Abstract / truncated to 115 words (read the full abstract)

Biometric authentication can be cast as a signal processing and statistical pattern recognition problem. As such, it relies on models of signal representations that can be used to discriminate between classes. One of the assumptions typically made by the practioner is that the training set used to learn the parameters of the class-conditional likelihood functions is a representative sample of the unseen test set on which the system will be used. If the test set data is distorted, the assumption no longer holds and the Bayes decision rule or Maximum Likelihood rules are no longer optimal. In biometrics, the distortions of the data come from two main sources: intra-user variability, and changes in acquisition conditions. ... toggle 8 keywords

bayesian networks pattern recognition multiple classifier systems reliability quality measures speech signature biometrics


Richiardi, Jonas
Signal Processing Institute, Swiss Federal Institute of Technology
Publication Year
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Jan. 28, 2009

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