Abstract
In this book chapter, a novel biometric system from the normalisation level up to the verification level is developed, tested and verified against other multimodal and unimodal systems. The main advantage of a new architecture is in flexibility of combining various features from multimodal biometrics in a new way, suitable for neural-network learner. The system utilises associative memories and pattern matchers as learners of biometric data, but the main advantage of a new architecture is increased resistance to noise and ability of system to compensate for an absence of some biometric traits. Detailed experimental analysis of pros and cons of such system is also provided.
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Acknowledgements
Authors acknowledge invaluable help and advice for Prof Khalid Saeed while working on this chapter. This research was partially supported by NSERC.
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© 2012 Springer Science+Business Media New York
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Ahmadian, K., Gavrilova, M. (2012). Chaotic Neural Network and Multidimensional Data Analysis in Biometric Applications. In: Saeed, K., Nagashima, T. (eds) Biometrics and Kansei Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5608-7_8
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DOI: https://doi.org/10.1007/978-1-4614-5608-7_8
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