Deep Neural Network-Based Fingerprint Reformation for Minimizing Displacement

In the realm of biometric authentication and forensic analysis, the accurate matching of fingerprints is paramount. Fingerprint displacement, caused by factors such as skin elasticity and pressure during touch, has been a significant challenge in achieving precise fingerprint recognition both during the enrollment and the authentication process. This research presents a technique to address this issue by leveraging Support Vector Machines (SVM) for fingerprint transformation and displacement minimization. The proposed methodology involves the extraction of distinctive fingerprint features and the application of SVM-based algorithms to realign and correct fingerprint distortions resulting from displacement. We analyze the effectiveness of SVM in reducing displacement-induced errors and improving matching accuracy. Experimental results demonstrate the potential of SVM-based fingerprint transformation techniques to significantly enhance the robustness and reliability of fingerprint recognition systems. This research contributes to the ongoing efforts in biometrics and forensic science by providing a practical solution to the problem of fingerprint displacement, with potential applications in identity verification, criminal investigations, and security systems. The findings of this study offer a promising avenue for further research and development in the field of biometric authentication, ultimately leading to more accurate and secure identification methods.