Vein Verification Research Articles

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 Novel Finger Vein Verification Framework Based on Siamese Network and Gabor Residual Block

A Novel Finger Vein Verification Framework Based on Siamese Network and Gabor Residual Block

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.

Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet

Palm vein recognition has received some considerable attention regarding its use in biometric identification. Palm vein characteristics offer a superior level of security and reliability in personal identification compared to extrinsic methods such as fingerprint, face, and palm print recognition, as vein patterns are difficult to duplicate and do not change throughout one’s lifetime. This study proposes both segmentation and recognition methods to enhance the authentication performance and achieve correct identification using palm vein features. First, we propose a segmentation model based on the U-Net model, enhanced with an attention gate, to effectively segment palm vein patterns. The incorporation of both the attention gate and residual block allows the segmentation model for the learning of the essential features required for specific segmentation tasks. The Hessian-based Jerman filtering method is used for ground-truth labeling. The segmentation model extracts the palm vein patterns and filters out the irrelevant and noisy pixels for the purpose of recognition. The efficient channel attention residual network is trained to learn discriminative features for personal identification using combined margin-based loss functions for palm vein recognition. The channel attention module enhances the useful information and suppresses irrelevant features in the feature maps, which overcomes the problem of rotation, position translation, and scale transformation, as well as improves the recognition rate. The combined loss function used in this study increases the similarity between the intra-class samples and the diversity between inter-class samples. The proposed recognition model achieved 100% accuracy for palm vein recognition and an equal error rate of 0.018 for palm vein verification.

EIFNet: An Explicit and Implicit Feature Fusion Network for Finger Vein Verification

Finger vein recognition has received more attention in recent years due to its high security and promising development potential. However, extracting complete vein patterns and obtaining features from the original images suffer from the low contrast of finger vein images, which dramatically restrains the performance of finger vein recognition algorithms. Inspired by this motivation, we propose an explicit and implicit feature fusion Network (EIFNet) for finger vein verification. It can extract more comprehensive and discriminative features by complementarily fusing the features extracted from binary vein masks and gray original images. We design a feature fusion module (FFM) acting as a bridge between mask feature extraction module (MFEM) and contextual feature extraction module (CFEM) to achieve the optimal fusion of features. To obtain more accurate vein masks, we develop a novel finger vein pattern extraction method and provide the first finger vein segmentation dataset THUFVS.We solve the difficulty of building finger vein segmentation datasets in a simple but effective way, and develop a complete process encompassing dataset creation, data augmentation refinement and network design, which refers to the Mask Generation Module (MGM), for the deep learning based finger vein pattern extraction method. Experimental results demonstrate the superior verification performance of EIFNet on three widely used datasets compared with other existing methods.

Local Binary Convolution Based Prior Knowledge of Multi-Direction Features for Finger Vein Verification

Local Binary Convolution Based Prior Knowledge of Multi-Direction Features for Finger Vein Verification

Review Paper on an Authentication System using Siamese Convolutional Neural Networks

Due to its distinct advantages, finger vein verification has lately drawn more attention. Focusing on the characteristics of finger vein verification, construct a Siamese structure combining with a modified contrastive loss function for training the above CNN, which effectively improves the network's performance. The experimental findings demonstrate that the lightweight CNN's size shrinks to 1/6th of the pretrained-weights based CNN and that it achieves an equal error rate of 75% in the SDUMLA-HMT dataset, which outperforms cutting-edge techniques and nearly maintains the same performance as CNN that is based on pretrained weights.

LAWNet: A Lightweight Attention-Based Deep Learning Model for Wrist Vein Verification in Smartphones Using RGB Images

LAWNet: A Lightweight Attention-Based Deep Learning Model for Wrist Vein Verification in Smartphones Using RGB Images

Finger Veins Verification by Exploiting the Deep Learning Technique

Finger vein verification has recently gained the attention of many researchers as one of the most interesting biometrics. This paper proposes a deep learning model called the Deep Fingers Vein Learning (DFVL). to improve recognition accuracy by training a Convolutional Neural Network. The final model consists of the following layers: three convolutional & ReLU, pooling, fully connected, soft-max and classification. All this after the hand image goes through the basic stages of determining the region of interest by operations within the preprocessing. The effect of changing parameter values was examined, analyzed, and discussed. The best accuracy results recorded by tuning the network parameter is 81.7%. This percentage was increased to 89% after using the five-finger fusion (Correct match of three or more fingers).

Exploiting Multiperspective Driven Hierarchical Content-Aware Network for Finger Vein Verification

The finger vein trait has attracted widespread attention for personal authentication in recent years. However, most finger vein verification methods are performed on the single perspective, captured by a monocular near-infrared camera fixed at one side of the finger. Consequently, the contents of a single perspective have few details of the spatial network structure of the finger vein and show noticeable differences even if the posture of the same finger is slightly different. Both of them impact the verification performance. Hence, finger vein images captured from different viewpoints are considered in this work. We first design a low-cost multi-perspective based dorsal finger vein imaging device for data collection. A deep neural network named Hierarchical Content-Aware Network (HCAN) is then proposed to extract the discriminative hierarchical features of the finger vein. Specifically, HCAN is compound of a Global Stem Network (GSN) and a Local Perception Module (LPM). GSN aims to extract the latent global 3D feature from all perspectives through a recurrent neural network. It enables the model to retain the details in previous hidden states by incorporating a memory weighting strategy. LPM is designed to perceive each perspective from the aspect of image entropy. Guided by the entropy loss, LPM captures the prominent local feature and improves the discriminability and robustness of the hierarchical feature. The experimental results on the newly collected THU-MFV database demonstrate the superiority of the proposed method in comparison with other multi-perspective and single-perspective based methods.

Finger Vein Verification on Different Datasets Based on Deep Learning with Triplet Loss.

In this study, deep learning and triplet loss function methods are used for finger vein verification research, and the model is trained and validated between different kinds of datasets including FV-USM, HKPU, and SDUMLA-HMT datasets. This work gives the accuracy and other evaluation indexes of finger vein verification calculated for different training-validation set combinations and gives the corresponding ROC curves and AUC values. The accuracy of the best result has reached 98%, and all the ROC AUC values are above 0.98, indicating that the obtained model can identify the finger veins well. Since the experiments are cross-validated between different kinds of datasets, the model has good adaptability and applicability. From the experimental results, it is also found that the model trained on the dataset that is more difficult to be distinguished will be a better and more robust model.

A secure finger vein verification and authentication scheme for banking network

SummaryThe development of electronic exchanges has created a more important interest in fast and accurate customer IDs and verification. Structures, bank records, and PC framework often use personally identifiable access code IDs and test numbers (PINs) for reliable status. Biometric verification requires a level of security, unchanged structure, spoofing, and reliability. In this research work, a biometric authentication system was developed to provide security for access to the ATM network. Honest technology used for security purposes to provide efficient ATM network security is a finger vein. This method undergoes image processing technology such as preprocessing, feature extraction, and classification to locate the finger vein region for accessing the ATM network. Finally, the cascade neural network acts as a classifier method to differentiate registered and unregistered user. The performance analysis of this method is simulated by MATLAB and the process is evaluated to measure the efficient security of the network. The dataset is segregated into two stages; one for training and other for testing. The features extracted with the help of hidden layers with neurons. The matched finger vein is compared to check the accuracy.

New Hierarchical Finger-Vein Feature Extraction Method for iVehicles

With the advancement of multimedia and digital technology, traditional vehicles are gradually replaced by intelligent ones. As people attach increasing importance to convenience and security, traditional keys and password locks are also being replaced. Although radio frequency identification (RFID) is convenient, some researches have pointed out security concerns on its unlocking technology. In view of this, the finger-vein patterns to be used as a keyless vehicle access control system for intelligent vehicles (iVehicles) is presented. Semantic segmentation DeepLabv <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3^{+}$ </tex-math></inline-formula> based on deep learning (DL) was integrated to filter out the background noise and enhance processing stability. Also, the enhanced maximum curvature (EMC) method to extract vein features was adopted, and best matching regional scores (SMRS) and support vector machines (SVMs) were utilized for hierarchical feature extraction. Lastly, these methods were actualized on a low-level embedded platform Raspberry Pi, with which cloud computing was used to realize real-time identification. When three images were used for training and three for testing, the results showed that the proposed hierarchical vein verification technique had an equal error rate (EER) of 0.84% and 0.47% in the NIU-MIT and FV-USM datasets, respectively.

ConvNet for Finger Vein based Personal Authentication

The human brain, can easily perceive and differentiate the objects in an image. Subsequently the field of computer vision intent to mimic / simulate the human vision system. Finger vein-based user authentication has been used to control access and maintaining privacy of confidential data. The main challenges in the finger vein verification are the quality of an acquired images due to uneven illumination of light, quality of sensor, positional variation and environmental condition. In this article, we used Wiener filter, to improve the quality of finger vein images. These noise free images are provided for training to popular pretrained ConvNet architecture for user verification using finger vein biometric. Then we analysed the performance of ConvNet (convolutional neural networks) such as Alex Net, Squeeze Net, Google Net, Shuffle Net, Efficient Net, Mobile Net, Res Net, Dense Net and NASNet for the finger vein based personal authentication to secure confidential data and maintain privacy. The finger vein images from Kaggle database is used for this research work. The experiment exhibits the outstanding performance of resnet101 with the 97.64% accuracy over its peer networks.

GAN-Based Inter-Class Sample Generation for Contrastive Learning of Vein Image Representations

Biometric vein recognition using deep learning has shown promising performance recently. However, due to the lack of vein image training data, the performance is greatly limited. Although the use of traditional data augmentation can alleviate this problem to some extent, it can only expand the intra-class samples and is fundamentally limited to the intra-class space, resulting in limited performance improvement. In this paper, we tackle this deep learning data shortage problem by proposing a GAN-based framework that can generate arbitrary-pattern vein images, which can augment the training data with new vein classes. The proposed generation framework consists of three progressive synthetic steps, namely, generation of random vein patterns in the binary space, refinement of the binary vein patterns, and rendering them into grayscale vein images. Moreover, we use the synthetic dataset to pretrain the feature embedding network via unsupervised contrastive learning, which allows learning data augmentation-invariant and instance-separating representations. After that, we further fine-tune the embedding network on the real training data in a supervised manner. Our results show that highfidelity and diverse vein image samples can be generated to alleviate the problem of data shortage and improve the learning of features representations for biometric vein verification.

Trilateral Filterative Hermitian feature transformed deep perceptive fuzzy neural network for finger vein verification

Automated person recognition is an essential field to preserve individual privacy in biometrics. A conventional system performs the recognition, but inaccurate detection of finger-vein lines causes lesser verification accuracy. To address this problem, a novel Trilateral Filterative Hermitian Feature Transformation based Deep Perceptive Fuzzy Neural Network (TFHFT-DPFNN) model is introduced for the capability of learning biometric features that offers robust and accurate vein image verification with minimum time. The proposed TFHFT-DPFNN model performs four different processing stages in the network layers. In the first stage, multiple finger vein images are captured from the database. Then the input images are taken to perform the denoising in the second stage by applying a guided trilateral filter to smooth the input image for accurate matching with minimum time consumption. Followed by, Hermitian Hat wavelet transformation is applied to perform decomposition and feature extraction from the input image. Then, the extracted features are transferred to the next hidden layer where the feature matching process is carried by applying the Jaccard similarity index. Based on the similarity value, the finger vein verification is performed through the fuzzy triangular membership function at the output layer. Finally, the verified results are obtained at the output layer with minimum error. Experimental evaluation is carried out with training images and testing images on different factors such as peak signal-to-noise ratio, finger vein verification accuracy, false-positive rate, and computation time with the number of finger vein images. The proposed TFHFT-DPFNN model offers promising outcomes in terms of higher verification accuracy and lesser false-positive rate with minimum time consumption than the conventional methods.

Endowing rotation invariance for 3D finger shape and vein verification

Endowing rotation invariance for 3D finger shape and vein verification

Design of a Lightweight Palmf-Vein Authentication System Based on Model Compression

Palm-vein authentication is a secure and highly accurate vein feature authentication technology that has recently gained a lot of attention. Convolutional neural networks (CNNs) provide relatively high performance in the field of image processing, computer vision, and have been adapted for feature learning of palm-vein images. However, they often require high computation that not only are infeasible for real-time vein verification but also a challenge to apply on mobile devices. To address this limitation, we proposed a lightweight MobileNet based deep learning (DL) architecture with depthwise separable convolution (DSC) and adopt a knowledge distillation (KD) method to learn the knowledge from the more complex CNN, which makes it small but effective. Through the depth of separable convolution, the number of model parameters is significantly decreased, while still remaining high accuracy and stable performance. Experiments demonstrated that the size of the proposed model is 100 times less than the Inception_v3 model, while the performance can go beyond 98% correct identification rate (CIR) for the CASIA database.

ArcVein-Arccosine Center Loss for Finger Vein Verification

This article proposes a new loss function termed arccosine center loss, which can learn interclass and intraclass information simultaneously, to improve the discriminative ability of convolutional neural networks for finger vein verification. Specifically, the purpose of arccosine center loss is to reduce intraclass distance and increase the interclass distance through network model training. With the combination of softmax loss and arccosine center loss, the proposed network model can extract features with the interclass dispensation and intraclass compactness, which improves the discriminative ability of learned features. Experimental studies have confirmed the effectiveness and efficiency of the proposed method for finger vein verification.

Finger Vein Verification Techniques using Convolutional Neural Networks

Finger vein beneath our skin is one of the unique features for identifying an individual. Because of its uniqueness and security the finger vein recognition is considered as a powerful biometric identifier for user authentication. Several techniques have been evolved for finger vein recognition from its early stage of development, but majority approaches were based on hand crafted features which had limitations on quality of the image, positioning of the finger etc. The emergence of neural networks led to the development of various Convolutional Neural Networks (CNN) based approaches for identity verification. This paper surveys various finger vein verification techniques using CNN and determines the factors that will affect the final result. Publicly available finger vein datasets as well as user designed ones, which are of different qualities, are used for the experimental analysis of these techniques. Though CNN is used in all the cases each one differs in the number of layers used, weight updating methods, results obtained etc. It is found that higher recognition accuracy and lower equal error rate (EER) makes the finger vein verification system an effective one. This field has emerged wide popularity recently and is used in different applications where security is of prime importance.