Fingerprint Phalanx-Based Score Fusion

Traditional fingerprint-based authentication systems work on images obtained (e.g., via optical, thermal sensors) from the first phalanges of human fingers. Typical minutia-based systems extract the relevant feature vectors and these feature vectors are utilized by matchers. In this study, we report minutiae matching performance figures, originating from adaptively fusing second and third phalanx images of a mid-size fingerprint database that we collected in our laboratory. In a prior publication, we analyzed the individual phalanges in terms of matching accuracy, image quality, and feature statistics. As a continuation of that prior work, the algorithm developed here (using image quality in score weighting) leads to experimentally verified performance improvement. Utilizing confidence intervals for measuring statistical significance, the performance figures indicate that for cases where first phalanx images are not usable (e.g. due to missing digits, low image quality due to manual labor), proposed fusion algorithm provides an alternative authentication mechanism. Further, when all three phalanges are used, the authentication performance increases with respect to the first-phalanx-only scenario.