Improving cross sensor interoperability for fingerprint identification

Improving accuracy of matching fingerprint images acquired from two different fingerprint sensors is an important research problem with several promising studies in the literature. Most of these studies focus on sensor interoperability using fingerprints acquired from different kinds of contact-based sensors. However emerging contactless fingerprint technologies have shown its benefits. This paper investigates fingerprint sensor interoperability problem using fingerprints acquired from contact-based and contactless sensor. We propose a generalized contact-based fingerprint deformation correction model (DCM) to improve the matching accuracy. This model is trained by estimating the deformation between contact-based fingerprint and corresponding contactless fingerprint (ground truth). We present a method to estimate contact-based fingerprint impression type and intensity. As a result, minutiae features from contact-based and contactless fingerprint can be better aligned using the proposed model. A database of 1200 2D contactless fingerprints and respective contact-based fingerprints from 200 clients is used for the experiments. The experimental results presented in this paper validate our approach and illustrate promising improvement in performance using the proposed model.