Deep Contactless Fingerprint Unwarping

Abstract

Contactless fingerprints have emerged as a convenient, inexpensive, and hygienic way of capturing fingerprint samples. However, cross-matching contactless fingerprints to the legacy contact-based fingerprints is a challenging task due to the elastic and perspective distortion between the two modalities. Current cross-matching methods merely rectify the elastic distortion of the contact-based samples to reduce the geometric mismatch and ignore the perspective distortion of contactless fingerprints. Adopting classical deformation correction techniques to compensate for the perspective distortion requires a large number of minutiae-annotated contactless fingerprints. However, annotating minutiae of contactless samples is a labor-intensive and inaccurate task especially for regions which are severely distorted by the perspective projection. In this study, we propose a deep model to rectify the perspective distortion of contactless fingerprints by combining a rectification and a ridge enhancement network. The ridge enhancement network provides indirect supervision for training the rectification network and removes the need for the ground truth values of the estimated warp parameters. Comprehensive experiments using two public datasets of contactless fingerprints show that the proposed unwarping approach, on average, results in a 17% increase in the number of detectable minutiae from contactless fingerprints. Consequently, the proposed model achieves the equal error rate of 7.71% and Rank-1 accuracy of 61.01% on the challenging dataset of ‘2D/3D’ fingerprints.