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Unimodal biometric systems have to contend with a variety of problems such as noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. Multimodal biometric systems elegantly address several of these problems present in unimodal systems. By combining multiple sources of information, such as palm print and hand geometry, face and fingerprints, face and ear biometric, these systems improve matching performance, increase population coverage, deter spoofing, and facilitate indexing. Various fusion levels and scenarios are possible in multimodal systems.
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