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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MDPI AG
2025-07-01
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| author | Sarah Almuwayziri Abeer Al-Nafjan Hessah Aljumah Mashael Aldayel |
| author_facet | Sarah Almuwayziri Abeer Al-Nafjan Hessah Aljumah Mashael Aldayel |
| author_sort | Sarah Almuwayziri |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1fb8a2471c664d349f1ffdfad1a8ca0c |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1fb8a2471c664d349f1ffdfad1a8ca0c2025-08-20T03:36:31ZengMDPI AGApplied Sciences2076-34172025-07-011515850210.3390/app15158502Deep Learning-Based Fingerprint–Vein Biometric Fusion: A Systematic Review with Empirical EvaluationSarah Almuwayziri0Abeer Al-Nafjan1Hessah Aljumah2Mashael Aldayel3Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi ArabiaComputer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi ArabiaComputer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi ArabiaInformation Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaUser 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.https://www.mdpi.com/2076-3417/15/15/8502biometric authenticationfingerprintfinger veindeep learningconvolutional neural networkmultimodal fusion |
| spellingShingle | Sarah Almuwayziri Abeer Al-Nafjan Hessah Aljumah Mashael Aldayel Deep Learning-Based Fingerprint–Vein Biometric Fusion: A Systematic Review with Empirical Evaluation Applied Sciences biometric authentication fingerprint finger vein deep learning convolutional neural network multimodal fusion |
| title | Deep Learning-Based Fingerprint–Vein Biometric Fusion: A Systematic Review with Empirical Evaluation |
| title_full | Deep Learning-Based Fingerprint–Vein Biometric Fusion: A Systematic Review with Empirical Evaluation |
| title_fullStr | Deep Learning-Based Fingerprint–Vein Biometric Fusion: A Systematic Review with Empirical Evaluation |
| title_full_unstemmed | Deep Learning-Based Fingerprint–Vein Biometric Fusion: A Systematic Review with Empirical Evaluation |
| title_short | Deep Learning-Based Fingerprint–Vein Biometric Fusion: A Systematic Review with Empirical Evaluation |
| title_sort | deep learning based fingerprint vein biometric fusion a systematic review with empirical evaluation |
| topic | biometric authentication fingerprint finger vein deep learning convolutional neural network multimodal fusion |
| url | https://www.mdpi.com/2076-3417/15/15/8502 |
| work_keys_str_mv | AT sarahalmuwayziri deeplearningbasedfingerprintveinbiometricfusionasystematicreviewwithempiricalevaluation AT abeeralnafjan deeplearningbasedfingerprintveinbiometricfusionasystematicreviewwithempiricalevaluation AT hessahaljumah deeplearningbasedfingerprintveinbiometricfusionasystematicreviewwithempiricalevaluation AT mashaelaldayel deeplearningbasedfingerprintveinbiometricfusionasystematicreviewwithempiricalevaluation |