FVC2000: Fingerprint Verification Competition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation of Fingerprint Verification Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of Biometric Quality Measures
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Biometric Menagerie Index for Characterising Template/Model-Specific Variation
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
High resolution partial fingerprint alignment using pore-valley descriptors
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint quality indices for predicting authentication performance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Information Sciences: an International Journal
A Comparative Study of Fingerprint Image-Quality Estimation Methods
IEEE Transactions on Information Forensics and Security
Biometric zoos: Theory and experimental evidence
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
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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.