Finger Vein Recognition System 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.

An optimized deep learning based depthwise separable MobileNetV3 approach for automatic finger vein recognition system

An optimized deep learning based depthwise separable MobileNetV3 approach for automatic finger vein recognition system

A secure finger vein recognition system using WS-progressive GAN and C4 classifier

A secure finger vein recognition system using WS-progressive GAN and C4 classifier

Improved Lightweight Convolutional Neural Network for Finger Vein Recognition System.

Computer vision (CV) technology and convolutional neural networks (CNNs) demonstrate superior feature extraction capabilities in the field of bioengineering. However, during the capturing process of finger-vein images, translation can cause a decline in the accuracy rate of the model, making it challenging to apply CNNs to real-time and highly accurate finger-vein recognition in various real-world environments. Moreover, despite CNNs' high accuracy, CNNs require many parameters, and existing research has confirmed their lack of shift-invariant features. Based on these considerations, this study introduces an improved lightweight convolutional neural network (ILCNN) for finger vein recognition. The proposed model incorporates a diverse branch block (DBB), adaptive polyphase sampling (APS), and coordinate attention mechanism (CoAM) with the aim of improving the model's performance in accurately identifying finger vein features. To evaluate the effectiveness of the model in finger vein recognition, we employed the finger-vein by university sains malaysia (FV-USM) and PLUSVein dorsal-palmar finger-vein (PLUSVein-FV3) public database for analysis and comparative evaluation with recent research methodologies. The experimental results indicate that the finger vein recognition model proposed in this study achieves an impressive recognition accuracy rate of 99.82% and 95.90% on the FV-USM and PLUSVein-FV3 public databases, respectively, while utilizing just 1.23 million parameters. Moreover, compared to the finger vein recognition approaches proposed in previous studies, the ILCNN introduced in this work demonstrated superior performance.

Finger Vein Recognition Technology: Principles, Applications, and Future Prospects

Finger vein recognition technology is a biometric identification technique that analyzes the vein patterns in the fingers to authenticate individuals. This paper aims to provide a comprehensive overview of the principles, applications, and future prospects of finger vein recognition technology. Firstly, the unique biological characteristics and advantages of finger vein recognition will be explored. Secondly, the working principle and key techniques of the finger vein recognition system will be introduced, covering image acquisition, feature extraction, and matching algorithms. Subsequently, the extensive applications of finger vein recognition technology in personal identity authentication, financial payments, information security, and other domains will be discussed. Finally, the future development of finger vein recognition technology will be forecasted, addressing potential challenges and solutions, as well as its potential applications in broader domains.

Residual Gabor convolutional network and FV-Mix exponential level data augmentation strategy for finger vein recognition

Using deep learning to improve the performance of finger vein recognition has become a part of mainstream research. Currently, the performance of finger vein recognition systems is limited by insufficient finger vein training samples, which leads to insufficient feature learning and weak model generalization. To solve the above problems, first, we propose a simple and effective finger vein data augmentation strategy named FV-Mix, which uses fine finger vein image region of interest (ROI) for grayscale normalization and linear mixing. It can accomplish exponential-level data augmentation on the training sample, and the augmented data will represent a more complete dataset. Second, a residual Gabor convolutional network (RGCN) is designed for finger vein recognition, which includes a residual Gabor convolutional layer (RGCL) and a dense semantic analysis module (DSAM). The RGCL is designed to replace the shallow convolutional layer in the network by using the characteristics of the Gabor filter to enhance the scale and direction of information in the shallow pattern features. And then, the enhanced deep features are further extracted and analyzed by DSAM, which is used to assist the final model in classifying and recognizing finger vein images. To verify the effectiveness of our work, five publicly available finger vein image datasets are used, and a large number of comparison experiments are designed. The proposed FV-Mix strategy, RGCL module, and DSAM module were fully validated. The experimental results show that on these five datasets, the average recognition accuracy and equal error rate (EER) of the proposed RGCN are 99.22% and 0.188%, respectively, achieves competitive performance compared with current state-of-the-art work.

SIFT-Flow-Based Virtual Sample Generation for Single-Sample Finger Vein Recognition

Finger vein recognition is considered to be a very promising biometric identification technology due to its excellent recognition performance. However, in the real world, the finger vein recognition system inevitably suffers from the single-sample problem: that is, only one sample is registered per class. In this case, the performance of many classical finger vein recognition algorithms will decline or fail because they cannot learn enough intra-class variations. To solve this problem, in this paper, we propose a SIFT-flow-based virtual sample generation (SVSG) method. Specifically, first, on the generic set with multiple registered samples per class, the displacement matrix of each class is obtained using the scale-invariant feature transform flow (SIFT-flow) algorithm. Then, the key displacements of each displacement matrix are extracted to form a variation matrix. After removing noise displacements and redundant displacements, the final global variation matrix is obtained. On the single sample set, multiple virtual samples are generated for the single sample according to the global variation matrix. Experimental results on the public database show that this method can effectively improve the performance of single-sample finger vein recognition.

Spoof detection based on score fusion using ensemble networks robust against adversarial attacks of fake finger-vein images

Finger-vein recognition is a biometric technology, which makes use of the pattern of veins in the skin of fingers. Finger-vein recognition is widely employed owing to its high recognition rate and ease of use. A spoof detection method is essential for identifying spoof attacks in biometric-recognition systems. Studies have primarily investigated methods for detecting spoof attacks that utilize image printing or replay using fake finger-vein images. However, no studies have investigated the detection of spoof attacks based on fake finger-vein images generated through a generative adversarial network (GAN), which is commonly implemented in deep-learning technologies. To solve this problem, we newly propose a spoof attack detection method robust against GAN-based fake finger-vein image generation. We verified the use of spoof finger-vein images generated through cycle-consistent adversarial networks (CycleGAN) to attack conventional finger-vein recognition systems and used this finding as the motivation for our research. We also propose a finger-vein spoof-detection method that employs score fusion of ensemble networks based on support vector machine using adaptive moment estimation (Adam) with a sharpness-aware minimization (SAM) optimizer. We conducted experiments using two open databases, ISPR and Idiap, to evaluate the proposed framework. The average classification error rates (ACER) of spoof detection using the proposed method with ISPR and Idiap were 0.37% and 0.23%, respectively. This result confirms the better performance by our method than conventional state-of-the-art methods.

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.

Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition

The finger vein recognition system uses blood vessels inside the finger of an individual for identity verification. The public is in favor of a finger vein recognition system over conventional passwords or ID cards as the biometric technology is harder to forge, misplace, and share. In this study, the histogram of oriented gradients (HOG) features, which are robust against changes in illumination and position, are extracted from the finger vein for personal recognition. To further increase the amount of information that can be used for recognition, different instances of the finger vein, ranging from the index, middle, and ring finger are combined to form a multi-instance finger vein representation. This fusion approach is preferred since it can be performed without requiring additional sensors or feature extractors. To combine different instances of finger vein effectively, score level fusion is adopted to allow greater compatibility among the wide range of matches. Towards this end, two methods are proposed: Bayesian optimized support vector machine (SVM) score fusion (BSSF) and Bayesian optimized SVM based fusion (BSBF). The fusion results are incrementally improved by optimizing the hyperparameters of the HOG feature, SVM matcher, and the weighted sum of score level fusion using the Bayesian optimization approach. This is considered a kind of knowledge-based approach that takes into account the previous optimization attempts or trials to determine the next optimization trial, making it an efficient optimizer. By using stratified cross-validation in the training process, the proposed method is able to achieve the lowest EER of 0.48% and 0.22% for the SDUMLA-HMT dataset and UTFVP dataset, respectively.

Finger-vein presentation attack detection using depthwise separable convolution neural network

Biometrics is a powerful tool for identifying and authenticating persons based on their unique characteristics. Finger vein (FV) seems to be an emerging biometric of all types of hand-based biometrics, which have garnered considerable interest because of the extensive information and ease of implementation. As the FV system has grown in popularity, there have been numerous attempts to compromise it. Recent research reveals that the finger-vein recognition (FVR) system is vulnerable to presentation attacks, in which the sensory device accepts a fake printed FV image and grants access as if it were a genuine attempt. Few deep learning (DL) studies were developed in the past to detect spoof attacks in FV images. Existing works rely on performance improvement by neglecting the problems of the limited dataset, high computational complexity, and unavailability of lightweight and efficient feature descriptors. Therefore, we have proposed a novel depthwise separable convolution neural network (DSC) with residual connection and a linear support vector machine (LSVM) for the automatic detection of FV presentation attacks. At first, we apply the data augmentation method to raw images to increase the size of datasets. Afterward, this DSC technique is applied to extract robust features from FV images. Finally, a LSVM classifier is used to classify the images into bonafide and fake images. The proposed DSC-LSVM method is evaluated on two publicly available datasets such as Idiap and SCUT-FVD. The experimental results show that the DSC-SVM model attained a low error rate of 0.00% for presentation attack detection (PAD) on both datasets compared to state-of-the-art approaches. Several experimental results show that the DSC-LSVM model required less computation time and fewer parameters to detect spoof finger vein images. It concludes that the DSC-LSVM system is outperformed for PAD on finger vein images compared to existing methods.

Study on Reflection-Based Imaging Finger Vein Recognition

Finger vein modality plays an important role in biometrics due to its stability and security. However, existing state-of-the-art finger vein recognition systems adopt the transmission-based imaging mode with a sealed design, which requires redundant space in the imaging device and results in an uncomfortable user experience. Consequently, we design a reflection-based imaging device with an open structure to reduce the device volume and improve the portability, as well as the user experience. However, an open structure of the device inevitably introduces extra illumination variation to the image, which may deteriorate the performance of the system. In this paper, we propose Domain Adaptation Finger Vein Network (DAFVN) to narrow the domain shift between different illumination data domains and extract illumination-invariant features from finger vein images, improving the robustness to illumination variations. To evaluate the performance of DAFVN and remedy the lack of a publicly open reflection-based finger vein database, we use the self-made device to construct the first large-scale reflection-based finger vein database, namely SCUT Reflective Imaging Finger Vein database (SCUT-RIFV). It includes 32,064 images from 167 subjects with five different illumination conditions. Abundant experiments implemented on the SCUT-RIFV database indicate that the proposed method can effectively alleviate the influence of illumination variation on the reflection-based finger vein recognition system.

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.

Finger Vein Recognition System Based on Convolutional Neural Network and Android

With the rapid development of science and technology, biotechnology has developed rapidly. Among the many biometric technologies, finger vein technology has the characteristics of vitality, portability, and non-replicability, so it is considered to be the most promising biometric technology. However, the accuracy of finger vein recognition is affected by the collection device, the surrounding temperature and the algorithm. The flaws cannot be applied to real life on a large scale. This paper designs a finger vein recognition system based on convolutional neural network and Android, which mainly includes the following three parts. First, the system hardware includes the design of the acquisition device, the selection of the core development board and the display screen. Second, the design of the entire system software architecture is based on the MVVM architecture, which ensures low coupling of the program and is easy for later expansion and maintenance. The software includes collection function, recognition function and administrator function. Finally, a lightweight neural network is proposed for finger vein feature extraction, and proposed a storage method based on MMKV to meet the real-time performance of the system.

Recent advancements in finger vein recognition technology: Methodology, challenges and opportunities

Finger vein recognition biometric trait is a significant biometric modality that is considered more secure, reliable, and emerging. This article presents a review to focus on the recent research landscape in biometric finger vein recognition systems. This article focuses on manuscripts related to keywords ‘Finger Vein Authentication System’, ‘Anti-spoofing or Presentation Attack Detection’, ‘Multimodal Biometric Finger Vein Authentication’ and their variations in four main digital research libraries such as IEEE Xplore, Springer, ACM, and Science Direct. The final set of articles is divided into three main categories: Deep Learning (DL) based finger vein recognition, Presentation Attack Detection (PAD), and Multimodal-based finger vein authentication system. Deep learning-based finger vein recognition techniques are further sub-divided into pre-processing (Quality assessment and enhancement) based, feature extraction based, and feature extraction and recognition based schemes. Presentation attack detection methods are sub-divided into software-based and hardware-based approaches. Multimodal-based finger vein biometric system is sub-categorized into feature level fusion, matching level fusion, and hybrid fusion methods. The authors have studied the problem of the recent algorithm and their solution related to finger vein biometric system from the recent literature. Performance analysis and selected the best promising research work from the mentioned studies are also presented. Finally, open challenges, opportunities, and suggested solutions related to deep learning, PAD, and Multimodal based finger vein recognition systems have been discussed in the discussion section. This work would be helpful to the developers, company users, researchers, and readers to get insight into the importance of such technology and the recent problem faced by finger vein authentication technology with respect to deep learning and multimodal systems.

Template Protection Based on Chaotic Map for Finger Vein Recognition

AbstractWhile the widespread application of biometric identification technology has provided convenient services, the security, and privacy of personal data have also attracted increasing attention. Because biometric traits are unique, once an attack occurs, the information stored in the system will be permanently leaked. Thus, ensuring the security of biometric templates is a major challenge. In order to solve this problem, we propose a finger vein template protection method based on chaotic map. First, we randomize and scramble the finger vein features after feature extraction, disturbing the location distribution of the pixels. Second, on the basis of the chaotic features of the finger veins, binary quantization is applied to further ensure the safety of the template. Finally, the compact and safe finger vein template is directly stored in the database. Finger vein matching is realized in the encryption domain, which changes the way that the traditional finger vein recognition system directly stores biometric templates. We conducted a series of detailed experiments on the public dataset. The experimental results show that our method can ensure the safety of finger vein templates while maintaining stable recognition performance. Moreover, the method has universality for different feature‐extraction methods and meets the requirements of good biometric template protection. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Finger vein recognition system with template protection based on convolutional neural network

The finger vein features have received extensive attention due to their high safety and stability characteristics, and are gradually being used in various fields. However, studies have shown that finger vein recognition systems based on convolutional neural networks are at huge security risks. Criminals can obtain part of the user information of the system through certain illegal operations on the recognition results or the system database stored in the recognition system, which puts the original vein data at huge security risks. Once compromised, the user information stored in the system will be permanently leaked, and the individual’s vein features will no longer be safe. In order to solve these problems, we propose a new finger vein image encryption scheme, which applies Rivest–Shamir–Adleman encryption technology to finger vein image encryption. In addition, a complete cancelable finger vein recognition system with template protection is proposed to ensure the security of the user’s vein template while maintaining the recognition performance. A set of rigorous comparative experiments and security analyses have been performed on four open databases. The experimental results show that the performance of our proposed encryption scheme and the entire system for the finger veins recognition and protection is reliable and efficient.

A fully rotation invariant multi‐camera finger vein recognition system

Finger vein recognition systems utilize the venous pattern within the fingers to recognize subjects. It has been shown that the alignment of the acquired samples has a major impact on the recognition accuracy of such systems. Although a lot of work has been done in this field, there is still no approach that solves all kind of finger misplacements. In particular, longitudinal finger rotation still causes major problems. As the capturing devices evolve towards contactless acquisition, solutions to alignment problems become more important. As an alternative to rotation detection and correction, the problem can also be addressed by acquiring the vein pattern from different perspectives. This article presents a novel multi-camera finger vein recognition system that captures the vein pattern from multiple perspectives during enrolment and recognition. Contrary to existing multi-camera solutions that use the same capturing device for enrolment and recognition, the capturing devices for the proposed system differ in the configuration of the acquired perspectives. The cameras of the devices are positioned so that the recognition rates around the finger are high and that the number of cameras needed is kept to a minimum. The experimental results confirm the rotation invariance of the proposed approach.

Feature extraction of finger-vein patterns based on boosting evolutionary algorithm and its application for loT identity and access management

With billions of devices being connected, how to make sure that our information stays secure is becoming a hot topic of IoT. Traditional approaches to personal authentication are inadequate and ineffective in the IoT era. Finger vein technology is the current biometric system that utilizes the vein structure for recognition. As such patterns are veiled under the skin surface, they have significant privacy protection and are therefore incredibly difficult to forge. Finger vein recognition has gained a great deal of publicity because earlier approaches experienced significant pitfalls, such as its inability to handle the imbalanced collection of finger veins samples and the detection of distinguishing features in low-quality images. Such disadvantages have triggered a lack of consistency of the optimization algorithm or have contributed to a decrease in its efficiency. The key objective of the research discussed in this paper is to examine the impact of the genetic algorithm in the selection of the optimum vector characteristics of the finger vein. This is done by incorporating a Niching model in the form of a Context-Based Clearing (CBC) procedure to increase the heterogeneity of the features within the features’ vector, with the goal of minimizing the association between them. It also offers the idea of a reduction of the feature set to reduce duplication without reducing accuracy. The performance study of the proposed model is carried out through multiple tests and the findings indicate an overall increase of 6% in the accuracy relative to some of the state-of-the-art finger vein recognition systems present in the literature.

Robust Finger Vein Recognition Based on Deep CNN with Spatial Attention and Bias Field Correction

As one of the biometric information based authentication technologies, finger vein recognition has received increasing attention due to its safety and convenience. However, it is still a challenging task to design an efficient and robust finger vein recognition system because of the low quality of the finger vein images, lack of sufficient number of training samples with image-level annotated information and no pixel-level finger vein texture labels in the public available finger vein databases. In this paper, we propose a novel CNN-based finger vein recognition approach with bias field correction, spatial attention mechanism and a multistage transfer learning strategy to cope with the difficulties mentioned above. In the proposed method, the bias field correction module is to remove the unbalanced bias field of the original images by using a two-dimensional polynomial fitting algorithm, the spatial attention module is to enhance the informative vein texture regions while suppressing the other less informative regions, and the multistage transfer learning strategy is to solve the problem caused by insufficient training for CNN-based model due to lack of labeled training samples in the public finger vein databases. Moreover, several measures, including a label smoothing scheme and data augmentation, are exploited to improve the performance of the proposed method. Extensive experiments have been conducted in the work on three public databases, and the results show that the proposed approach outperforms the existing state-of-the-art methods.