Abstract
The uni-modal biometric systems making use of a single biometric modality have a limited performance that restricts their applicability in real life scenarios. The multimodal biometric systems make use of two or more modalities that together achieve much higher performances. In this chapter we discuss the means to fuse three modalities to make a more robust system. We first discuss the fusion of speech, lip, and face. This system uses Hidden Markov Models for the classification and an integration technique called as late integration for decision making from the three modalities. We then discuss the fusion of face, speech and fingerprint. Here each of the individual biometric modalities would make use of modular neural network which would then be combined using a fuzzy integration technique. The last model we discuss would carry the fusion of fingerprint, face and hand geometry. This system uses a variety of fusion techniques including a sum rule, linear discriminant function and decision trees.
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Shukla, A., Tiwari, R., Kala, R. (2010). Multimodal Biometric Systems. In: Towards Hybrid and Adaptive Computing. Studies in Computational Intelligence, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14344-1_18
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DOI: https://doi.org/10.1007/978-3-642-14344-1_18
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