Aug 2026· IEEE Transactions on Biometrics Behavior and Identity Science· 0 citations· 51 references
Computer Science
TL;DR
OpenVeinNet, a finger vein verification framework designed for cross-dataset and open-set evaluation, is presented and it is shown that explicitly modelling local vein geometry, global vascular relationships, and angularly compact embeddings is effective for openset finger vein verification.
Abstract
Finger vein verification is a promising biometric modality for secure authentication because vascular patterns are internal, difficult to observe externally, and relatively resistant to presentation attacks. However, reliable verification remains challenging in open-set settings, where test identities are unseen during training and non-enrolled probes must be rejected at inference. This paper presents OpenVeinNet, a finger vein verification framework designed for cross-dataset and open-set evaluation. The proposed model combines Dynamic Snake Convolution with graph-based feature modelling. Dynamic Snake Convolution extracts local curvilinear and tubular vein structures using adaptive sampling, while the graph convolutional backbone models long-range topological relationships between vein regions. To improve the discriminative quality of the embedding space, we introduce a Centroid Angular Hybrid Loss, which jointly encourages intra-class compactness and inter-class angular separation for cosinesimilaritybased verification. Experiments are conducted on five public finger vein datasets: FV-300, MMCBNU, FV-USM, PolyU, and VERA. The method is evaluated using leaveonedatasetout training under both enrolmentbased unknownrejection and fullsubject verification protocols, and is compared with handcrafted and recent deep learning-based baselines. The results show that OpenVeinNet achieves strong cross-dataset generalisation, consistently low equal error rates, and competitive true accept rates at fixed false accept rate operating points. Ablation studies further confirm the individual and combined contributions of adaptive tubular feature extraction, graph-based relational modelling, and the proposed loss function. These findings indicate that explicitly modelling local vein geometry, global vascular relationships, and angularly compact embeddings is effective for openset finger vein verification.
These findings suggest that pairing transfer learning with GAN-based augmentation is a practical, effective way to overcome data scarcity in finger vein recognition, offering a promising direction for dependable biometric systems.
Jyotiprakash Dash, P. P. Sarangi, Bhabani Shankar Prasad Mishra· F1000Research· 0 citations
Palm-vein recognition is a highly secure biometric modality due to the uniqueness and subcutaneous nature of vein patterns. However, low contrast in palm-vein images, caused by NIR light scattering and sensor limitations, remains a significant challenge. To address this, we propose the Intensity-Limited Adaptive Contrast Stretching with Bidirectional Gaussian-weighted Overlapping Tiles (ILACS-BGOT) method, an enhancement of the previously developed ILACS with Layered Gaussian-weighted Overlapping Tiles (ILACS-LGOT) technique. ILACS enhances local contrast, while BGOT mitigates blocky artefacts. This study further integrates RootSIFT features with KNN+RT and incorporates the previously introduced Mean and Median Distance (MMD) filter to investigate the parameter variations of both MMD and RT, and their impact on recognition performance. A comprehensive analysis was conducted across three benchmark datasets (CASIA, PolyU, and PUT), using 42 combinations of MMD filter thresholds and RT values. Results were evaluated using EER and Accuracy. Findings reveal that higher template sizes improve performance, while varying MMD thresholds reflect dataset-specific rotational variations. The proposed system demonstrates superior generalisability, achieving significant improvements in both EER and Accuracy over existing methods. Furthermore, the underlying ILACS-BGOT mechanism suggests potential applicability beyond palm vein recognition to other biometric modalities such as finger vein and palmprint recognition, and more generally to low-contrast image enhancement across computer vision applications.
K. Perera, F. Khelifi, A. Belatreche· arXiv.org· 0 citations
Palm-vein biometrics are increasingly used for secure, contactless authentication. Yet real-world deployment exposes them to surface noise (sweat, dirt), illumination and motion variation, and temperature-driven changes in vascular visibility, which remain underexplored for lack of data captured under such conditions. To study these effects, we introduce the Columbia University Palm-vein (CUP) dataset, to our knowledge the first public video-based palm-vein dataset. CUP records every palm under four surface conditions (a clean baseline, warm, wet, and dirty) and pairs each subject with physiological and demographic metadata. On it we benchmark twenty-one recognizers spanning static, video, and multi-frame aggregation architectures. Models that verify reliably on clean palms lose most of their accuracy on dirty ones, and the mean equal error rate (EER) roughly quadruples. We recover much of that robustness along both axes of the capture. Temporally, a consensus over the few frames the sensor already returns cancels transient corruption; spatially, a test-time matcher that adds no learned parameters fuses the global cosine with a saliency-steered region-level optimal transport that routes the comparison around corrupted regions. The full design leads on every surface of CUP in EER, TAR@FAR=0.01, and Rank-1, at 4.3M parameters and 3.1 GFLOPs, a fraction of the video models'cost. Attached to four frozen state-of-the-art backbones it cuts their mean EER by 29-37% without retraining, and on four public single-image datasets the regional matching alone still helps. A preliminary audit across ten demographic and physiological traits finds two warm-condition gaps, along body water and gender, that survive multiple-comparison correction. CUP will be released for non-commercial research use at https://github.com/MobileX-CU/CUP_v1 upon publication.
Xiaoxi Yan, Ke Liu, Abhilash Venkatesh et al.· 0 citations
The reliability of finger vein biometric systems is increasingly threatened by sophisticated presentation attacks. Current presentation attack detection (PAD) methods, often relying on supervised learning, are vulnerable to novel, unseen attacks because they depend on comprehensive labeled spoof datasets that are impractical to collect. To address this zero-day threat, this research proposes a one-class anomaly detection framework trained solely on authentic finger vein samples. While utilizing a standard U-Net-inspired denoising autoencoder as the architectural backbone, this work introduces two key novel contributions to tailor the model for biometric security: (1) a Multi-Objective Anomaly Detection Loss Framework that uniquely integrates multi-scale reconstruction error, gradient preservation constraints, and deep Support Vector Data Description loss to strictly regularize the latent space and (2) a Spectral Error Optimization technique that applies adaptive frequency weighting to amplify subtle texture artifacts inherent in spoof mediums. This combination is significant because the multi-objective loss forces the model to learn fine-grained physiological vein patterns, while the spectral optimization captures high-frequency anomalies often missed by spatial reconstruction alone. Experimental results on the FVPAD-USM dataset demonstrate that this approach achieves an Attack Presentation Classification Error Rate below 2% and an Average Classification Error Rate below 10%. By outperforming supervised baselines like support vector machines and convolutional neural networks in generalizing to unseen digital and glossy photo attacks, this work establishes the effectiveness of unsupervised, frequency-enhanced anomaly detection for robust biometric security.
Received: 30 August 2025 | Revised: 18 March 2026 | Accepted: 18 June 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
The data that support the findings of this study are openly available in the FVPAD-USM database at http://drfendi.com/fvpad_usm_database/, reference number [33].
Author Contribution Statement
Mohd Shahrimie Mohd Asaari: Conceptualization, Methodology, Software, Formal analysis, Writing – original draft. Bakhtiar Affendi Rosdi: Validation, Investigation, Writing review & editing, Supervision. Andrew Tiong Hoe Pin: Methodology, Software, Validation, Investigation, Data curation, Visualization. Zahid Ur Rahman: Investigation, Resources, Data curation. Muhammad Firdaus Akbar: Validation, Funding acquisition.
M. Asaari, B. Rosdi, Andrew Tiong Hoe Pin et al.· Artificial Intelligence and...· 0 citations
Face recognition systems are widely used in surveillance, biometric authentication, access control, and digital identity verification; however, supervision sensitivity, evaluation stability, and performance consistency across datasets remain insufficiently understood. This study investigates the behavior of convolutional, transformer-based, and hybrid face recognition architectures under both Softmax and ArcFace supervision using five-fold subject-disjoint cross-validation on the Labeled Faces in the Wild (LFW) and FAGEv2 datasets. ResNet50, MobileNetV3, DeiT-Small, and a Hybrid multi-branch architecture integrating complementary convolutional and transformer feature representations were evaluated using Top-1 identification accuracy, Area Under the ROC Curve (AUC), Equal Error Rate (EER), computational complexity, and fold-level statistical analysis. Experimental results revealed substantial supervision sensitivity across architectures and datasets. On the LFW dataset, Hybrid-Softmax achieved the highest Top-1 identification accuracy (62.4%), while DeiT-Small-Softmax achieved the strongest verification performance with an AUC of 0.905 and EER of 0.159. On the FAGEv2 dataset, Hybrid-Softmax and DeiT-Small-Softmax achieved the highest identification accuracy (38.0%), while Hybrid-Softmax achieved the strongest verification performance with an AUC of 0.825 and EER of 0.251. Fold-level analyses demonstrated that the effect of ArcFace supervision varied across architectures and datasets, with consistent improvements observed for some convolutional architectures but not for transformer-based or hybrid models. Cross-dataset evaluation further revealed changes in model ranking and supervision behavior, indicating that comparative performance is strongly influenced by dataset characteristics and evaluation conditions. The findings demonstrate that additive angular margin supervision does not universally outperform conventional Softmax optimization and highlight the importance of multi-dataset benchmarking, fold-level evaluation, and supervision sensitivity analysis for robust and reproducible face recognition benchmarking.
Andisani Nemavhola, C. Chibaya, Serestina Viriri· Frontiers in Artificial Inte...· 0 citations
Comparing the deep learning architectures used for biometric verification in a fair manner has remained a difficult task. Researchers typically rely on different datasets, employ varying preprocessing steps, and adopt inconsistent evaluation criteria, all of which muddy the picture when trying to decide whether one network truly outperforms another. This work attempts to cut through that noise by fixing every variable except the backbone architecture itself. Residual Network-50) ResNet50), Efficient Network Version 2 Small (EfficientNetV2-S), and the Swin Transformer-Tiny (Swin-T) were trained and tested on the same LUTBIO and XJTU datasets, applying identical subject-disjoint splits across face, fingerprint, and palmprint modalities. The analysis measures Equal Error Rate (EER), Area Under the Curve, and True Acceptance Rate at False Acceptance Rate = 0.1%, and further examines five distinct score-level fusion strategies across every possible two- and three-modality pairing. The single-modality experiments reveal a clear pattern: Swin-T achieves a perfect 0.000% EER on faces, ResNet50 handles fingerprints best at 1.957% EER, and EfficientNetV2-S leads on palmprints with 0.140% EER. On LUTBIO, 10 out of 12 tested fusion configurations reach a reported EER of 0.000%. This study reports the effect sizes and selected confidence intervals for the key comparisons, allowing both their statistical and practical significance to be assessed. The results show that no single architecture is universally superior; the best-performing backbone is modality-dependent. Score-level fusion consistently improved verification accuracy over the best single modality, substantially reducing error rates. These findings imply that architecture choices for biometric systems should be modality-aware and that score-level fusion is an appropriate method for robust multimodal verification. The controlled protocol additionally provides a reproducible framework for fair architectural comparison in future biometric research.
Rowida Jasim Alazawi, N. Habeeb, Alaa Jabbar Qasim Almaliki· Kurdistan Journal of Applied...· 0 citations
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