Abstract
This paper introduces a method based on Evolving Connectionist Systems (ECOS) for person verification tasks. The method allows for the development of models of persons and their on-going adjustment based on new speech and face images. Some experimental person verification models based on speech and face image features are developed based on this method where speech and face image information are integrated at a feature level to model each person. It is shown that the integration of speech and image features improves significantly the accuracy of the person verification model when compared with the use of only image or speech data.
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Ghobakhlou, A., Zhang, D., Kasabov, N. (2004). An Evolving Neural Network Model for Person Verification Combining Speech and Image. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_58
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DOI: https://doi.org/10.1007/978-3-540-30499-9_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
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