Vein recognition is a secure biometric technology often constrained by limited annotated data and imaging variations. While data augmentation mitigates this, strategies designed for natural images may disrupt the fine-grained topology and textures essential for identity discrimination. We present AGVBench, which evaluates 30 representative augmentation strategies on five public palm- and finger-vein datasets with seven backbone architectures, covering classic CNNs, vision transformers, and vein-specific recognition models. Our results show that multi-image mixing methods (e.g., MixUp, PuzzleMix, StarMixup) generally provide the strongest recognition performance. However, they are often poorly calibrated and vulnerable to adversarial perturbations, revealing a clear inconsistency between clean accuracy and adversarial security. We also find that severe geometric transformations frequently degrade recognition, which is potentially due to feature misalignment or spatial cropping, and that augmentation effectiveness varies across palm and finger vein datasets. These findings prove that accuracy-centric evaluation is insufficient for biometric augmentation. AGVBench provides standardized protocols to support reproducible research and guide the design of reliable, secure, and robust vein recognition systems. Our codebase is available at https://github.com/Advance-VeinTech-Innovators/AGVBench.
Background Finger vein biometrics has evolved into a reliable biometric modality because of its inherent resistance to spoofing and robustness to external attacks. In recent years, advances in deep learning-based person identification using finger vein trait have further enhanced its performance and effectiveness. However, convolutional neural networks (CNNs) need large volumes of labeled data to perform well, and finger vein datasets are typically small, making this shortage of training samples one of the major obstacles to building reliable, deployable systems. Method This paper addresses that gap by combining transfer learning with Conditional GAN-based data augmentation to build a more robust finger vein recognition framework. Rather than relying on conventional augmentation alone, synthetic, class-conditioned finger vein images are generated using two GAN variants, Conditional DCGAN and Conditional WGAN-GP, which incorporate class-conditional information to enable stable, controllable adversarial training and improve intra-class compactness and inter-class discrimination while enriching the training data. To evaluate the generalization capability of the proposed approach, extensive experiments are conducted on two widely used benchmark databases, THU-FVFDT2 and FV-USM, under various data-split schemes. For classification, four established CNN architectures such as VGG19, MobileNetV2, InceptionV3, and EfficientNetV2-M were fine-tuned and evaluated using standard classification metrics for all partitioning schemes. Results The experimental results were consistently strong across both datasets and confirming that the framework generalizes well with consistently high classification accuracy across both datasets. As observed, MobileNetV2 reached 99.74% identification accuracy on THU-FVFDT2 dataset, while EfficientNetV2-M achieved the best overall accuracy 99.85% on FV-USM dataset using the Conditional DCGAN augmentation technique. Conclusion 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
Overall, GenPix provides a challenging and realistic benchmark for evaluating modern detectors, and the proposed AAE offers an efficient, interpretable baseline for future research on general-purpose fake-image detection.
Guessoum Dalila, B. Nadjia, Boumahdi Fatima et al.· Iraqi Journal for Computer S...· 0 citations
Physical-domain adversarial attacks have been extensively studied in face recognition and object detection, yet the field of palmprint recognition remains largely unexplored. Existing methods generate grayscale patches constrained by the single-channel input of most palmprint models. When deployed on skin, these patches contrast sharply with the surrounding tissue and are readily noticeable to human observers, undermining the covertness required in practical attacks. To address this limitation, we propose a Color Adversarial Patch (CAP) generation algorithm that leverages style transfer principles to produce visually natural color patches while maintaining high attack success rates. The method initiates the patch with a style prior using a pre-trained Contrastive Arbitrary Style Transfer (CAST) model and jointly optimizes adversarial loss, style loss, and smoothness loss within a unified framework. A three-channel averaging strategy is adopted to ensure compatibility with single-channel recognition models during gradient backpropagation. Experiments on the Tongji palmprint dataset show that the generated color patches achieve average cosine similarity values above the decision threshold in physical-domain tests, with peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values significantly higher than those for their grayscale counterparts. Ablation studies validate the indispensable role of each loss component. CAP offers a practical balance between attack effectiveness and visual camouflage, demonstrating the feasibility of concealed physical-domain attacks on palmprint recognition systems.
Yue Liu, Qi Xiong, Lu Leng et al.· Electronics· 0 citations
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.
Sushrut Patwardhan, Raghavendra Ramachandra· IEEE Transactions on Biometr...· 0 citations
In computer vision and pattern recognition tasks, deep learning models are widely used, especially in face recognition systems. Even with their excellent performance, these models are still susceptible to a variety of adversarial manipulations, such as blur, additive noise, translation, flipping, scaling, rotation, and changes in illumination. Furthermore, some architectures might experience optimization problems like vanishing gradients, which would further impair the stability of the model. In order to provide robust face verification under adversarial attack, Optimized Deep Learning–based Adversarial Defense Mechanism (ODL-ADM) is proposed in this work. It projects adversarial samples into an immune feature space. A Learnable Convolutional Principal Component Network (LCPCN) is incorporated into the framework to create a representation space that is both discriminative and resistant to perturbations. Adversarially corrupted facial images are suppressed and reconstructed using a Stacked Attention-based Residual Generative Adversarial Network (SARGAN). Accurate identity recognition is achieved by an Improved Cross-Triple MobileNetV1 architecture after perturbation removal. Enhanced Fire Hawk Optimization (EFHO) is used for performance maximization and parameter tuning to further improve recognition performance. Following image reconstruction and adversarial perturbation removal, the suggested model achieves a 98% face recognition accuracy.
Sheilla Ann Bangoy Pacheco, Mahesh Goyani, Jayzel P. Bangoy et al.· ITEGAM- Journal of Engineeri...· 0 citations
Adversarial perturbations pose a practical integrity risk to vision-based decision systems by causing image classifiers to misclassify inputs after visually subtle changes. This paper evaluates a wrapper-based adversarial input screening approach that compares a classifier’s output on an original image with its output after benign feature-squeezing transformations. The evaluated transformations include median filtering, non-local means denoising, and bit-depth reduction. Using a frozen-backbone ResNet50 on CIFAR-10, the detector is assessed under untargeted and targeted Fast Gradient Sign Method attacks, followed by stronger Projected Gradient Descent verification. Detection thresholds are calibrated only on clean data using a fixed 5% false positive rate protocol and are validated on held-out clean samples. The results show that softmax-space ℓ1 inconsistency provides moderate, transformation-dependent detection, whereas logit-space ℓ2 inconsistency yields a stronger, more stable screening signal. Median filtering with logit-space ℓ2 achieves near-complete detection at ε = 8/255 and remains the most reliable configuration across the perturbation sensitivity analysis, while non-local means denoising becomes effective mainly for large perturbations. An additional two-stage gate is evaluated for deployment-oriented alert triage; it does not replace the primary detector or override its screening decision, but ranks flagged inputs by risk severity. Further evaluations show that performance decreases on TinyImageNet and that targeted threshold-aware adaptive optimisation can substantially reduce detection recall. The findings support the use of median filtering with logit-space ℓ2 inconsistency as a tool for screening adversarial inputs to image classifiers, but its effectiveness depends on dataset complexity, classifier behaviour, and attack adaptivity.
Alketa Hyso, Dezdemona Gjylapi· Journal of Innovative Image...· 0 citations