Bridging Dimensions in Fingerprints to Advance Distinctiveness: Recovering 3D Minutiae From a Single Contactless 2D Fingerprint Image

Contactless 3D fingerprint identification systems have emerged to provide more accurate and hygienic alternatives to contact-based conventional systems that acquire hundreds of millions of fingerprints everyday. However, the intricate process of acquiring 3D fingerprints presents a significant challenge, acting as a key barrier to fully unlocking the potential of 3D fingerprint biometrics. This paper introduces a novel framework to directly recover corresponding 3D minutiae template from a single contactless 2D fingerprint image. Billions of contact-based fingerprints have been acquired and employed everyday for e-governance and other applications. Seamless adoption of contactless 3D fingerprint technologies also requires advanced capabilities to accurately match 3D fingerprints with respective 2D fingerprint templates, which is currently missing in existing literature. We therefore introduce novel capabilities to accurately align minutiae templates in 3D spaces and enable compensation for the unknown perspective transformation. This capability significantly enhances the ability to accurately match 3D to 3D and 3D to 2D fingerprint templates. Furthermore, we introduce a new approach to synthesizing realistic contactless fingerprint images, resulting in the generation of a large synthetic database complete with corresponding 3D ground truths of minutiae points. Finally, we provide a detailed theoretical analysis of formulation for the uniqueness of recovered 3D minutiae templates, providing a theoretical justification for the superiority of such 3D minutiae templates over their 2D counterparts.