A study on the calibration of fingerprint classifiers

Abstract

Fingerprint classification is a frequent approach to deal with very large scale databases in fingerprint recognition. In the last few years, several proposals based on Convolutional Neural Networks have pushed state of the art results even further. However, it has also been proven that such networks are prone to be overconfident in the predictions of the classes, which may have an impact on their performance. This paper aims to study the problem from a systematic point of view. First, it is determined that the most common network to classify fingerprints does suffer from badly calibrated predictions. Second, two calibration methods (temperature scaling and Dirichlet calibration) are applied to correct for this tendency. Third, a modified search strategy is proposed, which makes use of the calibrated class probabilities predicted by the classifier to further reduce the penetration rate and avoid the negative impact of impostor input fingerprints. Fourth, all the proposals are evaluated on five datasets, which combine synthetic and real fingerprints of different qualities. Dirichlet calibration led to improved predicted class probabilities, which in turn allowed for further reduction of the penetration, while maintaining a good trade-off with respect to the false rejection rate.