Assessing the Difficulty Level of Fingerprint Datasets Based on Relative Quality Measures

Abstract

Understanding the difficulty of a dataset is of primary importance when it comes to testing and evaluating fingerprint recognition systems or algorithms because the evaluation result is dependent on the dataset. Proposed in this paper is a general framework of assessing the level of difficulty of fingerprint datasets based on quantitative measurements of not only the sample quality of individual fingerprints but also relative differences between genuine pairs, such as common area and deformation. The experimental results over multi-year FVC datasets demonstrate that the proposed method can predict the relative difficulty levels of the fingerprint datasets which coincide with the equal error rates produced by two matching algorithms. The proposed framework is independent of matching algorithms and can be performed automatically.