A Deep Learning-based Model for Fingerprint Verification | ||
| Journal of AI and Data Mining | ||
| دوره 12، شماره 2، تیر 2024، صفحه 241-248 اصل مقاله (1.53 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2024.14298.2531 | ||
| نویسندگان | ||
| Mobina Talebian؛ Kourosh Kiani* ؛ Razieh Rastgoo | ||
| Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Fingerprint؛ Verification؛ Deep learning؛ Pretrained؛ Convolutional Neural Network | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 265 تعداد دریافت فایل اصل مقاله: 314 |
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