Abstract
Authentication is becoming ever more important in computer-based applications because the amount of sensitive data stored in such systems is growing. However, in embedded computer-system applications, authentication is difficult to implement because resources are scarce. Using fuzzy logic and artificial neural networks to process biometric data can yield improvements in authentication performance by limiting memory and processing-power requirements. A multibiometric platform that combines voiceprint and fingerprint authentication has been developed. It uses traditional pattern-matching algorithms to match hard-biometric features. An artificial neural network was trained to match soft-biometric features. A fuzzy logic inference engine performs smart decision fusion and authentication. Finally, a digital signal processor is used to embed the entire identification system. The embedded implementation demonstrates that improvement in performance is attainable, despite limited system resources.
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Malcangi, M. (2009). Fuzzy Logic and Artificial Neural Networks for Advanced Authentication Using Soft Biometric Data. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_7
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DOI: https://doi.org/10.1007/978-3-642-03969-0_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03968-3
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