Assessing the level of difficulty of fingerprint datasets based on relative quality measures

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. The difficulty exhibited in this paper represents how difficult it is to achieve better recognition accuracy within the specific dataset. Proposed in this paper is a general framework for assessing the level of difficulty of fingerprint datasets based on quantitative measurements of not only the sample quality of individual fingerprints but also the relative differences between genuine pairs, such as common area and deformation. The experimental results over various datasets demonstrate that the proposed method can predict the level of difficulty of fingerprint datasets which coincide with the equal error rates produced by four comparison algorithms. The proposed method is independent of comparison algorithms and can be performed automatically.