Abstract
In this paper we use a novel neural approach for face recognition with Hidden Markov Models. A method based on the extraction of 2D-DCT feature vectors is described, and the recognition results are compared with a new face recognition approach with Artificial Neural Networks (ANN). ANNs are used to compress a bitmap image in order to represent it with a number of coefficients that is smaller than the total number of pixels. To train HMM has been used the Hidden Markov Model Toolkit v3.3 (HTK), designed by Steve Young from the Cambridge University Engineering Department. However, HTK is able to speakers recognition, for this reason we have realized a special adjustment to use HTK for face identification.
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Bevilacqua, V., Mastronardi, G., Pedone, A., Romanazzi, G., Daleno, D. (2006). Hidden Markov Models for Recognition Using Artificial Neural Networks. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_13
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DOI: https://doi.org/10.1007/11816157_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
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