a deep learning-based model for fingerprint verification

Fingerprint verification has emerged as a cornerstone of personal identity authentication. this research introduces a deep learning-based framework for enhancing the accuracy of this critical process. by integrating a pre-trained inception model with a custom-designed architecture, we propose a model that effectively extracts discriminative features from fingerprint images. to this end, the input fingerprint image is aligned to a base fingerprint through minutiae vector comparison. the aligned input fingerprint is then subtracted from the base fingerprint to generate a residual image. this residual image, along with the aligned input fingerprint and the base fingerprint, constitutes the three input channels for a pre-trained inception model. our main contribution lies in the alignment of fingerprint minutiae, followed by the construction of a color fingerprint representation. moreover, we collected a dataset, including 200 fingerprint images corresponding to 20 persons, for fingerprint verification. the proposed method is evaluated on two distinct datasets, demonstrating its superiority over existing state-of-the-art techniques. with a verification accuracy of 99.40% on the public hong kong dataset, our approach establishes a new benchmark in fingerprint verification. this research holds the potential for applications in various domains, including law enforcement, border control, and secure access systems.