Abstract
Abstract: This paper introduces an enhanced approach for distorted fingerprint matching, integrating OpenCV, SIFT analysis, and FLANN-based algorithms. By leveraging the robustness of SIFT in feature extraction and the efficiency of FLANN in nearest neighbor matching, our method aims to enhance accuracy and robustness in fingerprint recognition. Experimental evaluations on a dataset of distorted fingerprints demonstrate superior performance compared to some of the existing techniques, showcasing resilience against various distortions such as noise, rotation, and occlusion. Our method offers a reliable solution for real-world applications in security, forensics, and biometric authentication systems. In comparison to recent techniques, our approach significantly improves matching accuracy and computational efficiency, addressing the challenges of distorted fingerprint recognition effectively in modern contexts.
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