Directional filter bank-based fingerprint image quality

Fingerprint image quality ensures that only the high-quality fingerprints containing a good amount of features are used for verification. Fingerprint matching accuracy depends heavily on the quality of the fingerprints. In this paper, we have proposed a new fingerprint image quality method based on the Directional Filter Banks (DFB). The fingerprint image is decomposed into subbands using the DFB. The similarity between the different subbands is used to calculate the fingerprint image quality. We have compared the performance of the proposed method with the ten existing fingerprint quality estimation methods with special emphasis on widely used fingerprint quality metrics NFIQ and NFIQ 2.0. The experimental results on the top three high-quality fingerprints in the FVC 2004 DB2A dataset show an equal error rate (EER) of 5.59% for the proposed method as compared with 5.74% and 7.95% for NFIQ 2.0 and NFIQ, respectively. Ranked EER, ROC, and error-reject curves show that DFB-based method is a good predictor of matching performance, and it outperforms the existing fingerprint quality methods. We have also analyzed the effect of the partial fingerprints on the fingerprint recognition system using the FVC 2004 DB1A dataset. The presence of partial fingerprints has an adverse effect on the recognition system. The equal error rate increases from 2.34 to 13.93% in partial fingerprint recognition. The proposed DFB-based method rightly assigns low-quality values to partial fingerprints.