Improving biometric recognition by means of score ratio, the likelihood ratio for non-probabilistic classifiers. A benchmarking study

Abstract

One of the ever present goals in biometrics research is to improve system performance. Herein, an alternative method is proposed that is independent of the biometric characteristic and the system, as this proposal, Score Ratio, is applied to the output (comparison score) of the classifier. The Likelihood Ratio is widely used with probabilistic classifiers because it performs well in these circumstances. However, when the classifiers are non-probabilistic, then this ratio is not used. This is our proposal: with non-probabilistic classifier based systems, the decision is taken solely through the score, supposing that the biometric feature, X, belongs to the Claimant (H0 hypothesis), here, it is also proposed to make use of the score considering that X does not belong to the Claimant (H1 hypothesis); more specifically, using the ratio between these two scores: the Score Ratio. For more objective results, benchmarking and reproducibility are used in the experiments, applying our proposal with third-party (benchmarking) experimental protocols, databases, classifiers and performance measures for fingerprint, iris and finger vein recognition. Statistically significant improvements have been obtained when the Score Ratio is used with regard to not using it in all cases tested.