Dichotomy transformation in biometric authentication problem creates a two class (""within"" or ""between"") classification problem in multivariate distance space. Linear discriminant analysis, which is a linear classifier, results in good performance in IRIS biometric authentication problem. However, it assumes that the distributions of two classes are normal, whereas they are closely related to the log-normal distributions. Here a modified variance linear discriminant analysis algorithm is proposed and its superior experimental results on the IRIS biometric database are reported.

Receiver operating characteristic or ROC curves are of great interest in evaluating many security systems such as biometric authentication. They visualize the trade-off between the number of security breaches and the level of convenience. In the earlier work, ROC curves and their decision boundaries were studied for various classifiers. Here, further studies are conducted to identify problems of ROC curve analysis when artificial neural network (ANN) classifiers' net values are used. Graphical decision boundaries and experimental results on the IRIS biometric authentication system reveal the over-fitting in the ROC curve analysis. This graphical decision boundaries suggest that ANN classifiers with two output units are more desirable than those with a single output unit for two class classification problems.