###### Abstract  
User authentication is crucial for safeguarding access to digital systems and services. Biometric authentication serves as a strong and user-friendly alternative to conventional security methods such as passwords and PINs, which are often susceptible to breaches. This study proposes a deep learning-based multimodal biometric system that combines fingerprint (FP) and finger vein (FV) modalities to improve accuracy and security. The system explores three fusion strategies: feature-level fusion (combining feature vectors from each modality), score-level fusion (integrating prediction scores from each modality), and a hybrid approach that leverages both feature and score information. The implementation involved five pretrained convolutional neural network (CNN) models: two unimodal (FP-only and FV-only) and three multimodal models corresponding to each fusion strategy. The models were assessed using the NUPT-FPV dataset, which consists of 33,600 images collected from 140 subjects with a dual-mode acquisition device in varied environmental conditions. The results indicate that the hybrid-level fusion with a dominant score weight (0.7 score, 0.3 feature) achieved the highest accuracy (99.79%) and the lowest equal error rate (EER = 0.0018), demonstrating superior robustness. Overall, the results demonstrate that integrating deep learning with multimodal fusion is highly effective for advancing scalable and accurate biometric authentication solutions suitable for real-world deployments. biometric authentication; fingerprint; finger vein; deep learning; convolutional neural network; multimodal fusion; feature-level fusion; score-level fusion; hybrid fusion; NUPT-FPV dataset 2025  
158022015802201580  
domain of intelligent systems assumes that recognition by a particular user is a function to follow in the process of granting access to information and services. Unimodal biometric systems incorporate only one trait, whereas multimodal biometric systems incorporate two or more traits. The most popular biometric modalities are FP, facial recognition, iris patterns, voice, palmprints, and some behavioral characteristics, as well as additional features, such as gait, ear images, retina mobility, and DNA patterns [3]. In recent years, researchers in the user recognition and authentication field have explored the use of biometric systems based on deep learning (DL) to enhance these systems [4; 5]. This study demonstrates improved accuracy and reliability for biometric systems by applying effective machine learning (ML) methodologies, particularly DL approaches such as convolutional neural networks (CNNs). Additionally, we implement multimodal biometric identification by incorporating multiple biometric traits and classification approaches, with the goal of enhancing the efficiency and accuracy of biometric integration compared to unimodal methods. The proposed research has substantial implications for user recognition and authentication systems. The primary aim of this study is to develop a multimodal biometric system capable of accurately recognizing users by employing FP and FV modalities and by leveraging pretrained DL methods. To achieve this, three fusion strategies are explored: feature-level fusion, which combines deep features before classification; score-level fusion, which integrates the prediction scores from each modality; and hybrid fusion, which combines both approaches to leverage their complementary strengths. The primary research question addressed in this study is as follows: _How can DL algorithms be used in multimodal biometric authentication systems for person recognition?_ This study makes the following assumptions: (i) This study examines whether multimodal biometric authentication systems can satisfactorily operate in person recognition using DL algorithms. (ii) This study elaborates on the advantages of using multiple biometrics to enhance the security of person or user authentication compared to unimodal and multimodal biometric systems.