Gharahbagh, A.A. (2013). Persian Vowel Recognition Using the Combination of Haar Wavelet and Neural Network. In: Jordanov, I., Jain, L.C. (eds) Innovations in Intelligent Machines -3. Studies in Computational Intelligence, vol 442. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32177-1_4
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DOI: https://doi.org/10.1007/978-3-642-32177-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32176-4
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