2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 18 , Issue 1 , PP: 240-248, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Maha A. Al-Bayati 1 *
Doi: https://doi.org/10.54216/FPA.180116
Biometric verification has grown into critical to privacy across areas such as finance and safe accessing services. The present study addresses the utilization of techniques for deep learning, namely convolutional neural networks (CNNs), to boost both the precision and dependability of biometric authentication. Researchers explore the effectiveness of these algorithms on collections containing genuine and forged banknote photos, taking into account information collecting obstacles such as operator condition changes and ambient conditions. The novelty shows an incredible proficiency in classification of 100%, with clarity, recall, and F1-scores of 1.00 across the two categories, demonstrating that the representation is excellent at discerning amongst legitimate and replica materials. Further, researchers investigate the effects of different design variables on efficiency and precision. This investigation provides important insights into merging deep learning with biometric data, laying the basis for future safe authorization developments.
CCN , Deep Learning , Biometric , Classification , Banknote Authentication dataset
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