On the error-reject trade-off in biometric verification systems

In this work, we address the problem of performance evaluation in biometric verification systems. By formulating the optimum Bayesian decision criterion for a verification system and by assuming the data distributions to be multinormals, we derive two statistical expressions for calculating theoretically the false acceptance and false rejection rates. Generally, the adoption of a Bayesian parametric model does not allow for obtaining explicit expressions for the calculation of the system errors. As far as biometric verification systems are concerned, some hypotheses can be reasonably adopted, thus allowing simple and affordable expressions to be derived. By using two verification system prototypes, based on hand shape and human face, respectively, we show our results are well founded.