A Bayesian approach to fingerprint minutia localization and quality assessment using adaptable templates

Fingerprints continue to serve as a reliable trait for human identification. Feature-based matching techniques, such as those used by Automated Fingerprint Identification Systems (AFIS), have demonstrated remarkable success in minutiae-based matching from good quality prints with relatively large extent. As the image quality degrades and acquired fingerprint area decreases, however, the number of reliable minutiae that can be automatically detected decreases, causing match performance to suffer. This paper presents a novel approach to improving the precision of features that can be extracted from fingerprint images. This is accomplished through improved minutia localization and quality assessment routines that are inspired in part by human visual perception. Initial results have shown an improvement in minutia accuracy for 88.2% of fingerprint minutia sets after applying the proposed localization method. An increase in average quality of true minutiae was found for 98.6% of the fingerprint images when using the proposed quality assessment. The results were obtained using a database of 516 fingerprints with ground truth minutiae.