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

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-...

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Vydané v:Applied sciences Ročník 15; číslo 15; s. 8502
Hlavní autori: Almuwayziri, Sarah, Al-Nafjan, Abeer, Aljumah, Hessah, Aldayel, Mashael
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.08.2025
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app15158502