Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism.

FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method named Let-Net (large kernel and attention mechanism network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to capture a broader spectrum of spatial contextual information, utilizing deep convolution in conjunction with residual connections to curtail the volume of model parameters. Subsequently, an integrated attention mechanism is applied to augment information flow within the channel and spatial dimensions, effectively modeling global information for the extraction of crucial FV features. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.

FV-MViT: Mobile Vision Transformer for Finger Vein Recognition.

In addressing challenges related to high parameter counts and limited training samples for finger vein recognition, we present the FV-MViT model. It serves as a lightweight deep learning solution, emphasizing high accuracy, portable design, and low latency. The FV-MViT introduces two key components. The Mul-MV2 Block utilizes a dual-path inverted residual connection structure for multi-scale convolutions, extracting additional local features. Simultaneously, the Enhanced MobileViT Block eliminates the large-scale convolution block at the beginning of the original MobileViT Block. It converts the Transformer's self-attention into separable self-attention with linear complexity, optimizing the back end of the original MobileViT Block with depth-wise separable convolutions. This aims to extract global features and effectively reduce parameter counts and feature extraction times. Additionally, we introduce a soft target center cross-entropy loss function to enhance generalization and increase accuracy. Experimental results indicate that the FV-MViT achieves a recognition accuracy of 99.53% and 100.00% on the Shandong University (SDU) and Universiti Teknologi Malaysia (USM) datasets, with equal error rates of 0.47% and 0.02%, respectively. The model has a parameter count of 5.26 million and exhibits a latency of 10.00 milliseconds from the sample input to the recognition output. Comparison with state-of-the-art (SOTA) methods reveals competitive performance for FV-MViT.

A Degraded Finger Vein Image Recovery and Enhancement Algorithm Based on Atmospheric Scattering Theory.

With the development of biometric identification technology, finger vein identification has received more and more widespread attention for its security, efficiency, and stability. However, because of the performance of the current standard finger vein image acquisition device and the complex internal organization of the finger, the acquired images are often heavily degraded and have lost their texture characteristics. This makes the topology of the finger veins inconspicuous or even difficult to distinguish, greatly affecting the identification accuracy. Therefore, this paper proposes a finger vein image recovery and enhancement algorithm using atmospheric scattering theory. Firstly, to normalize the local over-bright and over-dark regions of finger vein images within a certain threshold, the Gamma transform method is improved in this paper to correct and measure the gray value of a given image. Then, we reconstruct the image based on atmospheric scattering theory and design a pixel mutation filter to segment the venous and non-venous contact zones. Finally, the degraded finger vein images are recovered and enhanced by global image gray value normalization. Experiments on SDUMLA-HMT and ZJ-UVM datasets show that our proposed method effectively achieves the recovery and enhancement of degraded finger vein images. The image restoration and enhancement algorithm proposed in this paper performs well in finger vein recognition using traditional methods, machine learning, and deep learning. The recognition accuracy of the processed image is improved by more than 10% compared to the original image.

A Finger Vein Liveness Detection System Based on Multi-Scale Spatial-Temporal Map and Light-ViT Model.

Prosthetic attack is a problem that must be prevented in current finger vein recognition applications. To solve this problem, a finger vein liveness detection system was established in this study. The system begins by capturing short-term static finger vein videos using uniform near-infrared lighting. Subsequently, it employs Gabor filters without a direct-current (DC) component for vein area segmentation. The vein area is then divided into blocks to compute a multi-scale spatial-temporal map (MSTmap), which facilitates the extraction of coarse liveness features. Finally, these features are trained for refinement and used to predict liveness detection results with the proposed Light Vision Transformer (Light-ViT) model, which is equipped with an enhanced Light-ViT backbone, meticulously designed by interleaving multiple MN blocks and Light-ViT blocks, ensuring improved performance in the task. This architecture effectively balances the learning of local image features, controls network parameter complexity, and substantially improves the accuracy of liveness detection. The accuracy of the Light-ViT model was verified to be 99.63% on a self-made living/prosthetic finger vein video dataset. This proposed system can also be directly applied to the finger vein recognition terminal after the model is made lightweight.

Design of Low-Complexity Convolutional Neural Network Accelerator for Finger Vein Identification System.

In the biometric field, vein identification is a vital process that is constrained by the invisibility of veins as well as other unique features. Moreover, users generally do not wish to have their personal information uploaded to the cloud, so edge computing has become popular for the sake of protecting user privacy. In this paper, we propose a low-complexity and lightweight convolutional neural network (CNN) and we design intellectual property (IP) for shortening the inference time in finger vein recognition. This neural network system can operate independently in client mode. After fetching the user's finger vein image via a near-infrared (NIR) camera mounted on an embedded system, vein features can be efficiently extracted by vein curving algorithms and user identification can be completed quickly. Better image quality and higher recognition accuracy can be obtained by combining several preprocessing techniques and the modified CNN. Experimental data were collected by the finger vein image capture equipment developed in our laboratory based on the specifications of similar products currently on the market. Extensive experiments demonstrated the practicality and robustness of the proposed finger vein identification system.

An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics.

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.

A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections.

In the field of biometric recognition, finger vein recognition has received widespread attention by virtue of its advantages, such as biopsy, which is not easy to be stolen. However, due to the limitation of acquisition conditions such as noise and illumination, as well as the limitation of computational resources, the discriminative features are not comprehensive enough when performing finger vein image feature extraction. It will lead to such a result that the accuracy of image recognition cannot meet the needs of large numbers of users and high security. Therefore, this paper proposes a novel feature extraction method called principal component local preservation projections (PCLPP). It organically combines principal component analysis (PCA) and locality preserving projections (LPP) and constructs a projection matrix that preserves both the global and local features of the image, which will meet the urgent needs of large numbers of users and high security. In this paper, we apply the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger vein database to evaluate PCLPP and add "Salt and pepper" noise to the dataset to verify the robustness of PCLPP. The experimental results show that the image recognition rate after applying PCLPP is much better than the other two methods, PCA and LPP, for feature extraction.

A Simple and Efficient Method for Finger Vein Recognition.

Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishing ability, security, and non-invasive procedure. The main idea of traditional schemes is to directly extract features from finger vein images and then compare features to find the best match. However, the features extracted from images contain much redundant data, while the features extracted from patterns are greatly influenced by image segmentation methods. To tackle these problems, this paper proposes a new finger vein recognition algorithm by generating code. The proposed method does not require an image segmentation algorithm, is simple to calculate, and has a small amount of data. Firstly, the finger vein images were divided into blocks to calculate the mean value. Then, the centrosymmetric coding was performed using the matrix generated by blocking and averaging. The obtained codewords were concatenated as the feature codewords of the image. The similarity between vein codes is measured by the ratio of minimum Hamming distance to codeword length. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed method. The results indicate that our method outperforms the state-of-the-art methods and has competitive potential in performing the matching task.

Convolutional Neural Network Approach Based on Multimodal Biometric System with Fusion of Face and Finger Vein Features.

In today's information age, how to accurately identify a person's identity and protect information security has become a hot topic of people from all walks of life. At present, a more convenient and secure solution to identity identification is undoubtedly biometric identification, but a single biometric identification cannot support increasingly complex and diversified authentication scenarios. Using multimodal biometric technology can improve the accuracy and safety of identification. This paper proposes a biometric method based on finger vein and face bimodal feature layer fusion, which uses a convolutional neural network (CNN), and the fusion occurs in the feature layer. The self-attention mechanism is used to obtain the weights of the two biometrics, and combined with the RESNET residual structure, the self-attention weight feature is cascaded with the bimodal fusion feature channel Concat. To prove the high efficiency of bimodal feature layer fusion, AlexNet and VGG-19 network models were selected in the experimental part for extracting finger vein and face image features as inputs to the feature fusion module. The extensive experiments show that the recognition accuracy of both models exceeds 98.4%, demonstrating the high efficiency of the bimodal feature fusion.

A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image.

As the second generation of biometric technology, finger vein recognition has become a research hotspot due to its advantages such as high security, and living body recognition. In recent years, the global pandemic has promoted the development of contactless identification. However, the unconstrained finger vein acquisition process will introduce more uneven illumination, finger image deformation, and some other factors that may affect the recognition, so it puts forward higher requirements for the acquisition speed, accuracy and other performance. Considering the universal, obvious, and stable characteristics of the original finger vein imaging, we proposed a new Region Of Interest (ROI) extraction method based on the characteristics of finger vein image, which contains three innovative elements: a horizontal Sobel operator with additional weights; an edge detection method based on finger contour imaging characteristics; a gradient detection operator based on large receptive field. The proposed methods were evaluated and compared with some representative methods by using four different public datasets of finger veins. The experimental results show that, compared with the existing representative methods, our proposed ROI extraction method is 1/10th of the processing time of the threshold-based methods, and it is similar to the time spent for coarse extraction in the mask-based methods. The ROI extraction results show that the proposed method has better robustness for different quality images. Moreover, the results of recognition matching experiments on different datasets indicate that our method achieves the best Equal Error Rate (EER) of 0.67% without the refinement of feature extraction parameters, and all the EERs are significantly lower than those of the representative methods.

A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features.

Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.

Restoration of Motion Blurred Image by Modified DeblurGAN for Enhancing the Accuracies of Finger-Vein Recognition.

Among many available biometrics identification methods, finger-vein recognition has an advantage that is difficult to counterfeit, as finger veins are located under the skin, and high user convenience as a non-invasive image capturing device is used for recognition. However, blurring can occur when acquiring finger-vein images, and such blur can be mainly categorized into three types. First, skin scattering blur due to light scattering in the skin layer; second, optical blur occurs due to lens focus mismatching; and third, motion blur exists due to finger movements. Blurred images generated in these kinds of blur can significantly reduce finger-vein recognition performance. Therefore, restoration of blurred finger-vein images is necessary. Most of the previous studies have addressed the restoration method of skin scattering blurred images and some of the studies have addressed the restoration method of optically blurred images. However, there has been no research on restoration methods of motion blurred finger-vein images that can occur in actual environments. To address this problem, this study proposes a new method for improving the finger-vein recognition performance by restoring motion blurred finger-vein images using a modified deblur generative adversarial network (modified DeblurGAN). Based on an experiment conducted using two open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger-vein database and Hong Kong Polytechnic University finger-image database version 1, the proposed method demonstrates outstanding performance that is better than those obtained using state-of-the-art methods.

Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network.

The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases-Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method.

Robust Finger-vein ROI Localization Based on the 3σ Criterion Dynamic Threshold Strategy.

Region of interest (ROI) localization is one of the key preprocessing technologies for a finger-vein identification system, so an effective ROI definition can improve the matching accuracy. However, due to the impact of uneven illumination, equipment noise, as well as the distortion of finger position, etc., these make accurate ROI localization a very difficult task. To address these issues, in this paper, we propose a robust finger-vein ROI localization method, which is based on the 3 σ criterion dynamic threshold strategy. The proposed method includes three main steps: First, the Kirsch edge detector is introduced to detect the horizontal-like edges in the acquired finger-vein image. Then, the obtained edge gradient image is divided into four parts: upper-left, upper-right, lower-left, and lower-right. For each part of the image, the three-level dynamic threshold, which is based on the 3 σ criterion of the normal distribution, is imposed to obtain more distinct and complete edge information. Finally, through labeling the longest connected component at the same horizontal line, two reliable finger boundaries, which represent the upper and lower boundaries, respectively, are defined, and the ROI is localized in the region between these two boundaries. Extensive experiments are carried out on four different finger-vein image datasets, including three publicly available datasets and one of our newly developed finger-vein datasets with 37,080 finger-vein samples and 1030 individuals. The experimental results indicate that our proposed method has very competitive ROI localization performance, as well as satisfactory matching results on different datasets.

Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection.

In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.

Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits.

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

Combined Fully Contactless Finger and Hand Vein Capturing Device with a Corresponding Dataset.

Vascular pattern based biometric recognition is gaining more and more attention, with a trend towards contactless acquisition. An important requirement for conducting research in vascular pattern recognition are available datasets. These datasets can be established using a suitable biometric capturing device. A sophisticated capturing device design is important for good image quality and, furthermore, at a decent recognition rate. We propose a novel contactless capturing device design, including technical details of its individual parts. Our capturing device is suitable for finger and hand vein image acquisition and is able to acquire palmar finger vein images using light transmission as well as palmar hand vein images using reflected light. An experimental evaluation using several well-established vein recognition schemes on a dataset acquired with the proposed capturing device confirms its good image quality and competitive recognition performance. This challenging dataset, which is one of the first publicly available contactless finger and hand vein datasets, is published as well.

Based Medical Systems for Patient's Authentication: Towards a New Verification Secure Framework Using CIA Standard.

In medical systems for patient's authentication, keeping biometric data secure is a general problem. Many studies have presented various ways of protecting biometric data especially finger vein biometric data. Thus, It is needs to find better ways of securing this data by applying the three principles of information security aforementioned, and creating a robust verification system with high levels of reliability, privacy and security. Moreover, it is very difficult to replace biometric information and any leakage of biometrics information leads to earnest risks for example replay attacks using the robbed biometric data. In this paper presented criticism and analysis to all attempts as revealed in the literature review and discussion the proposes a novel verification secure framework based confidentiality, integrity and availability (CIA) standard in triplex blockchain-particle swarm optimization (PSO)-advanced encryption standard (AES) techniques for medical systems patient's authentication. Three stages are performed on discussion. Firstly, proposes a new hybrid model pattern in order to increase the randomization based on radio frequency identification (RFID) and finger vein biometrics. To achieve this, proposed a new merge algorithm to combine the RFID features and finger vein features in one hybrid and random pattern. Secondly, how the propose verification secure framework are followed the CIA standard for telemedicine authentication by combination of AES encryption technique, blockchain and PSO in steganography technique based on proposed pattern model. Finally, discussed the validation and evaluation of the proposed verification secure framework.

Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor.

Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To solve such problems, multimodal biometric systems that are able to simultaneously recognize both finger-veins and fingerprints have been researched. However, because the image-acquisition positions for finger-veins and fingerprints are different, not to mention that finger-vein images must be acquired in NIR light environments and fingerprints in visible light environments, either two sensors must be used, or the size of the image acquisition device must be enlarged. Hence, there are multimodal biometrics based on finger-veins and finger shapes. However, such methods recognize individuals that are based on handcrafted features, which present certain limitations in terms of performance improvement. To solve these problems, finger-vein and finger shape multimodal biometrics using near-infrared (NIR) light camera sensor based on a deep convolutional neural network (CNN) are proposed in this research. Experimental results obtained using two types of open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) and the Hong Kong Polytechnic University Finger Image Database (version 1), revealed that the proposed method in the present study features superior performance to the conventional methods.

Real-Time Remote Health Monitoring Systems Using Body Sensor Information and Finger Vein Biometric Verification: A Multi-Layer Systematic Review.

The development of wireless body area sensor networks is imperative for modern telemedicine. However, attackers and cybercriminals are gradually becoming aware in attacking telemedicine systems, and the black market value of protected health information has the highest price nowadays. Security remains a formidable challenge to be resolved. Intelligent home environments make up one of the major application areas of pervasive computing. Security and privacy are the two most important issues in the remote monitoring and control of intelligent home environments for clients and servers in telemedicine architecture. The personal authentication approach that uses the finger vein pattern is a newly investigated biometric technique. This type of biometric has many advantages over other types (explained in detail later on) and is suitable for different human categories and ages. This study aims to establish a secure verification method for real-time monitoring systems to be used for the authentication of patients and other members who are working in telemedicine systems. The process begins with the sensor based on Tiers 1 and 2 (client side) in the telemedicine architecture and ends with patient verification in Tier 3 (server side) via finger vein biometric technology to ensure patient security on both sides. Multilayer taxonomy is conducted in this research to attain the study's goal. In the first layer, real-time remote monitoring studies based on the sensor technology used in telemedicine applications are reviewed and analysed to provide researchers a clear vision of security and privacy based on sensors in telemedicine. An extensive search is conducted to identify articles that deal with security and privacy issues, related applications are reviewed comprehensively and a coherent taxonomy of these articles is established. ScienceDirect, IEEE Xplore and Web of Science databases are checked for articles on mHealth in telemedicine based on sensors. A total of 3064 papers are collected from 2007 to 2017. The retrieved articles are filtered according to the security and privacy of telemedicine applications based on sensors. Nineteen articles are selected and classified into two categories. The first category, which accounts for 57.89% (n = 11/19), includes surveys on telemedicine articles and their applications. The second category, accounting for 42.1% (n = 8/19), includes articles on the three-tiered architecture of telemedicine. The collected studies reveal the essential need to construct another taxonomy layer and review studies on finger vein biometric verification systems. This map-matching for both taxonomies is developed for this study to go deeply into the sensor field and determine novel risks and benefits for patient security and privacy on client and server sides in telemedicine applications. In the second layer of our taxonomy, the literature on finger vein biometric verification systems is analysed and reviewed. In this layer, we obtain a final set of 65 articles classified into four categories. In the first category, 80% (n = 52/65) of the articles focus on development and design. In the second category, 12.30% (n = 8/65) includes evaluation and comparative articles. These articles are not intensively included in our literature analysis. In the third category, 4.61% (n = 3/65) includes articles about analytical studies. In the fourth category, 3.07% (n = 2/65) comprises reviews and surveys. This study aims to provide researchers with an up-to-date overview of studies that have been conducted on (user/patient) authentication to enhance the security level in telemedicine or any information system. In the current study, taxonomy is presented by explaining previous studies. Moreover, this review highlights the motivations, challenges and recommendations related to finger vein biometric verification systems and determines the gaps in this research direction (protection of finger vein templates in real time), which represent a new research direction in this area.