Combined Feature Level and Score Level Fusion Gabor Filter-Based Multiple Enrollment Fingerprint Recognition

Abstract

Minutiae-based fingerprint matching methods suffer difficulty in automatically extracting all minutiae points due to failure to detect the complete ridge structures of a fingerprint, as well as describing all the local ridge structures as minutiae points. These make matching a difficult process for example, the case where two fingerprints have different numbers of uncaptured minutiae points and hence negatively affecting recognition performance, matching speed and memory consumption. Gabor filter-based matching methods can capture both the local and global details of a fingerprint which qualifies them to be a possible alternative due to their rich features. This paper presents a Combined Feature Level and Score Level Fusion Gabor filter-based approach; the first of the kind to implement a verification multiple enrollment based fingerprint recognition system. We compared the Combined Feature Level and Score Level Fusion Gabor filter-based multiple enrollment fingerprint recognition method with a spectral minutiae-based method using two fingerprint databases; FVC 2000-DB2-A and FVC 2006-DB2-A. Our results indicate that there is a significant percentage increase brought about by the combined feature level and Score Level fusion Gabor filter-based matching approach in comparison to the famous minutiae-based matching approach. The percentage increases in the FVC 2000-DB2-A were 86.45%, 98.01% and 87.82%, while those in the FVC 2006-DB2-A were 79.71%, 97.07% and 85.88% respectively for recognition performance improvement, matching speed improvement and memory consumption reduction. The outstanding results attained from the proposed approach leave no room for deployment in real world multiple enrollment fingerprint recognition applications that require better recognition performance, good matching speed and reduced memory consumption.