Deep Learning-Based Fingerprint–Vein Biometric Fusion: A Systematic Review with Empirical Evaluation

• Published in: Applied sciences Vol. 15; no. 15; p. 8502
• Main Authors: Almuwayziri, Sarah, Al-Nafjan, Abeer, Aljumah, Hessah, Aldayel, Mashael
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2025
• Subjects: Access control; Access to information; Accuracy; Algorithms; biometric authentication; Biometric identification; Biometrics; Biometry; Business metrics; Classification; convolutional neural network; Datasets; Deep learning; finger vein; fingerprint; Forgery; Literature reviews; Machine learning; multimodal fusion; Neural networks; Performance evaluation; Safety and security measures; Systematic review; Systems and data security software
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15158502

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.