Secure And Robust User Authentication Using Partial Fingerprint Matching

This project proposes a robust fingerprint-based authentication algorithm that ensures secure verification even with limited-sized partial fingerprints. It has become increasingly common that a number of recent consumer devices, such as smartphones, employ fingerprint sensors for user authentication. The sensors to be embedded are generally preferred to take up less space for better usability and product design, the sensing areas are therefore limited in size. To supplement the insufficient area, devices often store multiple acquisitions from a single finger in enrollment, later to verify at least any one of them successfully match an acquisition in authentication. Considering the low information entropy of a partial image, the security aspect of small area-based systems is a major concern. On the other hand, unpredictable variability due to finger rotation, grip positions, and skin deformation has a negative impact on biometric performance. This project presents a fingerprint matcher or verifier incorporating several efficient algorithms against these concerns on both the performance and security aspects. A method of "segmented area matching" brought an enhanced robustness to the variability, especially to finger rotation. And a method of "feature-weighted block scoring" provided with more detailed image discrimination, resulting in improved security. Experimental evaluations with extensive database of partial fingerprint images from more than 100 people, acquired by a small-sized capacitive sensor, demonstrated a significant improvement over the previously suggested algorithms in both
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