Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3462 2018 2018 Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq Maha Maha 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. 2025 2025 240 248 10.54216/FPA.180116 https://www.americaspg.com/articleinfo/3/show/3462