A novel biometric approach to generate ROC curve from the Probabilistic Neural Network

The aim of this paper is to present a new method to produce a Receiver Operating Characteristic (ROC) curve from a Probabilistic Neural Network (PNN). Traditionally, an ROC curve has been used widely to report the recognition system measurements. Two main problems arise when using the PNN. Firstly, the PNN outputs are always logical (zeros and one); secondly, a PNN is considered as a multi-class classifier, because it usually has more than one output class. To solve these problems, we suggest a new approach to acquire the score values from the PNN, establish the relationship between the ROC parameters for each class and fusing them to generate one main ROC curve. Personal authentication based on the Finger Texture (FT) biometric has been used to collect the ROC parameters, where three feature extraction methods have been implemented and evaluated: Coefficient of Variance (CV) statistics, Gabor filter followed by the CV calculations and Local Binary Pattern (LBP) followed by the CVs. The results show the accuracy of the Equal Error Rates (EERs) recorded for each ROC graph compared with the actual practical values.