Optimizing contactless to contact-based fingerprint comparison using simple parametric warping models

Abstract

2D contactless to contact-based fingerprint (FP) comparison is a challenging task due to different types of distortion introduced during the capturing process. While contact-based FPs typically exhibit a wide range of elastic distortions, perspective distortions pose a problem in contactless FP imagery. In this work, we investigate three simple parametric warping models for contactless fingerprints — circular, elliptical and bidirectional warping — and show that these models can be used to improve the interoperability between the two modalities by simulating unfolding of a generic 3D model. Additionally, we employ score fusion as a technique to enhance the comparison performance in scenarios where multiple contactless FPs of the same finger are available. Using the simple circular warping, we have been able to decrease the Equal Error Rate (EER) from 1.79% to 0.78% and 1.82% to 1.31% on our dataset, respectively.