Finger image quality based on singular point localization

Singular points are important global features of fingerprints and singular point localization is a crucial step in biometric recognition. Moreover the presence and position of the core point in a captured fingerprint sample can reflect whether the finger is placed properly on the sensor. Therefore, the displacement given by detected core points is investigated. We propose pattern-based filters to eliminate the false detection given by state of the art approaches. The experimental results show improvement using different databases. Based on the improved singular point localization algorithm, we explore and analyze the importance of singular points on biometric accuracy. The experiment is based on large scale databases and conducted by relating the measured quality of a fingerprint sample, given by the positions of core points, to the biometric performance. The experimental results show the positions of core points do have influence on the comparison algorithms, but are not as relevant as other benchmarked quality metrics.