Improving Sensor Interoperability between Contactless and Contact-Based Fingerprints Using Pose Correction and Unwarping

Current fingerprint identification systems face significant challenges in achieving interoperability between contact-based and contactless fingerprint sensors. In contrast to existing literature, we propose a novel approach that can combine pose correction with further enhancement operations. It uses deep learning models to steer the correction of the viewing angle, therefore enhancing the matching features of contactless fingerprints. The proposed approach was tested on real data of 78 participants (37,162 contactless fingerprints) acquired by national police officers using both contact-based and contactless sensors. The study found that the effectiveness of pose correction and unwarping varied significantly based on the individual characteristics of each fingerprint. However, when the various extension methods were combined on a finger-wise basis, an average decrease of 36.9% in equal error rates (EERs) was observed. Additionally, the combined impact of pose correction and bidirectional unwarping led to an average increase of 3.72% in NFIQ 2 scores across all fingers, coupled with a 6.4% decrease in EERs relative to the baseline. The addition of deep learning techniques presents a promising approach for achieving high-quality fingerprint acquisition using contactless sensors, enhancing recognition accuracy in various domains.