Vein Images Research Articles

Delta Ruled Fully Recurrent Deep Learning for Finger-Vein Verification

Finger-vein verification is a significant problem to be resolved in image processing because it provides high security in many practical applications. Few research works have been designed in conventional works using different machine learning techniques. However, the verification accuracy of existing algorithms was not sufficient. Also, the amount of time required for verifying the input finger vein image was more. In order to overcome such limitations, Delta Ruled Fully Recurrent Deep Learning (DRFRDL) technique is proposed. The DRFRDL technique comprises of three main layers namely input, hidden, output layer for accurate finger-vein authentication. The input layer in DRFRDL Technique takes a number of finger vein images as input and then sent it to the hidden layer. The designed DRFRDL technique used numbers of hidden layers in order to deeply examine the input finger vein images. The result of the hidden layer is feeding back into the network along with the inputs in order to find outs the vein features that exist in a given image. Followed by, the extracted vein features at hidden layers are transmitted to the output layer. In DRFRDL technique, output layer applies Gaussian activation function that calculates the features matching score via determining the association between extracted vein features and the vein features that are already stored in the database. After estimating the matching score, the output layer returns the verification result. If the output layer result is 1, then vein features are matched and the user is considered as authorized person. Otherwise, vein features are not matched and the user is considered as unauthorized person. Thus, DRFRDL technique increases the authentication performance of finger-vein with higher accuracy and minimal time. The simulation of DRFRDL Technique is conducted using metrics such as verification accuracy, verification time and false positive rate with respect to a different number of finger-vein images. The simulation results depict that the DRFRDL Technique is able to improve the accuracy and also reduces the amount of time needed for finger-vein verification when compared to state-of-the-art works.

Development of the cost-effective, miniaturized vein imaging system with enhanced noise reduction

Development of the cost-effective, miniaturized vein imaging system with enhanced noise reduction

Non-contact Hand Vein Imaging by Use of Aerial Guiding Illumination with AIRR

Non-contact Hand Vein Imaging by Use of Aerial Guiding Illumination with AIRR

Non-contact Hand Vein Imaging by Use of Aerial Guiding Illumination with AIRR

Non-contact Hand Vein Imaging by Use of Aerial Guiding Illumination with AIRR

An Efficient Dorsal Hand Vein Recognition Security System Using Arduino And Gsm Technology

With the onset of maximum power, modest figuring and more prominent unpredictability, biometric verification has turned out to be conceivable at each scale in light of its more secure nature and furthermore easy to use conduct. Compare to other biometrics, vein biometric is a decent verification characteristic among others. The dorsal hand vein recognition is an emerging biometric procedure which is utilized for verification purposes in many applications. In this work pre-processing is done by median filter and region of interest such as veins separated from the muscles and bones through adaptive K-means clustering algorithm.The proposed method extracts the dorsal hand vein pattern features by using LBP and Repeated Line Tracking algorithm.Finally recognition and authentication is done using Artificial Neural Network. Arduino and GSM technology is used in this work to set security preference for the particular user.In order to validate the proposed work , a total of 480 images of dorsal hand veins is involved in this work. In a comparison with four existingverification algorithms, the proposed method achieves thehighest accuracy with lowest error rate.

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.

Persistent right umbilical vein in isomerism.

Our objective was to evaluate the incidence of a persistent right umbilical vein in isomerism versus situs solitus. For this retrospective, observational, nonrandomized report, we identified fetuses with confirmed right umbilical veins from all patients referred for fetal echocardiography in Southern Nevada between January 2006 and January 2019. For the period January 2006 to January 2019, we identified 89 fetuses with situs solitus and a right umbilical vein from 16 320 women undergoing prenatal cardiac evaluation, resulting in a right umbilical vein incidence of 0.5% in situs solitus. For the same period, we identified 36 fetuses with isomerism and confirmatory umbilical vein imaging. Of the 36, 15 (42%) had right umbilical veins. Of the 15, four of 11 (19%) had left isomerism, and 11 of 15 (73%) had right isomerism (P = .006). A right umbilical vein is rare with situs solitus and common with isomerism. A right umbilical vein is more common in right isomerism than left isomerism and should alert the clinician to check for isomeric situs, especially right isomerism.

A highly-verified biometric recognition system using an ultra-speed specifically-developed finger vein sensor

Currently, Biometrics has been utilized the top five modality of face, voice, IRIs, fingerprint, and palm to identify individuals. Comparatively, these Biometrics systems need complex computation to be slow and an easy target to hack. Alternatively, this work proposes a novel biometrics system of highly secured recognition with low computation time using specifically designed biometrics sensor. Consequently, finger vein recognition has been developed. Although, this recognition requires high point of safety measures comes with its individual experiments. The most prominent one being the vein pattern is very difficult to extract because finger vein images are constantly low in quality, seriously hampering the feature extraction and classification stages. Sophisticated algorithms need to be designed with the conventional hardware for capturing finger-vein images is modified by using red Surface Mounted Diode (SMD) leds. For capturing images, Canon 750D camera is used with micro lens. The integrated micro lens gives better quality images, and with some adjustments it can also capture finger print. Results have been comparatively improvement for SDUMLA-HMT database and extensively evaluated with k-nearest neighbors (KNN) algorithm. The (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. KNN calculations are highly accurate in test data. Using stratified 6- fold analysis on all fingers of all hands in collected database, a maximum accuracy of 100% was achieved with an EER of 0% when select right hand and middle finger, based on the analysis of the 106 persons present in the data set. Many approaches have been used to optimize vein image quality. The proposed system has optimum results as compared to existing related works. The work novelty is due to the hardware design of the sensor within the finger-vein recognition system to obtain, simultaneously, finger vein and finger print at low cost, unlimited users for one device and open source.

Characteristics of subcutaneous tissues at the site of insertion of peripheral infusion in patients undergoing paclitaxel and carboplatin chemotherapy.

Paclitaxel, a taxane, is frequently administered intravenously as an anticancer agent. When a peripheral intravenous catheter is used for paclitaxel infusion, clinical nurses often observe signs such as slight swelling at the catheter placement site, lack of blood return, and difficulty in continuing the infusion. However, the cause(s) of such phenomena at the puncture site has not yet been elucidated. The aim of this study was to obtain ultrasonography images of subcutaneous tissues and veins of patients undergoing paclitaxel and carboplatin chemotherapy and compare ultrasonography images taken immediately before catheter removal with those of patients receiving other types of taxanes. We studied 24 patients receiving chemotherapy, including seven receiving paclitaxel and carboplatin chemotherapy, through a peripheral intravenous catheter in a chemotherapy unit for outpatients of a university hospital in Japan. Ultrasonography images of venipuncture sites were obtained before catheter insertion and immediately before catheter removal. We observed subcutaneous edema in the absence of visible manifestations at the puncture sites of all patients undergoing paclitaxel and carboplatin chemotherapy, but not in any patients receiving other types of taxanes. When vesicant agents and vehicles have caused subclinical subcutaneous edema, clinical nurses may detect early slight extravasation by using ultrasonography.

Finger-Vein Quality Assessment Based on Deep Features From Grayscale and Binary Images

Finger-vein verification is a highly secure biometric authentication that has been widely investigated over the last years. One of its challenges, however, is the possible degradation of image quality, that results in spurious and missing vein patterns, which increases the verification error. Despite recent advances in finger-vein quality assessment, the proposed solutions are limited as they depend on human expertise and domain knowledge to extract handcrafted features for assessing quality. We have proposed, recently, the first deep neural network (DNN) framework for assessing finger-vein quality, that does not require manual labeling of high and low quality images, as is the case for state of the art methods, but infers such annotations automatically based on an objective indicator, the biometric verification decision. This framework has significantly outperformed the existing methods, whether the input image is in grayscale or is binary. Motivated by these performances, we propose, in this work, a representation learning of finger vein image quality, where a DNN takes as input conjointly the grayscale and binary versions of the input image to predict vein quality. Our model allows to learn the joint representation from grayscale and binary images, for quality assessment. The experimental results, obtained on a large public dataset, demonstrates that our proposed method accurately identifies high and low quality images, and outperforms other techniques in terms of equal error rate (EER) minimization, including our previous DNN models, based either on grayscale or binary input.

Fast Finger Vein Recognition Based on Sparse Matching Algorithm under a Multicore Platform for Real-Time Individuals Identification

Nowadays, individual identification is a problem in many private companies, but also in governmental and public order entities. Currently, there are multiple biometric methods, each with different advantages. Finger vein recognition is a modern biometric technique, which has several advantages, especially in terms of security and accuracy. However, image deformations and time efficiency are two of the major limitations of state-of-the-art contributions. In spite of affine transformations produced during the acquisition process, the geometric structure of finger vein images remains invariant. This consideration of the symmetry phenomena presented in finger vein images is exploited in the present work. We combine an image enhancement procedure, the DAISY descriptor, and an optimized Coarse-to-fine PatchMatch (CPM) algorithm under a multicore parallel platform, to develop a fast finger vein recognition method for real-time individuals identification. Our proposal provides an effective and efficient technique to obtain the displacement between finger vein images and considering it as discriminatory information. Experimental results on two well-known databases, PolyU and SDUMLA, show that our proposed approach achieves results comparable to deformation-based techniques of the state-of-the-art, finding statistical differences respect to non-deformation-based approaches. Moreover, our method highly outperforms the baseline method in time efficiency.

SIFT based Dorsal Vein Recognition System for Cashless Treatment through Medical Insurance

The time has come not to carry any physical personal identity. The person’s identity will be known by his/her biometric features for any purpose exclusively when the person is unconscious and not carrying any identity proof. Vein recognition system is leading biometric trait in terms of flexibility and security. The fake recognition of veins is almost impossible as the feature points lie underneath the skin and cannot be read without the knowledge of a person (if not unconscious). Most important thing about it is that vein recognitionsystem works only in living persons. In this paper, a recognition system is proposed that works on dorsal vein to claim cashless treatment in a hospital which is on panel of medical insurance company. For acquisition of vein image, a NIR camera VF620 of 850 nm wavelength is used. The proposed model is applied on 1000 samples of dorsal vein patterns of 250 persons. The overall performance of the system was observed to be 97.95% with EER (Equal Error Rate)0.0435%

FV-GAN: Finger Vein Representation Using Generative Adversarial Networks

In finger vein verification, the most important and challenging part is to robustly extract finger vein patterns from low-contrast infrared finger images with limited a priori knowledge. Although recent convolutional neural network (CNN)-based methods for finger vein verification have shown powerful capacity for feature representation and promising perspective in this area, they still have two critical issues to address. First, these CNN-based methods unexceptionally utilize fully connected layers, which restrict the size of finger vein images to process and increase the processing time. Second, the capacity of CNN for feature representation generally suffers from the low quality of finger vein ground-truth pattern maps for training, particularly due to outliers and vessel breaks. To address these issues, in this paper, we propose a novel approach termed FV-GAN to finger vein extraction and verification, based on generative adversarial network (GAN), as the first attempt in this area. Unlike the CNN-based methods, FV-GAN learns from the joint distribution of finger vein images and pattern maps rather than the direct mapping between them, with the aim at achieving stronger robustness against outliers and vessel breaks. Moreover, FV-GAN adopts fully convolutional networks as the basic architecture and discards fully connected layers, which relaxes the constraint on the input image size and reduces the computational expenditure for feature extraction. Furthermore, we design an adversarial training strategy and propose a hybrid loss function for FV-GAN. The experimental results on two public databases show significant improvement by FV-GAN in finger vein verification in terms of both verification accuracy and equal error rate.

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.

The Living Image in Bio-Art and in Philosophy

The central piece of Eduardo Kac’s project Natural History of the Enigma (2003–08) is a genetically modified, living artwork: a transgenic petunia (Fig. 1). The pinkish flower might first appear to be a pleasant, unthreatening, even mundane representative of its species, familiar from window boxes and suburban gardens. Only once we are informed of the flower’s provenance does it acquire an uncanny quality. The flower has been produced by introducing a gene, extracted from Kac’s blood sample, into the plant’s DNA. Kac’s gene has been modified to express itself as the blood-red, vein-like pattern on the petunia’s bloom. The genetically modified flower thereby presents us with a living image of blood-filled veins on pinkish human skin, and it is on this vision that we are invited to reflect. Consider now an image of a different sort, one conjured by a text rather than by living matter. In...

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%.

An Inconvenient Truth: The Added Value of Transvaginal Imaging of the Internal Iliac and Adnexal Veins for Pelvic Congestion Syndrome

Pelvic congestion syndrome is a chronic condition involving varicose veins in the pelvic area, resulting in dull aching pain, pelvic heaviness, palpable varicosities, and dyspareunia. The primary diagnostic modalities, computed tomography (CT) angiography and magnetic resonance (MR) angiography, can be time-consuming, expensive, and carry the risks of diagnostic radiation (CT) and intravenous contrast (CT and MR), and may not offer dynamic vascular information (CT). Recently, an increased role for ultrasound has been proposed in the diagnosis of pelvic congestion; however, the role of transvaginal (TV) duplex ultrasound has not been thoroughly investigated. Our study aims to evaluate the utility of a TV approach in the evaluation of valvular incompetence time (VIT) in seconds of the internal iliac and adnexal veins. A waiver of consent was granted by our Institutional Review Board for this retrospective review. A cohort of 36 women undergoing ultrasound evaluation for pelvic congestion syndrome was analyzed. Transvaginal and transabdominal (TA) images were obtained with reverse Trendelenburg positioning for imaging of the internal iliac veins with Valsalva maneuver to evaluate VIT, at the same visit, and the yield of each approach was compared for the presence of VIT. The number of vessels visualized, VIT, diagnostic success, and number of abnormal vessels were recorded. Between September 2018 and February 2019, 36 women underwent both TA and TV imaging. A total of 72 (100%) internal iliac veins were visualized by TV, but only 15 (21%) by TA. Of the 72 internal iliac veins imaged, 12 (17%) were shown to have valvular incompetence by TV exam, whereas only 2 (3%) were identified by TA, both of which were identified TV. Transabdominal images, when limited, were usually due to bowel gas. Transvaginal images had a higher yield for detection of internal iliac vein VIT, suggesting that the TV approach may be more sensitive and potentially more accurate versus a TA-only protocol.

Diabetic retinopathy fundus image generation based on generative adversarial networks

Objective To generate various types of diabetic retinopathy (DR) fundus images automatically by computer vision algorithm. Methods A method based on deep learning to generate fundus images was proposed, which used the vascular vein of the fundus image and the text description of lesions as the constraint conditions to generate fundus image.The text description was encoded by using a long short-term memory (LSTM), and the vascular vein image was encoded by a convolutional neural network (CNN). Then the encoded information was combined and used to generate a fundus image by generative adversarial networks (GAN). Results The results showed that the algorithm can generate realistic fundus images.However, the image detail features were not obvious because the text-encoded recurrent neural network (RNN) loss function did not converge well. Conclusions Using the GAN can generate realistic DR fundus images, which has certain application value in expanding medical data.However, the generation of detail features in small areas still needs improvement. Key words: Generative adversarial networks; Fundus images; Image generation; Convolutional neural network

Design and implementation of secure ATM system using machine learning and crypto–stego methodology

The crucial prerequisite in these days is to get rid of various forms of attacks. Nowadays, for financial transaction, automated teller machines (ATMs) are the mostly used gadgets in which personal identification numbers (PINs) are generally used for transaction. But personal identification numbers (PINs) are not secured from many types of threats (spoofing, eavesdropping, man-in-the middle attack etc.), which can affect the security of the confidential and private information. Due to this reason, different biometric systems gain popularity worldwide for their behavioral and physiological features. However, the current biometric systems, for example, iris, palm, faces fingerprints or voice are extremely complex to use and have different disadvantages. In order to overcome these disadvantages a new concept has been introduced in this paper, for authentication in ATM a finger vein authentication method and for information (finger vein image) transfer a combined approach of steganography and cryptography scheme is used. Finger vein authentication system is implemented by the combination of machine learning and image processing procedure. For the purpose of information (finger vein image) transfer, a combined approach of light-weight cryptography and steganography (variable MSB–LSB algorithm) has been proposed. For finger-vein authentication system, the experiment shows that in proposed classification procedure (one-versus-one and one-versus-all) the average recognition accuracy is 98.75% and 97.92% and the execution time is 0.168 s and 0.187 s respectively. For transferring finger vein image, light-weight cryptography and variable MSB–LSB algorithm are proposed, because light weight cryptography is superior to traditional cryptography in terms of time consumption and variable MSB–LSB algorithm is superior to simple LSB in different aspects such as randomness, security and amount of space consumed.

Learning personalized binary codes for finger vein recognition

This paper aims to develop a new local binary learning feature for finger vein images called personalized binary code (PBC). Existing local learning features of the image, such as compact binary face descriptor (CBFD) and discriminant binary descriptor (DBD), ignore the structure of the binary feature corresponding to each class when learning. Inspired by the bit-consistency phenomenon of local binary pattern (LBP), we combine the constraints of within-class sparse constraints with linear discriminant analysis (LDA) criteria in the learning process to explore the bit-consistent structure of learned binary features. Specifically, a multi-directional pixel difference vector is first calculated at each pixel of the training set of finger vein images, and an objective function is designed to learn a discriminative map that projects these multi-directional pixel difference vectors into binary features. Our objective function consists of several constraints: Minimizing quantization error, minimizing within-class variance of learned binary features, and maximizing the between-class variance of learned binary features, and minimizing the L2,1 norm of the learned features for each class. Next, the algorithm based on alternating direction method (ADM) is designed to solve the optimization problem. Finally, a histogram representation of each finger vein image is constructed by the bag-of-words model. In our experiments, the proposed PBC shows that the leading performance on two public databases in comparison with existing finger vein recognition methods.