Finger Vein Research Articles

Development of Adaptive Vision Transformer-Based Multi-Scale EfficientNetB7 Network for Secured Authentication System Using Finger Vein Patterns

Finger vein recognition represents an important biometric attribute that is viewed as reliable, emerging, and secure. Finger veins possess the benefit that they are less vulnerable to determining theft since veins are found below the layer of skin, and are also not influenced by the natural aging of the user. The present advanced methods are capable of offering acceptable efficiency. However, they depend on the nature of the processed finger-vein data. Thus, in this paper, by employing deep learning, an intelligent finger vein pattern-based authentication model is introduced. In the beginning, images are accumulated from global resources. Further, the accumulated finger vein images are provided to the introduced Adaptive Vision Transformer-based Multi-scale EfficientNetB7 (AViTMENetB7) network for authentication purposes. This network helps to boost the safety and robustness of the model. Here, the Improved Garter Snake Optimization Algorithm (IGSOA) is introduced to reduce the computational complexity and increase the functionality rates. In the end, by contrasting the implemented vein-based authentication model with traditional vein-based authentication models, the numerical analysis is conducted.

MixCFormer: A CNN–Transformer Hybrid with Mixup Augmentation for Enhanced Finger Vein Attack Detection

Finger vein recognition has gained significant attention for its importance in enhancing security, safeguarding privacy, and ensuring reliable liveness detection. As a foundation of vein recognition systems, vein detection faces challenges, including low feature extraction efficiency, limited robustness, and a heavy reliance on real-world data. Additionally, environmental variability and advancements in spoofing technologies further exacerbate data privacy and security concerns. To address these challenges, this paper proposes MixCFormer, a hybrid CNN–transformer architecture that incorporates Mixup data augmentation to improve the accuracy of finger vein liveness detection and reduce dependency on large-scale real datasets. First, the MixCFormer model applies baseline drift elimination, morphological filtering, and Butterworth filtering techniques to minimize the impact of background noise and illumination variations, thereby enhancing the clarity and recognizability of vein features. Next, finger vein video data are transformed into feature sequences, optimizing feature extraction and matching efficiency, effectively capturing dynamic time-series information and improving discrimination between live and forged samples. Furthermore, Mixup data augmentation is used to expand sample diversity and decrease dependency on extensive real datasets, thereby enhancing the model’s ability to recognize forged samples across diverse attack scenarios. Finally, the CNN and transformer architecture leverages both local and global feature extraction capabilities to capture vein feature correlations and dependencies. Residual connections improve feature propagation, enhancing the stability of feature representations in liveness detection. Rigorous experimental evaluations demonstrate that MixCFormer achieves a detection accuracy of 99.51% on finger vein datasets, significantly outperforming existing methods.

Robust Finger Vein Presentation Attack Detection Using XceptionNet-based Modified Depthwise Separable Convolutional Neural Network

Finger vein presentation attack detection (FVPAD) biometric systems have seen substantial enhancements through the application of deep learning convolutional neural networks (DCNN). This advancement led to increased complexity, parameters and resource requirements. To address these challenges, a novel modification to the first entry flow of the XceptionNet architecture based on customized depthwise separable convolution (DSC) CNN-based for extracting robust features from FV images to detect spoofing attacks is proposed in this paper. The proposed approach stands out for its simplicity in design, fewer parameters, reduced computational load, minimal resource and equipment needs, and minimum data overflow while maintaining high accuracy in verification and classification tasks. The developed FVPAD system includes FV image data preprocessing and augmentation, a modified XceptionNet architecture based on DSC to deeply extract robust features. Finally, the fully connected (FC) layers exclusively use the SoftMax activation function to normalize, predict and classify output classes. The model was evaluated on cropped FV images from the IDIAP and SCUT-SFVD datasets, achieving high accuracy rates of 100% and 99.499%, respectively. It also has the lowest number of trainable parameters at 131,106 acquired from fifteen convolutional and depth-separable convolution layers.

PbS Quantum Dot-Based Optoelectronic Memristors toward Multi-Task Reservoir Computing.

The rise of big data and the internet of things has driven the demand for multimodal sensing and high-efficiency low-latency processing. Inspired by the human sensory system, we present a multifunctional optoelectronic-memristor-based reservoir computing (OM-RC) system by utilizing a CuSCN/PbS quantum dots (QDs) heterojunction. The OM-RC system exhibits volatile and nonlinear responses to electrical signals and wide-spectrum optical stimuli covering ultraviolet, visible, and near-infrared (NIR) regions, enabling multitask processing of dynamic signals. The OM-RC system accurately performs health monitoring through dynamic electroencephalogram and electrocardiogram signal analysis and achieves object and traffic trajectory recognition for intelligent driving under challenging conditions like foggy environments. By collaboratively using the NIR perception and trajectory recognition, we develop a human-computer interaction authentication system that integrates finger veins and motion behaviors of humans, significantly enhancing the security of traditional fingerprint anticounterfeiting systems. This work demonstrates the potential of QD-based optoelectronic-memristor for multitask in-sensor processing applications.

Consistency and label constrained transfer low-rank representation for cross-light finger vein recognition

Finger vein sensors are embedded into all kinds of electronic devices for personal identification, and the upgrading of sensors is unavoidable. Therefore, the concern about cross-sensor finger vein recognition is raised recently. However, little attention is paid to cross-sensor finger vein recognition. The imaging light variation is one main difference between different sensors, and it brings large image differences, seriously degrading finger vein recognition performance. This paper focuses on cross-light finger vein recognition problem, in which we assume that the training and testing finger vein images are captured by different near-infrared lights, and proposes a consistency and label constrained transfer low-rank representation (CLTLRR) method for dealing with cross-light finger vein recognition. In the proposed method, we first transfer cross-light finger vein images into a common feature space to narrow the gap between training images and testing images, and achieve the low-rank linear representations of images. Then, we develop a consistency constraint between the low-rank coefficients in the common feature space and the sparse coefficients in the original feature space to enhance the discrimination of linear representation. In addition, we design a class label constraint for the projection matrix to guide image transfer. Finally, the low-rank coefficients and the projected features in the common feature space are integrated for recognition. Experiments are performed on single-light and cross-light finger palmar vein databases and finger dorsal vein databases, and the experimental results prove the effectiveness of our CLTLRR.

Mask-guided network for finger vein feature extraction and biometric identification.

The problems of complex background, low quality of finger vein images, and poor discriminative features have been the bottleneck of feature extraction and finger vein recognition. To this end, we propose a feature extraction algorithm based on the open-set testing protocol. In order to eliminate the interference of irrelevant areas, this paper proposes the idea of segmentation-assisted classification, that is, using the rough mask of the finger vein to constrain the feature learning process so that the network can focus on the vein area and learn greater weight for the vein. Specifically, the feature maps of the shallow layers of the network are first sent to the feature pyramid module to fuse the primary features of different scales, which are then sent to the spatial attention module to obtain the spatial weight map of the image. Based on the results of several classical vein skeleton extraction algorithms, a weighting method is used to obtain a more accurate mask to constrain the learning of the spatial weight map. Finally, a hybrid loss function combining triplet loss and cross-entropy loss is used to reduce the distance between feature vectors of the same categories and increase the distance between feature vectors of different categories in the Euclidean space, thereby improving feature discriminability. Good recognition results were achieved on the three public data sets of SDUMLA, MMCBNU, and FVUSM, and the values of equal error rate (EER) on them are as low as 2.50%, 0.20%, and 0.14%, respectively.

Multi-Scale convolutional neural network for finger vein recognition

With the continuous advancement of science and technology, an increasing number of deep learning methods are being applied in the field of finger vein recognition to describe the structural characteristics of finger veins. However, some deep learning methods fail to adequately extract longer texture features. during the feature extraction process, resulting in a decrease in the uniqueness of extracted finger vein features. Additionally, these methods tend to extract global information while neglecting the importance of local texture information. To address the aforementioned issues, this paper introduces a multiscale convolution network (MCNet) model based on finger vein structure. On one hand, a multiscale feature extraction (MFE) model based on the rectangular and square convolution kernels are employed to extract structural information from finger veins and to simultaneously enhance the features of longer texture features. On the other hand, the paper introduces a cross-information fusion attention (CFA) block that combines spatial and channel information, in order to enhance local details information and the network’s ability to extract vein patterns. The experimental results on the public datasets FV-USM, SDUMLA-HMT, and HKPU validate the effectiveness of MCNet with the recognition rates of 99.86%, 99.11%, and 99.15% respectively.

Identifikasi Pembuluh Darah Jari Tangan, Menggunakan Bahasa Pemrograman Python

Biometric systems automatically identify people based on their behavioral characteristics. Finger veins are more secure as biometric data than other biometric data. Finger vein patterns are difficult to forge because they are below the surface of the skin. To make the vascular pattern visible on the skin, a special method is needed to identify it. This research aims to develop a finger vein recognition system using the Convolutional Neural Network (CNN) method implemented in Python, with the aim of replacing the fingerprint-based employee attendance system in hospitals. This method offers higher security and resistance to forgery, given that finger veins are difficult to access from outside the body. This research utilizes the blood vessel dataset from Kaggle and goes through three main stages: preprocessing, training, and testing. The trained CNN model showed high performance with 99.99% accuracy in recognizing patterns in the finger vein images. The results of this study confirm that the developed system can provide accurate

Multimodal biometric identification: leveraging convolutional neural network (CNN) architectures and fusion techniques with fingerprint and finger vein data.

Advancements in multimodal biometrics, which integrate multiple biometric traits, promise to enhance the accuracy and robustness of identification systems. This study focuses on improving multimodal biometric identification by using fingerprint and finger vein images as the primary traits. We utilized the "NUPT-FPV" dataset, which contains a substantial number of finger vein and fingerprint images, which significantly aided our research. Convolutional neural networks (CNNs), renowned for their efficacy in computer vision tasks, are used in our model to extract distinct discriminative features. Specifically, we incorporate three popular CNN architectures: ResNet, VGGNet, and DenseNet. We explore three fusion strategies used in security applications: early fusion, late fusion, and score-level fusion. Early fusion integrates raw images at the input layer of a single CNN, combining information at the initial stages. Late fusion, in contrast, merges features after individual learning from each CNN model. Score-level fusion employs weighted aggregation to combine scores from each modality, leveraging the complementary information they provide. We also use contrast limited adaptive histogram equalization (CLAHE) to enhance fingerprint contrast and vein pattern features, improving feature visibility and extraction. Our evaluation metrics include accuracy, equal error rate (EER), and ROC curves. The fusion of CNN architectures and enhancement methods shows promising performance in identifying multimodal biometrics, aiming to increase identification accuracy. The proposed model offers a reliable authentication system using multiple biometrics to verify identity.

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.

CRCGAN: Toward robust feature extraction in finger vein recognition

Deep convolutional neural networks (CNNs) have produced remarkable outcomes in finger vein recognition. However, these networks often overfit label information, losing essential image features, and are sensitive to noise, with minor input changes leading to incorrect recognition. To address above problems, this paper presents a new classification reconstruction cycle generative adversarial network (CRCGAN) for finger vein recognition. CRCGAN comprises a feature generator, a feature discriminator, an image generator, and an image discriminator, which are designed for robust feature extraction. Concretely, the feature generator extracts features for classification, while the image generator reconstructs images from these features. Two discriminators provide feedback, guiding the generators to improve the quality of generated data. With this design of bi-directional image-to-feature mapping and cyclic adversarial training, CRCGAN achieves the extraction of essential features and minimizes overfitting. Additionally, precisely due to the extraction of essential features, CRCGAN is not sensitive to noise. Experimental results on three public databases, including THU-FVFDT2, HKPU, and USM, demonstrate CRCGAN’s competitive performance and strong noise resistance, achieving recognition accuracies of 98.36%, 99.17% and 99.49% respectively, with less than 0.5% degradation on HKPU and USM databases under noisy conditions.

Enhancing cell phone security through finger vein biometric authentication systems

The growing demand for secure mobile devices has led to the widespread adoption of biometric technologies. While personal identification numbers (PINs), passwords, gridlock patterns to safeguard phones and facial recognition have become common, they have certain limitations in terms of security and user convenience. These conventional security measures are riddled with susceptibilities, such as password pilferage, memory lapses in authentication details, and user mistakes in grid pattern creation. Due to the rising occurrence of cell phone incursions and theft, there is an urgent requirement for a strong security system that not only defends data but also shields the device itself. This paper proposes a resilient finger vein biometric security system as a more effective substitute for knowledge-based and password-based authentication techniques. The implementation of finger vein biometrics as a novel approach to enhancing cell phone security. The method employs infrared (IR) light transmission to capture the vein patterns and shadows resulting from the different thicknesses of finger muscles, bones, and tissues. The unique vascular patterns in fingers are difficult to forge, offering a higher level of security compared to other biometric systems. The simulations we conducted indicate an identification accuracy rate of 93.82%, indicating that this technique provides a substantial enhancement in preventing unauthorized access and theft of cell phones. This study examines the technology behind finger vein biometrics, its integration into mobile devices, and the security advantages it offers.

IIS-FVIQA: Finger Vein Image Quality Assessment with intra-class and inter-class similarity

In recent years, Finger Vein Image Quality Assessment (FVIQA) has been recognized as an effective solution to the problem of erroneous recognition resulting from low image quality due to false and missing information in finger vein images, and has become an important part of finger vein recognition systems. Compared to traditional FVIQA methods that rely on domain knowledge, newer methods that reject low-quality images have been favored for their independence from human interference. However, these methods only consider intra-class similarity information and ignore valuable information from inter-class distribution, which is also an important factor in evaluating the performance of recognition systems. In this work, we propose a novel FVIQA approach, named IIS-FVIQA, which concurrently takes into account the intra-class similarity density and inter-class similarity distribution distance within recognition systems. Specifically, our method generates quality scores for finger vein images by combining the information entropy of intra-class similarity distribution and Wasserstein distance of inter-class distribution. Then, we train a regression network for quality prediction using training images and corresponding quality scores. When a new image enters the recognition system, the trained regression network directly predicts the quality score of the image, making it easier for the system to select the corresponding operation based on the quality score of the image. Extensive experiments conducted on benchmark datasets demonstrate that the IIS-FVIQA method proposed in this paper consistently achieves top performance across multiple public datasets. After filtering out 10% of low-quality images predicted by the quality regression network, the recognition system’s performance improves by 43.96% (SDUMLA), 32.23% (MMCBNU_6000), and 21.20% (FV-USM), respectively. Furthermore, the method exhibits strong generalizability across different recognition algorithms (e.g., LBP, MC, and Inception V3) and datasets (e.g., SDUMLA, MMCBNU_6000, and FV-USM).

Deep learning techniques for hand vein biometrics: A comprehensive review

Biometric authentication has garnered significant attention as a secure and efficient method of identity verification. Among the various modalities, hand vein biometrics, including finger vein, palm vein, and dorsal hand vein recognition, offer unique advantages due to their high accuracy, low susceptibility to forgery, and non-intrusiveness. The vein patterns within the hand are highly complex and distinct for each individual, making them an ideal biometric identifier. Additionally, hand vein recognition is contactless, enhancing user convenience and hygiene compared to other modalities such as fingerprint or iris recognition. Furthermore, the veins are internally located, rendering them less susceptible to damage or alteration, thus enhancing the security and reliability of the biometric system. The combination of these factors makes hand vein biometrics a highly effective and secure method for identity verification.This review paper delves into the latest advancements in deep learning techniques applied to finger vein, palm vein, and dorsal hand vein recognition. It encompasses all essential fundamentals of hand vein biometrics, summarizes publicly available datasets, and discusses state-of-the-art metrics used for evaluating the three modes. Moreover, it provides a comprehensive overview of suggested approaches for finger, palm, dorsal, and multimodal vein techniques, offering insights into the best performance achieved, data augmentation techniques, and effective transfer learning methods, along with associated pretrained deep learning models. Additionally, the review addresses research challenges faced and outlines future directions and perspectives, encouraging researchers to enhance existing methods and propose innovative techniques.

Deep Learning-Based Biometric Finger Vein Authentication System for Enhanced Security

Biometrics, which uses human physiological characteristics, is a method for protecting personal information. Recently, finger vein authentication has become one of the most popular biometric techniques. This method offers high security and accuracy, making it a reliable form of biometric authentication. The system compares a person's vascular structure in their finger to previously collected data. Finger vein authentication works by identifying vein patterns beneath the skin's surface. The proposed system aims to enhance user authentication security by leveraging the uniqueness of finger vein patterns. The finger vein image is obtained from a database, and preprocessing is done using a Gaussian median filter in both spatial and frequency domains to remove noise. Image segmentation is performed through a line tracking method, which enhances image contrast. For feature extraction, the system utilizes Convolutional Neural Networks (CNN), and these features are matched with the stored finger vein database. A deep learning approach is then applied to classify users as genuine or imposters. In real-time, a scanner captures the finger vein image, which is sent to an Arduino board for storage and subsequently processed in MATLAB for classification. The result is transmitted through a GSM module as an alert or message, and the information is also stored in an IoT system for future reference. A GSM module is integrated with the user for communication. The proposed system achieves an accuracy of 96%, making it highly beneficial for security applications like access control, identity verification, banking, and financial transactions.

Visual Feature-Guided Diamond Convolutional Network for Finger Vein Recognition.

Finger vein (FV) biometrics have garnered considerable attention due to their inherent non-contact nature and high security, exhibiting tremendous potential in identity authentication and beyond. Nevertheless, challenges pertaining to the scarcity of training data and inconsistent image quality continue to impede the effectiveness of finger vein recognition (FVR) systems. To tackle these challenges, we introduce the visual feature-guided diamond convolutional network (dubbed 'VF-DCN'), a uniquely configured multi-scale and multi-orientation convolutional neural network. The VF-DCN showcases three pivotal innovations: Firstly, it meticulously tunes the convolutional kernels through multi-scale Log-Gabor filters. Secondly, it implements a distinctive diamond-shaped convolutional kernel architecture inspired by human visual perception. This design intelligently allocates more orientational filters to medium scales, which inherently carry richer information. In contrast, at extreme scales, the use of orientational filters is minimized to simulate the natural blurring of objects at extreme focal lengths. Thirdly, the network boasts a deliberate three-layer configuration and fully unsupervised training process, prioritizing simplicity and optimal performance. Extensive experiments are conducted on four FV databases, including MMCBNU_6000, FV_USM, HKPU, and ZSC_FV. The experimental results reveal that VF-DCN achieves remarkable improvement with equal error rates (EERs) of 0.17%, 0.19%, 2.11%, and 0.65%, respectively, and Accuracy Rates (ACC) of 100%, 99.97%, 98.92%, and 99.36%, respectively. These results indicate that, compared with some existing FVR approaches, the proposed VF-DCN not only achieves notable recognition accuracy but also shows fewer number of parameters and lower model complexity. Moreover, VF-DCN exhibits superior robustness across diverse FV databases.

Authentication System Based on Fingerprint Using a New Technique for ROI selection

Through the analysis of biological, behavioral, or a combination of both traits, biometrics entails the identification of specific persons. Finger veins, iris patterns, fingerprints, and DNA are examples of common biometric qualities. Of these, fingerprints are the most commonly utilized since they are highly distinctive, simple to obtain, and can be obtained from a variety of sources (because each person has ten fingers). In addition to introducing a method for identifying the region of interest (ROI), which is a specific area selected from the fingerprint image to improve feature extraction, this research focuses on several models for extracting fingerprint features. The study suggests a novel technique known as the flexible region of interest (FROI) for extracting a wider region of interest. Using the GLCM algorithm and invariant moments, features were retrieved from four fingerprint models (gray, binary, ridges thinning, and valleys thinning) using this FROI. The highest performance was obtained with invariant moments recovered from the valleys thinning model, according to experimental results from a verification system. This led to a false rejection rate (FRR) of 7.5% and a false acceptance rate (FAR) of 0.3846%

A cancelable multi-biometric system based on the feature-level fusion of fingerprint and finger vein

A cancelable multi-biometric system based on the feature-level fusion of fingerprint and finger vein

A sequential combination of convolution neural network and machine learning for finger vein recognition system

A sequential combination of convolution neural network and machine learning for finger vein recognition system

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.