Robust personal authentication is becoming ever more important in computer-based applications. Among a variety of methods, biometric offers several advantages, mainly in embedded system applications. Hard and soft multi-biometric, combined with hard and soft computing methods, can be applied to improve the personal authentication process and to generalize the applicability. This chapter describes the embedded implementation of a multi-biometric (voiceprint and fingerprint) multimodal identification system based on hard computing methods (DSP) for feature extraction and matching, an artificial neural network (ANN) for soft feature pattern matching, and a fuzzy logic engine (FLE) for data fusion and decision.
CITATION STYLE
Malcangi, M. (2014). Developing a multimodal biometric authentication system using soft computing methods. In Artificial Neural Networks: Second Edition (pp. 205–226). Springer New York. https://doi.org/10.1007/978-1-4939-2239-0_13
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