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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    Concurrent Self-Organizing Maps - A Powerful Artificial Neural Tool for Biometric Technology

    Neagoe, V.-E. / Ropot, A.-D. | British Library Conference Proceedings | 2009


    Concurrent Self-Organizing Maps for Multispectral Facial Image Recognition

    Neagoe, Victor-Emil / Mugioiu, Alexandru-Cristian / Tudoran, Cristian-Tudor | IEEE | 2007




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    Johnson, M. / Rokhsaz, K. / AIAA | British Library Conference Proceedings | 2000