To defend state borders and backup egovernance programs, enormous databases of contactbased fingerprints have been generated. Contactless fingerprints sensors are becoming more popular because they provide a greater cleanliness, security and accuracy. The existing method have capacity to match contactless 2D fingerprints with legacy contact-based fingerprint databases is critical to the adoption and success of such contactless fingerprint technologies. This research looks at the issue and proposes a novel method for reliably matching fingerprint scans. The project consists of a robust thin-plate spline model that was a incorporated for the correction of deformations to address contact based and contactless sensor interoperability problems. The robust thin-plate spline (RTPS) is a new type of spline that can be more correctly describe elastic fingerprint deformations. The RTPS-based generalized fingerprint deformation correction model (DCM) is presented to correct such deformations on contact-based fingerprints. When DCM is used, essential minutiae element on both contactless and contact-based fingerprints are aligned accurately. Incorporating minutiae-related ridges into such cross-matching performance will be researched further. In addition, we create a new database of 1800 contactless 2D fingerprints and the associated contactbased fingerprints obtained from 300 clients, which is made public ally available for further research. Using two public ally available databases, the experimental results provided in this work confirm our approach and produce outperforming results for matching contactless 2D and contact-based fingerprint photos. Automated detection and correction of perspective distortion in contactless fingerprint images is expected to reduce the error rates. This work incorporates more robust core detection algorithms and powerful match strategy.