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

Al-Nima, R. R. O., Dlay, Satnam, Woo, Wai Lok and Chambers, Jonathon (2016) A novel biometric approach to generate ROC curve from the Probabilistic Neural Network. In: SIU 2016 - 24th Signal Processing and Communication Application Conference, 16th - 19th May 2016, Zonguldak, Turkey.

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Biometric, finger texture, probabilistic neural network, ROC curve
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	03 Apr 2019 17:11
Last Modified:	10 Oct 2019 20:34
URI:	http://nrl.northumbria.ac.uk/id/eprint/38737

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