Abstract
In this paper, we present Concurrent Self-Organizing Maps (CSOM), a new artificial neural classification model representing a winner-takes-all collection of small SOM units. We consider two significant areas of CSOM applications in Biometric Technology: face recognition and speaker recognition. For the ORL face database of 40 subjects, CSOM yields a recognition score of 91%, while a single, large SOM yields a score of only 71%! For a speaker database provided by 25 talkers, a recognition score of 92.17% was obtained using CSOM, compared to the recognition rate of 79.63% yielded by the SOM. This model may be applied in access control applications for harbour protection.
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Neagoe, VE., Ropot, AD. (2009). Concurrent Self-Organizing Maps — A Powerful Artificial Neural Tool for Biometric Technology. In: Shahbazian, E., Rogova, G., DeWeert, M.J. (eds) Harbour Protection Through Data Fusion Technologies. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8883-4_34
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DOI: https://doi.org/10.1007/978-1-4020-8883-4_34
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-8882-7
Online ISBN: 978-1-4020-8883-4
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