Decision level fusion falls under a broader area known as distributed detection systems and is the process of selecting one hypothesis from multiple M hypotheses given the decisions of multiple Nsensors in the presence of noise and interference. In biometrics, decision level fusion creates a single decision from typically two hypotheses, imposter or genuine user, from multiple biometric sensor decisions, which may or may not be identical sensors. Often, decision level fusion is implemented to save communication bandwidth as well as improve decision accuracy. A statistical performance model for each biometric sensor is needed a priori to support the system wide optimization in terms of two error rates: false acceptance rate, admitting an imposter, and false rejection rate, rejecting the genuine user. A weighted sum of these two errors is a useful...
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