Finger Vein Dataset Research Articles

Analysing the robustness of finger vein recognition: cross-dataset reliability and vein utility

AbstractFinger vein recognition is an emerging biometric trait known for its privacy features. Despite the remarkable performance of deep learning methods like convolutional neural networks on challenging finger vein datasets, their reliability and robustness need further examination. This study evaluates the robustness of three recognition methods—the traditional Miura Method, a supervised convolutional neural network, and an unsupervised convolutional auto-encoder—through the challenging and more realistic scenario of cross-dataset comparisons. We also analyse the reliability of these methods in terms of sample quality. We introduce a novel vein quality metric to measure vein clarity and complexity and compare it against an existing image quality metric, natural image quality evaluator. Our findings reveal differences in how these recognition methods utilise finger vein images for comparisons, highlighting the need for robust recognition techniques in more realistic scenarios. In addition, our vein quality metric effectively detects defective images, reducing the zero false-match rate from 34.98% to 8.18% on the SDUMLA-HMT dataset. These results indicate the need for metrics more focussing on finger vein image characteristics for effective quality assessment for finger vein images.

DDP-FedFV: A Dual-Decoupling Personalized Federated Learning Framework for Finger Vein Recognition.

Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditional centralized finger vein recognition algorithms face privacy and security issues. Federated learning, a distributed training method that protects data privacy without sharing data across endpoints, is gradually being promoted and applied. Nevertheless, its performance is severely limited by heterogeneity among datasets. To address these issues, this paper proposes a dual-decoupling personalized federated learning framework for finger vein recognition (DDP-FedFV). The DDP-FedFV method combines generalization and personalization. In the first stage, the DDP-FedFV method implements a dual-decoupling mechanism involving model and feature decoupling to optimize feature representations and enhance the generalizability of the global model. In the second stage, the DDP-FedFV method implements a personalized weight aggregation method, federated personalization weight ratio reduction (FedPWRR), to optimize the parameter aggregation process based on data distribution information, thereby enhancing the personalization of the client models. To evaluate the performance of the DDP-FedFV method, theoretical analyses and experiments were conducted based on six public finger vein datasets. The experimental results indicate that the proposed algorithm outperforms centralized training models without increasing communication costs or privacy leakage risks.

Multi-Biometric System Based On The Fusion Of Fingerprint And Finger-Vein

Biometrics is the process of measuring the unique biological traits of an individual for identification and verification purposes. Multiple features are used to enhance the security and robustness of the system. This study concentrates exclusively on the finger and employs two modalities - fingerprint and finger vein. The proposed system utilizes feature extraction for finger vein and two matching algorithms, namely ridge-based matching, and minutiae-based matching, to derive matching scores for both biometrics. The scores from the two modalities are combined using four fusion approaches: holistic fusion, non-linear fusion, sum rule-based fusion, and Dempster-Shafer theory. The ultimate decision is made by the performance metrics and the Receiver Operating Characteristics (ROC) curve of the fusion technique with the best results. The proposed technique is tested on images collected from the “Nanjing University Posts and Telecommunications- Fingerprint and Finger vein dataset (NUPT-FPV).” According to the results, which were obtained for 840 input images the proposed system accomplishes the Equal Error Rate (EER) of 0% while using Dempster Shafer-based fusion and 14% while using the other three fusion techniques. Also, the False Acceptance Rate (FAR) is very low at 0% for all the fusion techniques which are crucial for security and preventing unauthorized access.

A frequency-injection backdoor attack against DNN-Based finger vein verification

Recent research has demonstrated that deep neural networks, typically used in finger vein recognition, are susceptible to different types of attacks, such as adversarial attacks, data poisoning attacks, and backdoor attacks. Among the attacks, backdoor attacks occur in almost every stage of the deep learning pipeline. Finger vein recognition has extensively been used in real-world applications for personal identity authentication. To develop a secure finger vein recognition system, one must study possible backdoor attacks, which can embed hidden malicious behaviors into the system. Existing backdoor attacks are as easily perceptible as conspicuous spatial triggers and difficult-to-resist data augmentation. To address this issue, we propose a novel frequency-injection-based backdoor attack method capable of delivering attacks in finger vein recognition. Specifically, images are transformed from the spatial domain to the frequency domain by discrete wavelet transform (DWT), and the trigger injects several times in the high-frequency part in the vertical direction. Experimental results of public finger vein datasets validate the proposed method's effectiveness, showing good attack performance and bypassing backdoor defense.

A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches

Finger vein recognition is gaining popularity in the field of biometrics, yet the inter-operability of finger vein patterns has received limited attention. This study aims to fill this gap by introducing a cross-device finger vein dataset and evaluating the performance of finger vein recognition across devices using a classical method, a convolutional neural network, and our proposed patch-based convolutional auto-encoder (CAE). The findings emphasise the importance of standardisation of finger vein recognition, similar to that of fingerprints or irises, crucial for achieving inter-operability. Despite the inherent challenges of cross-device recognition, the proposed CAE architecture in this study demonstrates promising results in finger vein recognition, particularly in the context of cross-device comparisons.

Automated vein verification using self-attention-based convolutional neural networks

Vein-based biometric traits have been regarded as trustworthy for biometric applications. With technical advances in deep learning, verification performance has started to be improved in these applications to increase trust level in daily life by providing usage convenience and user satisfaction. In this study, the effect of self-attention mechanism on convolutional neural networks for the performance of finger-vein and hand dorsal vein verification was investigated using an open-set protocol. To provide generalizability to the trained model, self-attention-based convolutional neural networks were used rather than existing architectures and pre-trained models. With the architecture that uses residual blocks and self-attention mechanism, a fair verification performance was suggested. Verification performance was assessed on DHVI-DB and Bosphorus hand dorsal vein datasets and SDUMLA and PolyU-F finger-vein datasets in terms of equal error rate using the distance between feature vectors through the existing and the proposed distance measures. The obtained equal error rates for hand dorsal vein datasets DHVI-DB1, DHVI-DB2, and Bosphorus are 2.17, 2.21, and 18.33, respectively and for finger-vein datasets SDUMLA and PolyU-F, are 1.65 and 10.64, respectively. Moreover, 4 different loss functions were used throughout the conducted experiments to see the discriminative ability of the proposed network for vein verification. The experimental results on these datasets indicate the potential effectiveness of the self-attention mechanism on automated vein verification.

FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication

Most finger vein authentication systems suffer from the problem of small sample size. However, the data augmentation can alleviate this problem to a certain extent but did not fundamentally solve the problem of category diversity. So the researchers resort to pre-training or multi-source data joint training methods, but these methods will lead to the problem of user privacy leakage. In view of the above issues, this paper proposes a federated learning-based finger vein authentication framework (FedFV) to solve the problem of small sample size and category diversity while protecting user privacy. Through training under FedFV, each client can share the knowledge learned from its user’s finger vein data with the federated client without causing template leaks. In addition, we further propose an efficient personalized federated aggregation algorithm, named federated weighted proportion reduction (FedWPR), to tackle the problem of non-independent identically distribution caused by client diversity, thus achieving the best performance for each client. To thoroughly evaluate the effectiveness of FedFV, comprehensive experiments are conducted on nine publicly available finger vein datasets. Experimental results show that FedFV can improve the performance of the finger vein authentication system without directly using other client data. To the best of our knowledge, FedFV is the first personalized federated finger vein authentication framework, which has some reference value for subsequent biometric privacy protection research.

FVFSNet: Frequency-Spatial Coupling Network for Finger Vein Authentication

Finger vein biometrics is becoming an important source of human authentication due to its advantages in terms of liveness detection, high security, and user convenience. Although there exist a lot of deep learning-based methods for finger vein authentication, they only extract features from finger vein images in the spatial domain and may lose some important information that is present in other domains, such as the frequency domain. Motivated by this conjecture and the remarkable performance of image feature extraction in the frequency domain, this work explores a method capable of extracting finger vein features in both the spatial and frequency domains. Therefore, the features extracted from different domains can complement each other. In addition, we propose a novel frequency-spatial coupling network (FVFSNet) for finger vein authentication. FVFSNet is mainly composed of three parts: (1) the frequency domain processing module (FDPM), (2) the spatial domain processing module (SDPM), and (3) the frequency-spatial coupling module (FSCM). The FDPM is used to extract the finger vein features present in the frequency domain, which is mainly composed of the frequency-spatial domain transformation and the frequency domain convolution layer. The SDPM is used to extract the finger vein features present in the spatial domain, which is mainly composed of convolution layers with an efficient design. The FSCM is used to couple the features extracted from the FDPM and SDPM, which is mainly composed of the channel and spatial attention mechanisms. To validate our conjecture and the performances of FVFSNet, extensive experiments are conducted on nine commonly used publicly available finger vein datasets. Experimental results show that the frequency domain constitutional neural network has a surprising effect on finger vein authentication, and the proposed FVFSNet achieves the state-of-the-art performance with the advantages of lightweight and low computational cost.

A Sparsified Densely Connected Network with Separable Convolution for Finger-Vein Recognition

At present, ResNet and DenseNet have achieved significant performance gains in the field of finger-vein biometric recognition, which is partially attributed to the dominant design of cross-layer skip connection. In this manner, features from multiple layers can be effectively aggregated to provide sufficient discriminant representation. Nevertheless, an over-dense connection pattern may induce channel expansion of feature maps and excessive memory consumption. To address these issues, we proposed a low memory overhead and fairly lightweight network architecture for finger-vein recognition. The core components of the proposed network are a sequence of sparsified densely connected blocks with symmetric structure. In each block, a novel connection cropping strategy is adopted to balance the channel ratio of input/output feature maps. Beyond this, to facilitate smaller model volume and faster convergence, we substitute the standard convolutional kernels with separable convolutional kernels and introduce a robust loss metric that is defined on the geodesic distance of angular space. Our proposed sparsified densely connected network with separable convolution (hereinafter dubbed ‘SC-SDCN’) has been tested on two benchmark finger-vein datasets, including the Multimedia Lab of Chonbuk National University (MMCBNU)and Finger Vein of Universiti Sains Malaysia (FV-USM), and the advantages of our SC-SDCN can be evident from the experimental results. Specifically, an equal error rate (EER) of 0.01% and an accuracy of 99.98% are obtained on the MMCBNU dataset, and an EER of 0.45% and an accuracy of 99.74% are obtained on the FV-USM dataset.

A study on finger vein recognition with few samples based on residual connected meta‐learning

AbstractA meta‐learning based method for finger vein recognition with few samples is proposed to overcome the problem of low recognition accuracy caused by the small number and variety of finger vein samples as well as fuzzy vein lines. The method is based on meta‐learning, incorporating multiscale features, and using the idea of residual networks to join meta‐learning to improve the recognition accuracy of finger vein images with few samples; to further improve its recognition ability, a differential map is constructed in the form of a differential between the finger vein image of singular value decomposition and finger vein image. We are the first to apply meta‐learning to the field of finger vein recognition, to our knowledge, and the experiments show that this approach is superior, with recognition accuracy of up to 99.13% for finger vein datasets with few‐shot samples.

Anti-aliasing convolution neural network of finger vein recognition for virtual reality (VR) human-robot equipment of metaverse.

Metaverse, which is anticipated to be the future of the internet, is a 3D virtual world in which users interact via highly customizable computer avatars. It is considerably promising for several industries, including gaming, education, and business. However, it still has drawbacks, particularly in the privacy and identity threads. When a person joins the metaverse via a virtual reality (VR) human-robot equipment, their avatar, digital assets, and private information may be compromised by cybercriminals. This paper introduces a specific Finger Vein Recognition approach for the virtual reality (VR) human-robot equipment of the metaverse of the Metaverse to prevent others from misappropriating it. Finger vein is a is a biometric feature hidden beneath our skin. It is considerably more secure in person verification than other hand-based biometric characteristics such as finger print and palm print since it is difficult to imitate. Most conventional finger vein recognition systems that use hand-crafted features are ineffective, especially for images with low quality, low contrast, scale variation, translation, and rotation. Deep learning methods have been demonstrated to be more successful than traditional methods in computer vision. This paper develops a finger vein recognition system based on a convolution neural network and anti-aliasing technique. We employ/ utilize a contrast image enhancement algorithm in the preprocessing step to improve performance of the system. The proposed approach is evaluated on three publicly available finger vein datasets. Experimental results show that our proposed method outperforms the current state-of-the-art methods, improvement of 97.66% accuracy on FVUSM dataset, 99.94% accuracy on SDUMLA dataset, and 88.19% accuracy on THUFV2 dataset.

FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling

In this paper, we present FVR-Net, which is a novel finger vein recognition network using a convolutional neural network (CNN) with a hybrid pooling mechanism. The scheme is based on the use of a block-wise feature extraction network to extract discrete features from interclass vein image samples, regardless of their visual quality. Input images to FVR-Net are subjected to preprocessing prior to being fed into the network in order to segment the vein patterns from the background. We designed a feature extraction network in which each block consists of a convolutional layer followed by hybrid pooling, whose output activation maps are concatenated before passing features to another block within the network. In the hybrid pooling layer, two subsampling layers of maxpooling and average pooling are placed in parallel where the former activates the most discrete features of the input, and the latter considers the complete extent of the input volume so better localization of features can be accessed. After the features are extracted, they are passed to three fully connected layers (FCLs) for classification. We conduct several experiments on two publicly available finger vein datasets based on visual quality of the images. When compared to conventional studies, the proposed model achieves outstanding recognition performance with accuracies of up to 97.84% and 97.22% for good and poor-quality images, respectively. By varying multiple network hyperparameters, we obtain optimal settings such that the model can guarantee the best recognition accuracy for a finger vein biometric system.

An ROI Extraction Method of Finger Vein Images Based on Large Receptive Field Gradient Operator for Accurate Localization of Joint Cavity.

Region of interest (ROI) extraction is a key step in finger vein recognition preprocessing. The current method takes the finger region in the vein image as the ROI, but this method cannot obtain better recognition accuracy because it only removes the background noise of the image and ignores factors such as the position and shape of the finger. To solve this problem, we limited the ROI to a fixed region between two finger joint cavities, proposed a new large receptive field gradient operator, and designed and implemented a new method for ROI extraction. It uses a large receptive field to search the target, which is similar to human vision, thus solving the problem of difficult ROI localization for images with large gradient areas. Moreover, for external factors such as noise and uneven illumination in the vein image, the interference factors can be eliminated by averaging them to a larger range with a larger size operator to improve the accuracy of the subsequent matching recognition. To verify the effectiveness of the proposed method, we conducted sufficient matching experiments on three public finger vein datasets. On various datasets, our method significantly reduced the identified EER value, with the lowest EER value reaching 0.96%. The experimental results show that the proposed ROI extraction method can effectively eliminate the influence of finger position, finger shape, and other factors on the subsequent recognition performance, accurately locate the finger joint cavity, and effectively improve the recognition performance.

V-Vibe: A robust ROI extraction method based on background subtraction for vein images collected by infrared device

In recent years, the global epidemic has put forward higher requirements for public health security, and the non-contact finger vein acquisition and identification technology based on infrared imaging has become a research hotspot due to its high security, accuracy and convenience. However, infrared images generally have the problems of low resolution, low contrast and low signal-to-noise ratio, which seriously affect the effective extraction and utilization of image feature information. Moreover, most of the images captured by unconstrained vein acquisition devices have a large number of complex backgrounds, which are often similar to the pixel distribution of finger region, which greatly affects the recognition performance of vein images. To solve the above problems, we carried out the following exploratory work. First, we designed and developed a new finger vein infrared acquisition device. The device adopts a kind of physical structure of triangular spotlight, and in combination with our proposed V-Vibe method can achieve complete Region of Interest (ROI) extraction of finger vein images. Second, we created a new finger vein dataset (named FV-BS) for background subtraction. The dataset contains 420 individuals, 4,200 finger vein image samples and 420 device background images, as well as a subset of vein image data filled with complex backgrounds. Third, we proposed a new method named V-Vibe to extract ROI from vein images by using background subtraction technology. Subtraction can effectively eliminate factors that affect recognition performance in both vein images and background sample set, thus greatly reducing the influence of complex backgrounds and low-quality infrared images on identification. To verify the validity and accuracy of the V-Vibe method, we designed and conducted a large number of experiments on several datasets, including four publicly available datasets, as well as self-built FV-BS, and analyzed the results in detail. Compared with other methods, the ROI extraction effect of V-Vibe method proposed in this paper is significantly improved, and EER is reduced by 1.18% and 2.37% on average respectively in maximum curvature and repeated line tracking feature extraction methods, which is an effective method for ROI extraction of finger vein images. Furthermore, we also tested the proposed V-Vibe method to extract ROI from the vein images of palm and back of hands. Experimental results show that our method has excellent segmentation accuracy on both palm and dorsal hand vein datasets, with the average F1 Score reaching 95.1%.

Finger Vein Image ROI Extraction Based on Active Contour Method

Under the background of the novel coronavirus pneumonia outbreak in the world, unrestricted and contactless finger vein collection devices have significantly improved public health safety. However, due to the unfixed position of the finger and the open or semi-open characteristics of the acquisition device, it is inevitable to introduce plenty of factors that affect the recognition performance, such as low contrast, uneven illumination and edge disappearance. In view of these practical problems, we propose a method for ROI extraction of finger vein images that combines active contour method and morphological post-processing operations. This method starts from the local segmentation, and finally completes the acquisition of finger masks at the global level, and then combines some morphological operations to achieve precise extraction of finger masks. We designed and conducted plenty of comparison experiments on the proposed algorithm and the current mainstream finger vein image ROI extraction methods on three public available finger vein datasets. Experimental results show that our method accurately extracts the complete finger region mask and achieves the best matching accuracy on all datasets.

A Dataset and Benchmark for Multimodal Biometric Recognition Based on Fingerprint and Finger Vein

Compared with single biometric recognition, multimodal biometric recognition based on fingerprint and finger vein has been widely considered because of its convenient sample collection, high security and accurate recognition. However, according to our investigation, there is no public dataset of fingerprint and finger vein collected at the same time. The existing work uses fingerprint datasets and finger vein datasets from different sources for research, besides the researchers data from building their own equipment, which lacks consideration of practical applications. This is not conducive to the promotion of multibiometric technology based on finger. To promote research on multimodal biometric recognition based on fingerprint and finger vein, we design a finger collection device and introduce a new dataset, NUPT-FPV. It is the first public dataset to collect fingerprint and finger vein simultaneously in real-world applications. NUPT-FPV obtained 840 finger information from 140 volunteers, each finger was collected 20 times (collected in two sessions), and 33600 fingerprint and finger vein images were obtained. In addition, we propose a novel multimodal fusion method based on a convolutional neural network as a benchmark. Extensive experiments were conducted to verify the necessity of our dataset. Through the released dataset and benchmark, we hope to further promote the development of multimodal biometrics based on fingerprint and finger vein.

FVT: Finger Vein Transformer for Authentication

In recent years, deep learning-based finger vein (FV) authentication has attracted the attention of biometric researchers and achieved breakthrough results. Previously, convolutional neural networks (CNNs) were the most commonly used deep learning-based methods for FV authentication. Recently, the vision Transformer-based method has started getting attention from the research community due to its excellent performance in many computer vision tasks. In this paper, we delve into vision Transformers and propose a novel model, FV Transformer (FVT), for finger vein authentication. FVT consists of four key modules: (1) the conditional position embedding, which is capable of dynamically generating position codes according to the input FV tokens; (2) the weight-shared expanded multi-layer perceptron, which helps to extract richer and more robust token information; (3) the local information-enhanced feed-forward network, which enhances the ability of local information extraction; and (4) the expansion-less mechanism for aggregating adjacent FV tokens, which implements the pyramid structure, and hence the multi-level feature extraction capability is introduced to the Transformer architecture, which originally focuses on global information. To fully validate the performance and generalization of FVT, experiments were conducted on nine publicly available FV datasets. The effectiveness of each key module of FVT is demonstrated in the ablation experiments. And the comparative experiments show that FVT outperforms several baseline Transformer models and achieves competitive performance when compared with the state-of-the-art finger vein authentication methods.

Finger Vein Recognition Algorithm Based on Lightweight Deep Convolutional Neural Network

Even though the deep neural networks have strong feature representation capability and high recognition accuracy in finger vein recognition, the deep models are computationally intensive and poor in timeliness. To address these issues, this article proposes a lightweight algorithm for finger vein image recognition and matching. The proposed algorithm uses a lightweight convolutional model in the backbone network and employs a triplet loss function to train the model, which not only improves the matching accuracy, but also satisfies the real-time matching requirements. In addition, the Mini-region of interest (RoI) and finger vein pattern feature extraction also effectively solve the problems of large amounts of calculation and background noise. Moreover, the present model recognizes new categories based on the feature vector space constructed by the finger vein recognition system, so that new categories can be recognized without retraining the model. The results show that the finger vein recognition and matching algorithm proposed in this article achieves 99.3% and 99.6% in recognition accuracy and 14.2 and 16.5 ms in matching time for the dataset Shandong University Machine Learning and Applications Laboratory-Homologous Multimodal Biometric Traits (SDUMLA-HMT) and Peking University Finger Vein Dataset (PKU-FVD), respectively. These metrics show that our approach is time-saving and more effective than previous algorithms. Compared with the state-of-the-art finger vein recognition algorithm, the proposed algorithm improves 1.45% in recognition accuracy while saving 45.7% in recognition time.

A Joint Bayesian Framework Based on Partial Least Squares Discriminant Analysis for Finger Vein Recognition

Finger vein recognition has attracted considerable attention from the biometric identification technology community owing to its convenience and security. Unlike most previous works only pay attention to one part of finger vein recognition, we propose a joint Bayesian framework in this paper, which is based on partial least squares discriminant analysis (PLS-DA). It involves three major stages: 1) robust feature description, 2) discriminative feature mapping, and 3) separative verification. In stage 1), we extract line responses and orientation of finger veins using a bank of Gabor filter, and histograms are constructed in local patches as primitive features. Subsequently, in stage 2), a discriminant feature mapping based the PLS-DA (PLS-DA-FM) method is proposed to project these primitive features into low-dimensional forms in a supervised manner. Thus, highly compact and discriminative features are obtained in this stage. Finally, in stage 3), we directly build a Bayesian model based on the joint distribution of finger vein feature pairs to measure the similarity between the features. Extensive experiments on five finger vein datasets demonstrate the superior performance of the proposed method to most state-of-the-art finger vein recognition methods.

Multi-instance Finger Vein-based Authentication with Secured Templates

The illegitimate access to biometric templates is one of the major issues to be handled for authentication systems. In this work, we propose to use two instances of finger vein images which inherits the advantages of a robust multi-modal biometric authentication system without needing different sensors. Two local texture feature extraction methods are experimented on standard finger-vein datasets. Fused discriminating features with reduced dimension lowers down the system computational cost. A cancelable template protection scheme as Gaussian Random Projection based Index-of-Max is then applied for embedding privacy and security to the templates. Foremost template protection properties like revocability, non-invertibility and unlinkability are observed to be significantly obeyed by the proposed system with considerable authentication performance. Recognition performance of the proposed methods are compared with some previously executed finger vein systems and observed to be less complex and overperforming on the combined basis of authentication and template protection. Thus, the proposed system utilizes multiple evidence and provides a balanced performance with respect to authentication, template protection and computational cost.