Hand Vein Images Research Articles

Fast and Accurate ROI Extraction for Non-Contact Dorsal Hand Vein Detection in Complex Backgrounds Based on Improved U-Net.

In response to the difficulty of traditional image processing methods to quickly and accurately extract regions of interest from non-contact dorsal hand vein images in complex backgrounds, this study proposes a model based on an improved U-Net for dorsal hand keypoint detection. The residual module was added to the downsampling path of the U-Net network to solve the model degradation problem and improve the feature information extraction ability of the network; the Jensen-Shannon (JS) divergence loss function was used to supervise the final feature map distribution so that the output feature map tended to Gaussian distribution and improved the feature map multi-peak problem; and Soft-argmax is used to calculate the keypoint coordinates of the final feature map to realize end-to-end training. The experimental results showed that the accuracy of the improved U-Net network model reached 98.6%, which was 1% better than the original U-Net network model; the improved U-Net network model file was only 1.16 M, which achieved a higher accuracy than the original U-Net network model with significantly reduced model parameters. Therefore, the improved U-Net model in this study can realize dorsal hand keypoint detection (for region of interest extraction) for non-contact dorsal hand vein images and is suitable for practical deployment in low-resource platforms such as edge-embedded systems.

A novel security and authentication method for infrared medical image with discrete time chaotic systems

Objective:Hand vein images have become important biometric signs used for identification systems. Also, dorsal hand vein images have noteworthy advantages in terms of reliability and contactless procedure. Surgically changing the vascular pattern under the skin is extremely difficult. Therefore, the use of such patterns in identity recognition applications is increasing day by day. In this context, many studies have been carried out in essential areas such as image acquisition for different textures, image preprocessing, data security, image feature extraction and recognition. Methods:In this article, a new dorsal hand vein identification application has been carried out with the chaos-based security mechanism to protect personal data and the Improved SURF method to reduce the error matches of traditional SURF method in the current application. There are two improvements in the presented system. The first one finds the features representing only the vein segments, not the remaining tissue or hairly parts. The second one eliminates the mismatching points to increase the accuracy. Results:Both encryption and data hiding techniques are preferred together to protect person data. In addition, dorsal hand vein images taken with a camera for matching and recorded in the database of the people have been improved with the newly developed I-SURF feature extraction method. Thereby, with the developed application, in addition to keeping the personal identity information securely in the database with chaos-based methods, higher accuracy rate has been achieved in matching the new image taken with the camera.

An improved YOLO Nano model for dorsal hand vein detection system.

To date, venipuncture, the most necessary and fundamental medical means, still remains a challenging task for medical stuff due to significant individual differences in vein condition. Thanks to mature development in near-infrared (NIR) imaging technology, a series of venepuncture auxiliary equipment has been devised and put into use. Yet, previous researches concentrated more on vein pattern segmentation, failing to materialize the identification of veins suitable to puncture in an embedded system. Given the above, we propose an approach to detect and locate the optimal veins fully utilizing the state-of-the-art deep learning and image processing technologies in order to provide a more practical reference. Firstly, a dedicated NIR-based puncturable vein positioning system is designed, realizing collection of dorsal hand vein images as well as the rapid and accurate location of veins suitable to puncture. Secondly, considering the limitations of embedded devices on computation ability and memory, an improved network based on YOLO Nano, named YOLO Nano-Vein, is presented with architecture trimmed, output scales reduced, and an atrous spatial pyramid pooling (ASPP) added. Finally, average precision (AP) is increased from 91.68 to 93.23%, and the detection time and parameters of network are reduced by 22% and 17.5%, respectively, which validates the proposed network achieves higher accuracy with less detection time in comparison with YOLO Nano and YOLOv3, indicating stronger applicability for detection tasks on embedded devices.

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma.

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.

Improvement of confusion matrix for Hand Vein Recognition Based On Deep- Learning multi-classifier Decisions

In this paper, recognition of the hand vein patterns approach is proposed employing the Convolutional Neural Network (CNN). This approach is routinely well-learned in what way to get features from the main pattern using Region of Interest (ROI). Though, the poor quality of the hand vein image still attitudes an unlimited strain to the extension leads of its usability. Firstly, by applying the method of Generative adversarial networks (GAN) data augmentation the performance gain of adding GAN generated data exceeds that of adding more true images, and apply ROI in a hand vein image feature extraction is studied initially. Secondly, the suggested approach is tested on the data sets of hand veins to decrease the overfitting in the fully connecting layer of CNN which this model proves the most effective one. In total, 1575 hand vein images from 100 subjects are applied to authorize the proposed approach for hand vein. A high accuracy (>99.8%) and low False Rejection Rate(FRR) (<0.99%) were achieved by applying the suggested approach, when compared with the existing CNN classifiers, indicating the efficiency of the suggested approach.

Cross-device recognition of dorsal hand vein images by two-stage coarse-to-fine matching

Cross-device recognition of dorsal hand vein images by two-stage coarse-to-fine matching

Dorsal Hand Vein Recognition Using Very Deep Learning

AbstractDesigning a deep neural network for multiclass classification is a challenging task in comparison to binary classification. In this paper, a very deep learning model for dorsal hand vein recognition is presented. Deep learning models may contain hundreds of hidden layers. The increase in layers makes a model complex and it takes much time to train especially on CPU instead of GPU. The proposed model consists of network of Convolution layers and ReLU layers followed by one Global Average Pooling layer and two dense layers. To keep the model stable, batch normalization is applied on input and hidden layers and Adam optimizer is used to optimize the model. For the training and testing purpose two self‐constructed datasets are used, first having 1200 dorsal hand vein images of children of age ranging from 5 to 12 years and second having 2000 dorsal hand vein images from 100 adults. The accuracy of the proposed model is compared with two recent contributions, which also used self‐constructed datasets. The accuracy of the proposed system observed is 98.33% when trained and tested with children' dataset, and 100% when trained and tested with adults' dataset.

Human Identity Verification From Biometric Dorsal Hand Vein Images Using the DL-GAN Method

In this research, biometric authentication, which has been widely used for different purposes in the last quarter-century, is studied. Dorsal hand veins are used for biometric authentication. “Deep learning” (DL) and “generative adversarial networks” (GANs) are used together as keys in the study. A DL-GAN is obtained by combining deep learning and GAN. The developed DL-GAN method is tested on two separate databases. The adversarial network (DL-GAN) method is developed to increase the authentication process’s proportional value. For identity verification, dorsal hand veins with biometric physical properties are used. A multistep approach is used for selecting hand dorsal features, including preimage processing and effectively identifying individuals. The deep learning productive antinetwork method is used to effectively identify individuals based on the information obtained from the dorsal hand vein images. For the test in the study, two open access databases are used. These databases are the Jilin University - dorsal hand vein database and the 11K hands database. The results of the experiments performed on the dataset related to the dorsal hand vessels show that the DL-GAN method reaches an identity accuracy level of 98.36% and has an error rate of 2.47% and a standard accuracy of 0.19%. The accuracy of the experimental results in the second dataset is 96.43%, the equal error rate is 3.55% and the standard accuracy is 0.21%. The improved DL-GAN method obtains better results than physical biometric methods such as LBP, LPQ, GABOR, FGM, BGM and SIFT.

FPGA-Based Two-Dimensional Matched Filter Design for Vein Imaging Systems.

Venipuncture is a common medical procedure. The use of augmented reality-based assistive devices can improve the first puncture success rate in patients with poor vascular filling. In order to improve the image rendering quality and speed of auxiliary equipment, this study develop a two-dimensional matched filtering algorithm on a Field Programmable Gate Array (FPGA) in a near-infrared vein imaging system, which use parallel processing to offer real-time response and is designed as a small handheld portable device. A customized dorsal hand vein image library with 200 images captured from 120 participants is used to analyze the effects of convolution kernel parameters and exposure time on vascular imaging with different depths, and the correlation model between these parameters and vascular depth are constructed. We use the Tenengrad, variance, Laplace smoothness and standard deviation as evaluation indicators, and compare our algorithm with three other related studies. Experimental results show that the rendering quality of our proposed algorithm is significantly higher than other algorithms. In addition, the rendering speed of our algorithm can reach 66 fps, which is twice faster than the current fastest algorithm.

Certain investigation on feature extraction in dorsal hand vein image

Biometrics refers to people being known by their fingerprints or attributes. This research takes into account veins on the back of the hand called the dorsal vein. The primary target of this work is to build up an individual acknowledgment framework dependent available dorsal vein design with high acknowledgment rate. Attributes such as LBP ( Local Binary Pattern), HOG (Histogram of Oriented Gradients) and WLD (Weber Local Descriptor) are extracted and the technique of Chisquare, Cityblock, Euclidean and Minikowski is used for recognition speeds.The quality assessment is conducted in terms of accuracy of category. Hand vein biometrics offer higher security, simple to get the hand vein picture and extremely difficult to fashion the information contrasted with progressively settled biometric check techniques.

Intelligent technique for human authentication using hand vein

In this paper, we propose a new intelligent technique to authenticate human using dorsal hand vein (DHV) pattern. Recently, authentication was adopted by smart hospitals in many countries as an intelligent tool for patient identification to prevent insurance and can connect the patient with his or her medical record securely. In this paper we developed an image analysis technique to extract region of interest (ROI) from DHV image. After extracting ROI we design a sequence of preprocessing steps to improve hand vein images using Median filter, Wiener filter and contrast limited adaptive histogram equalisation (CLAHE) to enhance hand vein image. Our smart technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-nearest neighbours (K-NN) classifier for matching operation. This technique has been applied on the Bosphorus Hand Vein Database. The experimental results show that the result of (CRR) is 91.2%.

A Recognition System for Partially Occluded Dorsal Hand Vein Using Improved Biometric Graph Matching

Some portions of dorsal hand may be occluded due to injuries, pigmentation, or tattoos, which significantly affects the performance of dorsal hand vein recognition systems. Biometric graph matching is a common shape-based feature extraction algorithm for vein recognition. However, this method does not consider edge attributes, which can provide additional discrimination ability. We present an improved biometric graph matching method that includes edge attributes for graph registration and a matching module to extract discriminating features. Moreover, we propose a recognition system for partially occluded dorsal hand vein. A database of normal hand vein images, three databases of images with artificially occluded dorsal hand vein with occlusions in different positions and ratios, and a database of images with tattooed hands are established to verify the validity of the proposed method. The experimental results demonstrated that the equal error rates and the accuracies were 0.0202 and 98.09% ± 0.28%, respectively for the normal hand vein images, 0.0453 and 96.58% ± 0.34%, respectively for images of artificially occluded dorsal hand vein with occlusion at all positions and area ratios (0 - 20%, mean occluded area ratio = 9.3%), and 0.0343 and 97.14% ± 0.29%, respectively for the images of tattooed hands.

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.

Robust and adaptive ROI extraction for hyperspectral dorsal hand vein images

Hyperspectral dorsal hand vein image analysis for biometrics is a relatively new technology with great potential. Compared to traditional dorsal hand biometrics that use only one spectral band to capture and analyse the veins, hyperspectral imaging allows additional information to be included. Given the difficulties of processing hyperspectral dorsal hand images, including uneven illuminations, a noisy background, translation, and deformation, this study proposes a robust and adaptive region of interest (ROI) extraction algorithm. First, a novel knuckle pinky invariant point is located. Next, based on this invariant point a key line on the dorsal hand representing one side of the squared ROI is found. The remaining three sides of the ROI can then be formed. To evaluate the proposed method, both identification and verification experiments were conducted on a large hyperspectral dorsal hand vein database. The experimental results showed that the proposed method outperformed other dorsal hand ROI extraction algorithms for each hyperspectral band. Having performed well in both experiments, the groundwork has been laid to further analyse the extracted ROI in terms of feature extraction.

Recognition of Dorsal Hand Vein Based Bit Planes and Block Mutual Information.

The dorsal hand vein images captured by cross-device may have great differences in brightness, displacement, rotation angle and size. These deviations must influence greatly the results of dorsal hand vein recognition. To solve these problems, the method of dorsal hand vein recognition was put forward based on bit plane and block mutual information in this paper. Firstly, the input gray image of dorsal hand vein was converted to eight-bit planes to overcome the interference of brightness inside the higher bit planes and the interference of noise inside the lower bit planes. Secondly, the texture of each bit plane of dorsal hand vein was described by a block method and the mutual information between blocks was calculated as texture features by three kinds of modes to solve the problem of rotation and size. Finally, the experiments cross-device were carried out. One device was used to be registered, the other was used to recognize. Compared with the SIFT (Scale-invariant feature transform, SIFT) algorithm, the new algorithm can increase the recognition rate of dorsal hand vein from 86.60% to 93.33%.

Performance analysis on feature extraction using dorsal hand vein image

Personal verification has become a severe and necessary perspective of security access systems. Compared to the traditional authentication codes, the personal identification using biometric feature has attracted more attention these years because the biometric feature is hard to forget and filch and easy to use. Biometrics refers to the identification of the humans by their characteristics or traits. This paper work considers vessels at the back side of the hand called dorsal vein. The features like local binary pattern (LBP), histogram of oriented gradients (HOG) and Weber local descriptor (WLD) are extracted, and performance analysis is done in terms of K-NN classification accuracy. The 10 image of right dorsal hand vein and 10 image of left dorsal hand vein were used from 102 persons, including both male and female. There were 50 males and 52 females. The total number of images is 2040. The WLD method has recognition accuracy up to 98%, the LBP method has 96% of recognition accuracy, and compared with both, the HOG method proclaims the highest recognition accuracy up to 99%.

A Novel Encryption Method for Dorsal Hand Vein Images on a Microcomputer

In this paper, a Lorenz-like chaotic system was developed to encrypt the dorsal hand patterns on a microcomputer. First, the dorsal hand vein images were taken from the subjects via an infrared camera. These were subjected to two different processes called contrast enhancement and segmentation of vein regions. Second, the pre- and post-processed images were encrypted with a new encryption algorithm in the microcomputer environment. For the encryption process, random numbers were generated by the chaotic system. These random numbers were subjected to NIST-800-22 test which is the most widely accepted statistical test suite. The speeded up robust feature (SURF) matching algorithm was utilized in the initial condition sensitivity analysis of the encrypted images. The results of the analysis have shown that the proposed encryption algorithm can be used in identification and verification systems. The encrypted images were analyzed with histogram, correlation, entropy, pixel change rate (NPCR), initial condition sensitivity, data loss, and noise attacks which are frequently used for security analyses in the literature. In addition, the images were analyzed after noise attacks by means of peak signal-to-noise ratio (PSNR), mean square error (MSE), and the structural similarity index (SSIM) tests. It has been shown that the dorsal hand vein images can be used in identification systems safely with the help of the proposed method on microcomputers.

Research on image segmentation algorithm based on features of venous gray value

In the process of collecting hand vein images, due to the influence of image acquisition equipment, illumination and subcutaneous fat thickness, the contrast of hand vein images is relatively low. Meanwhile, vein extraction is seriously affected by image noise. To solve this problem, an algorithm of image segmentation and contrast enhancement based on features of venous gray value is proposed in this paper. Firstly, effective region of interest (ROI) is extracted and filtered through Wiener filtering. Secondly, a new image segmentation algorithm is obtained to extract vein image. The venous binary image is denoised by an 8-adjacent inner boundary tracking method and morphological processing. Finally, contrast-enhanced venous images are obtained by weight stack of the ROI and denoised images. The experiments results show that intravenous veins can be obtained perfectly by using the image segmentation algorithm based on features of venous gray value. Moreover, the high contrast venous images can be obtained.

Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach

Biometrics has emerged as a powerful technology for person authentication in various scenarios including forensic and civilian applications. Deployment of biometric solutions that use cues from multiple modalities enhances the reliability and robustness of authentication necessary to meet the increasingly stringent security requirements. However, there are two drawbacks typically associated with multimodal biometrics. Firstly, the image acquisition process in such systems is not very user-friendly, primarily due to the time and effort required to capture biometric samples belonging to multiple modalities. Secondly, the overall cost is higher as they employ multiple biometric sensors. To overcome these drawbacks, we employ a single NIR sensor-based image acquisition in the proposed approach for hand-vein recognition. From the input hand image, a palm-vein and four finger-vein subimages are extracted. These images are then enhanced by CLAHE and transformed into illumination invariant representation using center-symmetric local binary pattern (CS-LBP). Further, a hierarchical non-rigid matching technique inspired by the architecture of deep convolutional networks is employed for matching the CS-LBP features. Finally, weighted sum rule-based matching score-level fusion is performed to combine the palm-vein and the four finger-vein modalities. A set of rigorous experiments has been performed on an in-house database collected from the left and right hands of 185 subjects and the publicly available CASIA dataset. The proposed approach achieves equal error rates of 0.13% and 1.21%, and rank-1 identification rates of 100% and 100% on the in-house and CASIA datasets, respectively. Additionally, we compare the proposed approach with the state-of-the-art techniques proposed for vascular biometric recognition in the literature. The important findings are (1) the proposed approach outperforms all the existing techniques considered in this study, (2) the fusion of palm-vein and finger-vein modalities consistently leads to better performance for all the feature extraction techniques considered in this work. (3) Furthermore, our experimental results also suggest that considering the constituent palm-vein and finger-vein images instead of the entire hand-vein images achieves better performance.

MULTISPECTRAL DORSAL HAND VEIN RECOGNITION BASED ON LOCAL LINE BINARY PATTERN

Nowadays, dorsal hand vein recognition is one of the most recent multispectral biometrics technologies used for the person identification/authentication. Looking into another biometrics system, dorsal hand vein biometrics system has been popular because of the privilege: false duplicity, hygienic, static, and convenient. The most challenging phase in a biometric system is feature extraction phase. In this research, feature extraction method called Local Line Binary Pattern (LLBP) has been explored and implemented. We have used this method to our 300 dorsal hand vein images obtained from 50 persons using a low-cost infrared webcam. In recognition step, the adaptation fuzzy k-NN classifier is to evaluate the efficiency of the proposed approach is feasible and effective for dorsal hand vein recognition. The experimental result showed that LLBP method is reliable for feature extraction on dorsal hand vein recognition with a recognition accuracy up to 98%.