Vein Images Research Articles

Finger vein identification system using capsule networks with hyperparameter tuning

<p>Safety and security systems are essential for personnel who need to be protected and valuables. The security and safety system can be supported using a biometric system to identify and verify permitted users or owners. Finger vein is one type of biometric system that has high-level security. The finger vein biometrics system has two primary functions: identification and verification. Safety and security technology development is often followed by hackers' development of science and technology. Therefore, the science and technology of safety and security need to be continuously developed. The paper proposes finger vein identification using capsule networks with hyperparameter tuning. The augmentation, convolution layer parameters, and capsule layers are optimized. The experimental results show that the capsule network with hyperparameter tuning successfully identifies the finger vein images. The system achieves an accuracy of 91.25% using the Shandong University machine learning and applications-homologous multimodal traits (SDUMLA-HMT) dataset.</p>

3D imaging of aqueous veins and surrounding sclera using a dual-wavelength photoacoustic microscopy.

Understanding aqueous outflow resistance at the level of aqueous veins has been a challenge to the management of glaucoma. This study investigated resolving the anatomies of aqueous veins and the textures of surrounding sclera using photoacoustic microscopy (PAM). A dual wavelength PAM system was established and validated using imaging phantoms, porcine and human globes perfused with an optical contrast agent ex vivo. The system shows lateral resolution of 8.23 µm and 4.70 µm at 1200 nm and 532 nm, respectively, and an axial resolution of 27.6 µm. The system is able to separately distinguish the aqueous veins and the sclera with high contrast in full circumference of the porcine and human globes.

Catheter ablation of atrial fibrillation: three-dimensional transesophageal echocardiography versus magnetic resonance imaging for pre-interventioal pulmonary vein imaging (8-year follow-up)

Abstract Catheter ablation has become the first line of therapy in patients with symptomatic, recurrent, drug-refractory atrial fibrillation (AF). However, it is still challenging because of the high degree of variability of the pulmonary vein (PV) anatomy. Three-dimensional transesophageal echocardiography (TEE) is a promising new technique for cardiac imaging. Therefore, we have evaluated the usefulness of 3-D TEE for analysing the left atrial anatomy prior to an ablation procedure in comparison to magnetic resonance imaging (MRI). Methods In 240 patients, 3-D TEE and cardiac MRI were performed immediately prior to an ablation procedure (paroxysmal AF: 110 patients, persistent AF: 130 patients). The image quality provided by 3-D TEE and by cardiac MRI was compared in all patients. Two different ablation strategies were used. In patients with paroxysmal AF, the cryoablation technique was used. In the other patients, a circumferential pulmonary vein ablation was performed using a three-dimensional mapping system. Results A 3-D TEE and a cardiac MRI could be performed successfully in all patients prior to the ablation procedure. Several variations of the PV anatomy could be visualized precisely by 3-D TEE and cardiac MRI (e.g. accessory PVs, common PV ostia, varying diameter of the left atrial appendage and its distance to the left superior PV). The image quality was good in the majority of patients even if AF with rapid ventricular response was present during the examination. The image quality provided by 3-D TEE was acceptable in 230/240 patients (95.8 %). The TEE findings correlated well with the PV angiographies performed using cardiac MRI. There was a good correlation with regard to the diameter of the PV ostia assessed by these two imaging techniques. All ablation procedures could be performed successfully (mean number of completely isolated PVs: 3.9 (cryo group), 3.9 (radiofrequency catheter ablation group)). At 96-month follow-up, 64.6 % of all patients were free from an arrhythmia recurrence (cryo group: 70.0 %, Carto group: 60.0 %). There were no major complications. Conclusions AF ablation procedures can be performed safely and effectively based on prior 3-D TEE imaging. The image quality was acceptable in the vast majority of patients.

High‐Performance NIR Emission in Chromium‐Doped Garnet Phosphors Enabled by Structure and Excitation Regulation

AbstractThe burgeoning phosphor‐converted near‐infrared light‐emitting diodes (pc‐NIR LEDs) have important applications in special illumination and spectroscopy analysis. However, the development of efficient NIR‐emitting phosphors with high performance is still a challenge. In this work, a chemical unit co‐substitution strategy is proposed to realize the excitation transition regulation and successfully achieve high‐performance NIR luminescence in Ca3‐yNayMg1‐ySb2‐xAl2+yO12: xCr3+ (0 ≤ x ≤ 0.05, 0 ≤ y ≤ 1) garnet‐type solid solution phosphors. Through the excitation transition modulation from the 4A2 ground state to the 4T1(4P) and 4T1(4F) excitation state, the excitation intensity at the blue light region is largely enhanced by 25.6 times. Moreover, the NIR‐emitting efficiency and thermal stability are improved, with the optimal luminescence internal quantum efficiency of 90.6% and thermal stability of 97%@423 K, without any flux‐assisted sintering or reduction atmosphere protection. The structure regulation induced small Stokes shift, weak electron‐phonon coupling effect, and decreased non‐radiative transition are responsible for the excellent NIR‐emitting performance. Finally, the NIR pc‐LED is fabricated with photoelectric efficiencies of 18.7%@100 mA and NIR output powers of 63 mW@100 mA, presenting potential applications in nondestructive internal defect detection, veins imaging, and night vision surveillance.

Encoding Coefficient Similarity-Based Multifeature Sparse Representation for Finger Vein Recognition

Finger vein recognition is a promising biometric technology that has received significant research attention. However, most of the existing works often relied on a single feature, which failed to fully exploit the discriminative information in finger vein images, and therefore led to a limited recognition performance. To overcome this limitation, this paper proposes an encoding coefficient similarity-based multifeature sparse representation method for finger vein recognition. The proposed method not only uses multiple features to extract comprehensive information from finger vein images, but also obtains more discriminative information through constraints in the objective function. The sparsity constraint retains the key information of each feature, and the similarity constraint explores the shared information among the features. Furthermore, the proposed method is capable of fusing all kinds of features, not limited to specific ones. The optimization problem of the proposed method is efficiently solved using the alternating direction multiplier method algorithm. Experimental results on two public finger vein databases HKPU-FV and SDU-FV show that the proposed method achieves good recognition performance.

Feasibility and image quality of bright-blood and black-blood phase-sensitive inversion recovery (BOOST) sequence in clinical practice using for left atrial visualization in patients with atrial fibrillation

ObjectivesVisualizing left atrial anatomy including the pulmonary veins (PVs) is important for planning the procedure of pulmonary vein isolation with ablation in patients with atrial fibrillation (AF). The aims of our study are to investigate the feasibility of the 3D whole-heart bright-blood and black-blood phase-sensitive (BOOST) inversion recovery sequence in patients with AF scheduled for ablation or electro-cardioversion, and to analyze the correlation between image quality and heart rate and rhythm of patients.MethodsBOOST was performed for assessing PVs both with T2 preparation pre-pulse (T2prep) and magnetization transfer preparation (MTC) in 45 patients with paroxysmal or permanent AF scheduled for ablation or electro-cardioversion. Image quality analyses were performed by two independent observers. Qualitative assessment was made using the Likert scale; for quantitative analysis, signal to noise ratios (SNR) and contrast to noise ratios (CNR) were calculated for each PV. Heart rate and rhythm were analyzed based on standard 12-lead ECGs.ResultsAll MTC-BOOST acquisitions achieved diagnostic quality in the PVs, while a significant proportion of T2prep-BOOST images were not suitable for assessing PVs. SNR and CNR values of the MTC-BOOST bright-blood images were higher if patients had sinus rhythm. We found a significant or nearly significant negative correlation between heart rate and the SNR and CNR values of MTC-BOOST bright-blood images.Conclusion3D whole-heart MTC-BOOST bright-blood imaging is suitable for visualizing the PVs in patients with AF, producing diagnostic image quality in 100% of cases. However, image quality was influenced by heart rate and rhythm.Clinical relevance statementThe novel 3D whole-heart BOOST CMR sequence needs no contrast administration and is performed during free-breathing; therefore, it is easy to use for a wide range of patients and is suitable for visualizing the PVs in patients with AF.Key Points• The applicability of the novel 3D whole-heart bright-blood and black-blood phase-sensitive sequence to pulmonary vein imaging in clinical practice is unknown.• Magnetization transfer-bright-blood and black-blood phase-sensitive imaging is suitable for visualizing the pulmonary veins in patients with atrial fibrillation with excellent or good image quality.• Bright-blood and black-blood phase-sensitive cardiac magnetic resonance sequence is easy to use for a wide range of patients as it needs no contrast administration and is performed during free-breathing.

Hybrid feature extraction and LLTSA-based dimension reduction for vein pattern recognition

ABSTRACT In information and security, the personal identification of individuals becomes much more important. For improving security, several biometric recognition techniques are implemented. However, in finger vein recognition, it faces the critical problem of fake finger vein images, security and less accuracy. To conquer this problem, Hybrid Feature Extraction with Linear Local Tangent Space Alignment-based dimension reduction and Support Vector Machine classifier (HFE–LLTSA–SVM) is proposed. In this hybrid, FE is considered as the combination of histogram of oriented gradients (HOG), grey-level co-occurrence matrix (GLCM), stationary wavelet transform (SWT), and local binary pattern (LBP) for extracting the hybrid feature. LLTSA perform dimension reduction in the outputs of HFE from HOG, GLCM, and LBP. Furthermore, SVM is used for classification which gives authentication based on error-correcting code. Finally, the performance parameters were calculated and the proposed method achieved better accuracy of 99.75%, when compared with existing methods.

Standardized, Enhanced and Robust Automated Fingerprint Recognition using CNN

Abstract: In recent years, biometric authentication systems have seen rapid development in various industries, and they continue to provide high security functions for access control systems. Many types of unimodal biometric authentication systems have been developed. However, these systems can only provide a low to moderate range of security features. Therefore, a combination of two or more unimodal biometrics (multiple modalities) is required to achieve higher security capabilities. To achieve higher accuracy, this article uses generalized probabilistic learning model algorithm for processing finger vein images. Image preprocessing is done with the help of finger vein image database to prepare it for further processes of feature extraction and matching. It then uses generalized probabilistic learning model algorithm to find the feature points. algorithm is used to match the features to obtain the image information and determines the final identity according to the sparse distribution. Compared with other machine learning methods for finger vein detection, the proposed method has a higher accuracy rate.

Detection of varicose vein disease using optimized kernel Boosted ResNet-Dropped long Short term Memory

Varicose Veins (V2) are the most harmful chronic disease when blood circulation of leg veins is not working precisely. This results in serious blood circulation problems from the heart to the leg. Diagnosing the V2 problem is highly cost-effective; earlier detection is necessary for the patients to relieve pain effectively. Hence, this article brings a novel hybridized deep learning (DL) technique for efficiently detecting the presence/absence of V2. The Median Kuan Filtering (Me_KF) based pre-processing technique is introduced to filter the noises from the raw leg vein images. After pre-processing, the Extended Twofold Autoencoder (ExT_AE) based DL technique is proposed to extract leg vein features. Then, Tent Chaotic Zebra Optimizer (TCh-ZO) algorithm is proposed to eliminate redundant features and enhance accuracy performance. Finally, this study introduces the Hybridized Kernel Boosted ResNet-Dropped Long Short Term Memory (HyKBRNet-DLSTM) based DL technique to detect the presence and absence of varicose veins. The experiment is processed with 11,350 leg vein images, and measures like accuracy, precision, kappa, mean square error (MSE) and time complexity are analyzed. The experimental section proposes an accuracy of 98.5%, a precision of 98.69%, a kappa of 95.77%, an MSE of 1.73 and a time complexity of 25.36 ms.

Efficient disentangled representation learning for multi-modal finger biometrics

Most multi-modal biometric systems use multiple devices to capture different traits and directly fuse multi-modal data while ignoring correlation information between modalities. In this paper, finger skin and finger vein images are acquired from the same region of the finger and therefore have a higher correlation. To represent data efficiently, we propose a novel Finger Disentangled Representation Learning Framework (FDRL-Net) that is based on a factorization concept, which disentangles each modality into shared and private features, thereby improving complementarity for better fusion and extracting modality-invariant features for heterogeneous recognition. Besides, to capture as much finger texture as possible, we utilize three-view finger images to reconstruct full-view multi-spectral finger traits, which increases the identity information and the robustness to finger posture variation. Finally, a Boat-Trackers-based multi-task distillation method is proposed to migrate the feature representation ability to a lightweight multi-task network. Extensive experiments on six single-view multi-spectral finger datasets and two full-view multi-spectral finger datasets demonstrate the effectiveness of our approach.

Abstract 104: Assessment Of Brain Venous System In Hypertension

With an increasing recognition of the contribution of venous collagenosis to cerebral small vessel disease there is a growing interest in the imaging of brain veins. Some studies showed increased prominence of cerebral veins in animal models of hypertension (HTN) and increased venous visibility in patients with white matter lesions using susceptibility weighted imaging (SWI). Since SWI uses deoxygenated blood as an intrinsic contrast agent, reduced cerebral blood flow (CBF) could result in increased oxygen extraction and thus greater vein prominence. However, others reported that fewer veins visible on SWI correlated with more brain atrophy. We examined whether the number of deep medullary veins (DMVs) was related to HTN and CBF, in a group of cognitively healthy subjects from a brain aging research center (n=351, 220 women, mean age 69.4 ± 7.1). DMVs were assessed by visual inspection on SWI on the level of lateral ventricles, and the number was defined as the average count of both hemispheres. CBF was assessed using arterial spin labeling. The number of DMVs did not differ (p=0.17 after adjusting for age, sex and acquisition type) between normotensive subjects (n=194, <140/90, and no antihypertensive treatment), subjects with controlled HTN (n=74, <140/90, and antihypertensive treatment), and subjects with uncontrolled blood pressure (n=72, ≥ 140/90). It was, however, numerically the lowest in the uncontrolled group (22.8 ± standard error (SE) 1.1, 25.6 ± 1.1 for controlled, and 24.4 ± 0.7 for normotensive individuals). In the entire group there was a negative correlation between CBF and DMVs: r=-0.23, p<0.001. The correlation became progressively stronger across HTN spectrum: r=-0.16, p=0.03 for normotensive subjects, r=-.23, p=0.03 for the controlled group and r=-.038, p=0.001 for the uncontrolled HTN group. At the same time there was a linear trend (p=0.06 after adjusting for age and sex) for CBF decrease from 59.5 ml/100mg/min ± SE 0.4 in normotensive subjects, 58.3±0.7 in controlled and 58.0±0.7 in the uncontrolled group. A greater reliance on oxygen extraction due diminishing perfusion across HTN spectrum may obscure structural changes in cerebral veins previously reported in subjects with vascular disease.

SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images

Contactless biometric technologies such as palm vein recognition have gained more relevance in the present and immediate future due to the COVID-19 pandemic. Since certain soft biometrics like gender and age can generate variations in the visualization of palm vein patterns, these soft traits can reduce the penetration rate on large-scale databases for mass individual recognition. Due to the limited availability of public databases, few works report on the existing approaches to gender and age classification through vein pattern images. Moreover, soft biometric classification commonly faces the problem of imbalanced data class distributions, representing a limitation of the reported approaches. This paper introduces weighted extreme learning machine (W-ELM) models for gender and age classification based on palm vein images to address imbalanced data problems, improving the classification performance. The highlights of our proposal are that it avoids using a feature extraction process and can incorporate a weight matrix in optimizing the ELM model by exploiting the imbalanced nature of the data, which guarantees its application in realistic scenarios. In addition, we evaluate a new class distribution for soft biometrics on the VERA dataset and a new multi-label scheme identifying gender and age simultaneously. The experimental results demonstrate that both evaluated W-ELM models outperform previous existing approaches and a novel CNN-based method in terms of the accuracy and G-mean metrics, achieving accuracies of 98.91% and 99.53% for gender classification on VERA and PolyU, respectively. In more challenging scenarios for age and gender–age classifications on the VERA dataset, the proposed method reaches accuracies of 97.05% and 96.91%, respectively. The multi-label classification results suggest that further studies can be conducted on multi-task ELM for palm vein recognition.

Fake Biometric Detection Based on Photoplethysmography Extracted from Short Hand Videos

An array of authentication methods has emerged, underscoring the importance of addressing spoofing challenges arising from forgery and alteration. Previous studies utilizing palm biometrics have attempted to circumvent spoofing through geometric methods or the analysis of vein images. However, these approaches are inadequate when faced with hand-printed photographs or in the absence of near-infrared sensors. In this study, we propose using remote photoplethysmography (rPPG) signals to tackle spoofing concerns in palm images captured in RGB environments. rPPG signals were extracted using video durations of 3, 5, and 7 s, and 30 features within the heart rate band were identified through frequency conversion. A support vector machine (SVM) model was trained with the processed features, yielding accuracies of 97.16%, 98.4%, and 97.28% for video durations of 3, 5, and 7 s, respectively. These features underwent dimensionality reduction through a principal component analysis (PCA), and the results were compared with the initial 30 features. Additionally, we evaluated the confusion matrix with zero false-positives for each video duration, finding that the overall accuracy experienced a decline of 1 to 3%. The 5 s video retained the highest accuracy with the smallest decrement, registering a value of 97.2%.

Identification of Finger Vein Images with Deep Neural Networks

To establish identification, individuals often utilize biometrics so that their identity cannot be exploited without their consent. Collecting biometric data is getting easier. Existing smartphones and other intelligent technologies can discreetly acquire biometric information. Authentication through finger vein imaging is a biometric identification technique based on a vein pattern visible under finger's skin. Veins are safeguarded by the epidermis and cannot be duplicated. This research focuses on the consistent characteristics of veins in fingers. We collected invariant characteristics from several cutting-edge deep learning techniques before classifying them using multiclass SVM. We used publicly available image datasets of finger veins for this purpose. Several assessment criteria and a comparison of different deep learning approaches were used to characterize the performance and efficiency of these models on the SDUMLA-HMT dataset.

Identity Recognition System Based on Multi-Spectral Palm Vein Image

A multi-spectral palm vein image acquisition device based on an open environment has been designed to achieve a highly secure and user-friendly biometric recognition system. Furthermore, we conducted a study on a supervised discriminative sparse principal component analysis algorithm that preserves the neighborhood structure for palm vein recognition. The algorithm incorporates label information, sparse constraints, and local information for effective supervised learning. By employing a robust neighborhood selection technique, it extracts discriminative and interpretable principal component features from non-uniformly distributed multi-spectral palm vein images. The algorithm addresses challenges posed by light scattering, as well as issues related to rotation, translation, scale variation, and illumination changes during non-contact image acquisition, which can increase intra-class distance. Experimental tests are conducted using databases from the CASIA, Tongji University, and Hong Kong Polytechnic University, as well as a self-built multi-spectral palm vein dataset. The results demonstrate that the algorithm achieves the lowest equal error rates of 0.50%, 0.19%, 0.16%, and 0.1%, respectively, using the optimal projection parameters. Compared to other typical methods, the algorithm exhibits distinct advantages and holds practical value.

Disentangled Representation and Enhancement Network for Vein Recognition

Recently, vein recognition has been paid more considerable attention in biometric recognition fields. In the process of vein image acquisition, due to the influence of external factors such as illumination change, the texture information of vein images with the same identity information may change, which enhances the difference of intra-class and extremely degrades the performance of vein recognition systems. To address this problem, we proposed a Disentangled Representation and Enhancement Network for vein recognition called DRE-Net. First, robust vein shape masks are obtained by the designed vein segmentation algorithms, which are utilized as label information in DRE-Net to acquire the shape features of vein images. Second, a disentangled representation network which contains two encoders and two decoders is designed to disentangle the texture features and shape features of vein images. Besides, a Multi-Scale Attention Residual Block (MSARB) is presented to better mine the vein network information and enhance the representation ability of DRE-Net for vein images. Finally, a Weight-Guided Feature Enhancement Module (WGFEM) is presented to obtain more discriminative representations for vein recognition by reducing the importance of texture features and increasing the importance of shape features, which decreases the difference of intra-class caused by illumination change. Extensive experiments have been carried out on three benchmark vein databases, and the experimental results demonstrate that our proposed model outperforms the state-of-the-art methods.

Raspberry PI-Based Finger Vein Recognition System Using PCA NET

Despite simultaneously ignoring the intensity distribution that is formed by the finger tissue and, in some instances, processing it as background noise, the majority of finger vein feature extraction algorithms achieve satisfactory performance due to their ability to represent texture. This project makes use of two- directional two-dimensional Fisher Principal Component Analysis, also known as (2D) 2 FPCA, for feature extraction. This type of "noise" is presented as a novel soft biometric trait for improving finger vein recognition performance. In order to demonstrate that the intensity distribution that is formed by the finger tissue in the background can be extracted as a soft biometric trait for recognition, a comprehensive analysis of the finger vein imaging principle and the characteristics of the image are first presented.

Real-time superficial vein imaging system for observing abnormalities on vascular structures

Real-time superficial vein imaging system for observing abnormalities on vascular structures

Superficial venous morphometry in the antecubital fossa: An autonomous robotic ultrasound-based analysis.

The antecubital fossa is an important site for venepuncture and intravenous procedures. The size and location of a vein can affect the success of venepuncture and intravenous access. Several studies have investigated the superficial vein morphometry, but they had small sample sizes or focused on specific populations or groups. Therefore, we conducted a prospective study with large participants in general population to analyse the morphology of the antecubital superficial vein and identify the association of sex, age and body mass index (BMI) with the size and location of the vein. This study collected images of superficial veins prospectively using autonomous robotic ultrasound on the antecubital area between October and November 2020. We measured the superficial vein depth, vertical diameter and horizontal diameter at the antecubital area, extracted population characteristics (sex, age and BMI), and analysed a relationship between the vein dimensions and the characteristics. In this study, data from 461 participants (201 males and 260 females) with mean age of 41.1 years were produced. The mean vein depth, mean vertical diameter and mean horizontal diameter (±standard deviation) were 4.81 (±2.17), 3.01 (±1.10) and 4.46 (±1.60) mm, respectively. We found significant differences in vein dimensions between males and females, with males having larger vertical and horizontal diameters than females (p < 0.001). The study also revealed significant differences in vein depth and dimensions among age groups and BMI subgroups (p < 0.001). These findings revealed that the superficial vein in the antecubital area was oval, with a larger horizontal diameter than vertical diameter. Morphometry revealed differences in sex, age and BMI. Understanding variations in vein dimensions among different subgroups can help medical professionals improve success rate of venous access and patient safety.

Sequence-wise multimodal biometric fingerprint and finger-vein recognition network (STMFPFV-Net)

The existing multimodal biometric fingerprint and vein deep learning features were found effective for biometric recognition. However, the current performance of the deep learning features is limited due to missing temporal image dependence and the extracted features can obfuscate some vital information because of irrelevant image features. This work proposes a sequence of filtered spatial and temporal multimodal fingerprint and finger vein network (FS-STMFPFV-Net). The overall proposed FS-STMFPFV-Net is achieved from two-channel independent learning to improve image variabilities. In the first channel, the image sequence is generated by aligning the fingerprint and finger vein images together inside the image generator. The sequences are built into the five layers of a deep convolution neural network fusion model to extract spatial sequence-wise features. The second channel is where the extracted sequence-wise features are remembered in the long short-term memory for interactions between temporal and sequence dimensions which finally generate their long-term temporal dependencies as complementary information. These sequences are fused together, and the discriminative sequence features are selected using feature selection. We have presented the ReliefFS feature selection which serve as a complementary basis for selecting compact features from the extracted CNN-based features. To evaluate the performance of the proposed FS-STMFPFV-Net, the NUPT-FPV, FVC-2002-DBs, and the CASIA dataset were used, which provides fingerprint, finger-vein, and palmprint image databases, for experimental validation. The proposed FS-STMFPFV-Net when evaluated using standard protocols offers more than 97% accuracy across the different databases and is ten times more computationally friendly than the existing algorithms.