Vein Verification Research Articles

Finger Vein Verification Algorithm Based on Fully Convolutional Neural Network and Conditional Random Field

Owing to the complexity of finger vein patterns in shape and spatial dependence, the existing methods suffer from an inability to obtain accurate and stable finger vein features. This paper, so as to compensate this defect, proposes an end-to-end model to extract vein textures through integrating the fully convolutional neural network (FCN) with conditional random field (CRF). Firstly, to reduce missing pixels during ROI extraction, the method of sliding window summation is employed to filter and adjusted with self-built tools. In addition, the traditional baselines are endowed with different weights to automatically assign labels. Secondly, the deformable convolution network, through replacing the plain counterparts in the standard U-Net mode, can capture the complex venous structural features by adaptively adjusting the receptive fields according to veins’ scales and shapes. Moreover, the above features can be further mined and accumulated by combining the recurrent neural network (RNN) and the residual network (ResNet). With the steps mentioned above, the fully convolutional neural network is constructed. Finally, the CRF with Gaussian pairwise potential conducts mean-field approximate inference as the RNN, and then is embedded as a part of the FCN, so that the model can fully integrate CRF with FCNs, which provides the possibility to involve the usual back-propagation algorithm in training the whole deep network end-to-end. The proposed models in this paper were tested on three public finger vein datasets SDUMLA, MMCBNU and HKPU with experimental results to certify their superior performance on finger-vein verification tasks compared with other equivalent models including U-Net.

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.

Study of a Full-View 3D Finger Vein Verification Technique

Finger vein modality has unique advantages, allowing it to play an important role in biometrics. However, the approach to vein imaging and information acquisition typically adopted in current vein verification systems employs a monocular camera to acquire a single-view 2D vein image from only one side of the finger, which causes two problems: it acquires limited vein pattern information for verification, and it causes clear differences among samples of the same subject captured from different finger positions in contact-free mode. Both of these problems have adverse effects on system performance. In general, existing systems are more sensitive to positional variations of the finger, particularly those caused by pitch and roll movements. This concern remains a challenge despite considerable efforts to address it in recent years. To provide a fundamental solution to the above issues, we propose an entirely new system, which includes a software and hardware platform that collects a full-view of the vein pattern information from whole fingers with three cameras, a novel 3D reconstruction method to build the full-view 3D finger vein image, and a corresponding 3D finger vein feature extraction and matching strategy based on a lightweight convolutional neural network (CNN) with depthwise separable convolution. Experimental results demonstrate the potential of our proposed system and show that compared to the traditional single-view 2D mode of finger vein recognition, the new system both efficiently improves the recognition performance and simultaneously takes full advantage of additional valid information provided by the finger vein biometrics.

Finger vein verification using a Siamese CNN

Finger vein verification has received more attention recently due to its unique advantages. However, most existing algorithms rely on handcrafted features, making them less robust to finger rotation and offsets. To alleviate these problems, the authors propose a novel method to extract more discriminative features from finger vein images. First, facing the issue of insufficient training data, they adopt a heavy image augmentation strategy and develop a pretrained-weights based convolutional neural network (CNN). Second, focusing on the characteristics of finger vein verification, they construct a Siamese structure combining with a modified contrastive loss function for training the above CNN, which effectively improves the network's performance. Finally, considering the feasibility of deploying the above CNN on embedded devices, they construct a lightweight CNN with depthwise separable convolution and adopt a knowledge distillation method to learn the knowledge from the pretrained-weights based CNN, which makes it small but effective. The experimental results show that the size of the lightweight CNN shrinks to 1/6th of the pretrained-weights based CNN, while its equal error rates achieved in the MMCBNU_6000, FV-USM and SDUMLA-HMT datasets are 0.08, 0.11 and 0.75% respectively, which nearly stays the same with the pretrained-weights based CNN and surpasses state-of-the-art methods.

An Iterative Deep Neural Network for Hand-Vein Verification

Hand-vein biometrics as a high-security pattern has received more and more attention. One of the open issues in hand-vein verification is the lack of robustness against image quality degradation, which may comprise the verification accuracy. To achieve robust verification, vein feature extraction approaches, especially vein texture segmentation, have been extensively investigated. In recent years, deep neural networks have achieved promising results in medical image segmentation and have been brought into vein verification, but current solutions suffer from two challenges for vein segmentation: 1) lacking the labeling data, which is expensive to obtain and 2) the incorrect label data obtained by manual labeling scheme or automatic labeling scheme may strongly influence parameters when the network is trained, which may degrade the verification performance. This paper proposes an iterative deep belief network (DBN) to extract vein features based on the initial label data, which are automatically generated using a very limited a priori knowledge and iteratively corrected by our DBN. First, a known handcrafted vein image segmentation technique is employed to automatically label vein pixel and background pixel. A training dataset is constructed based on the patches centered on the labeled pixels. Second, a DBN is trained on the resulting database to predict the probability of each pixel to belong to be a vein pixel given a patch centered on it. The vein patterns are segmented using a probability threshold of 0.5. The resulting vein features are employed to reconstruct the training dataset, based on which the network is retrained. During the iterative procedure, the incorrect labels of training data are statistically corrected, which enables DBN to effectively learn what a finger-vein pattern is by learning the difference between vein patterns and background ones. The experimental results on two public hand-vein databases show a significant improvement in terms of hand-vein verification accuracy.

Palm Vein Verification System based on Nonsubsampled Contourlet Transform

This document presents a new approach in verification system to verify the identity of person by his intrinsic characteristics “Palm vein” which is unique, universal and easy to captured. The first step in this system is to extract the region of interest (ROI) which represent the most informative region of palm, then coding step based on nonsubsampled contourlet transform (NSCT) is presented to produce a binary vector for each ROI, next a matching step of different representative vectors is given and finally a decision is made for both identification and verification mode. This approach is tested on CASIA multispectral database; the experimentations have proved the effectiveness of this coding system in verification modes to gives 0.19% of Equal Error Rate (EER).

Multimodal Authentication of Ocular Biometric and Finger Vein Verification in Smartphones: A Review

Biometric authentication has demanded a lot of attention from the researchers in the current age, as the field aimsto identify human behavioral charcteristics based on fingerprint, finger vein, ocular, face, palm, etc. So, this field is useful in many applications for offering security and authentication of industry or business. Also, the multimodal biometric system is used to provide a greater security and higher reliability that combines two or more biometric identifiers. Finger vein and ocular-based multimodal biometric authentication system are one of the major techniques which have been considered for efficient identification and verification purpose. This system mainly works in some common stages which include, scanning of finger vein and ocular, pre-processing, feature extraction and matching of finger vein and ocular in a database as well. This paper attempts to review various recent and advanced multimodal finger vein and ocular biometric authentication systems. Finally, possible directions in the multimodal biometric authentication system for the future work are also discussed.

A Real Time of an Automatic Finger Vein Recognition System

Finger vein recognition biometric system is one of the most current and accurate biometric technologies. Yet, early implementation of this technology is not widely used in real-time applications. In this work, a finger vein-embedded system based on Rasberry-Pi has been presented. In our process, we use four structural directional elements for smoothing finger veins ROIs. A Top-Hat and Bottom-Hat kernel filters are used to enhance the contrast quality of images. For feature extraction step, we used two approaches for the synthesis of attributes including the geometric and texture representations of venous prints. The first one is a Local Directional Code (LDC) descriptor that characterizes texture and directional information of finger vein print. The Improved Gaussian Matched Filter (IMPGMF) is used to extract the finger vein map that characterised geometric venous information. The proposed vision system presents an Error Equal Rate (EER) lower to 0.02 and Identification Rate (IR) higher to 98.99. Moreover, experimental results show that the designed system is fast enough to run the decision of finger vein verification. Performance results show the efficiency and robustness of our system.

Challenges of Finger Vein Verification System: A Theoretical Perspective

Challenges of Finger Vein Verification System: A Theoretical Perspective

A novel finger vein verification system based on two-stream convolutional network learning

Abstract Convolutional neural networks have been proven to have strong feature representation ability; however, they often require large training samples and high computation that are infeasible for real-time finger vein verification. To address this limitation, we propose a lightweight deep-learning framework for finger vein verification. First, we designed a lightweight two-channel network that has only three convolution layers for finger vein verification. Then, we extracted the mini-ROI from the original image to better solve the displacement problem based on the evaluation of the two-channel network. Finally, we present a two-stream network to integrate the original image and the mini-ROI that achieves results superior to the current state of the art on both the MMCBNU and SDUMLA databases.

Finger Vein Verification System based on Three Methodologies of Feature Extraction

Finger Vein Verification System based on Three Methodologies of Feature Extraction

Enhanced maximum curvature descriptors for finger vein verification

Maximum Curvature Method (MCM) is one of the promising methods for finger vein verification. MCM scans the curvature of the vein image profiles within a finger for feature extraction. However, the quality of the image can be poor due to variations in illumination and sensor conditions. Furthermore, traditional MCM matching of the vein pattern requires extensive processing time. To address these limitations, we propose an integrated Enhanced Maximum Curvature (EMC) method with Histogram of Oriented Gradient (HOG) descriptor for finger vein verification. Unlike MCM, EMC incorporates an enhancement mechanism to extract small vein delineation that is hardly visible in the extracted vein patterns. Next, HOG is applied instead of image binarization to convert a two-dimensional vein image into a one-dimensional feature vector for efficient matching. The HOG descriptor is able to characterize the local spatial representation of a finger vein by capturing the gradient information effectively. The proposed method is evaluated based on two datasets namely the PKU Finger Vein Database (V4) and SDUMLA-HMT finger vein database. Experiments show promising verification results with equal error rates as low as 0.33 % for DB1 and 0.14 % for DB2 respectively, when EMC+HOG+SVM is applied.

Finger Vein Verification with Vein Textons

Finger vein pattern has become one of the most promising biometric identifiers. In this paper, a robust method based on Bag-of-Words (BoW) is developed for finger vein verification. Firstly, some robust and discriminative visual words are learned from local base features such as Local Binary Pattern (LBP), Mean Curvature and Webber Local Descriptor (WLD). We name these visual words as Finger Vein Textons (FVTs). Secondly, each image is mapped into a FVTs matrix. Finally, spatial pyramid matching (SPM) method is applied to maintain spatial layout information by representing each image as pyramid histogram which is performed for matching by histogram intersection function. Experimental results show that the proposed method achieves satisfactory performance both on our database and the open PolyU database. In addition, our method also has strong robustness and high accuracy on the self-built rotation and illumination databases.

Hardware Implementation of Palm Vein Biometric Modality for Access Control in Multilayered Security System

Among the biometric modalities palm veins are the most secure and difficult to duplicate. This palm vein verification system aims to recognize a person from its exclusive palm vein organization that cannot be forged easily since veins are situated in inner layers of skin. Embedded devices are gaining increased attention in biometrics due to reliability and cost efficient systems. An embedded palm vein recognition system is the need of today in institutes, industries, security places etc. The aim of this proposed work is to implement palm vein identification system on hardware unit so that it can be further build into a single standalone unit, where it can be used in final level security in multilayered security system without any possibility of hacking. The hardware platform used in the proposed work is Blackfin ADSP-561 processor and the algorithms used for matching of palm vein are performed using C language. The project focuses on storing images and implementing the matching algorithms on hardware platform itself such that PC or laptop is not needed for identification purpose. Principal component analysis (PCA) and template matching techniques are used as verification algorithms of palm veins. Finally, it can be concluded from the experimental results that this approach can verify an individual with an average accuracy of 92%.

Hand vein authentication using biometric graph matching

This study proposes an automatic dorsal hand vein verification system using a novel algorithm called biometric graph matching (BGM). The dorsal hand vein image is segmented using the K-means technique and the region of interest is extracted based on the morphological analysis operators and normalised using adaptive histogram equalisation. Veins are extracted using a maximum curvature algorithm. The locations and vascular connections between crossovers, bifurcations and terminations in a hand vein pattern define a hand vein graph. The matching performance of BGM for hand vein graphs is tested with two cost functions and compared with the matching performance of two standard point patterns matching algorithms, iterative closest point (ICP) and modified Hausdorff distance. Experiments are conducted on two public databases captured using far infrared and near infrared (NIR) cameras. BGM's matching performance is competitive with state-of-the-art algorithms on the databases despite using small and concise templates. For both databases, BGM performed at least as well as ICP. For the small sized graphs from the NIR database, BGM significantly outperformed point pattern matching. The size of the common subgraph of a pair of graphs is the most significant discriminating measure between genuine and imposter comparisons.

Palm Vein Verification Using Multiple Features and Isometric Projection

Biometric authentication has been widely studied for many years and attracted much attention due to its large ability security application. Palm vein is more immovable and more difficult to fake than other biometrics such as fingerprint, palm print and face. Since palm veins exist inside of the body, it is exceedingly hard to be forged. Palm vein authentication uses the unique patterns of the palm vein to identify individuals at a high level of accuracy. In the proposed work, the palm vein image enhancement algorithm proposed based on Gaussian matched filtering and then two types of feature extraction are extracted. The global features based on wavelet coefficients and locale feature based on local binary pattern (LBP). In the propose work, a linear dimensionality reduction algorithm, called Isometric Projection is used. Finally, the Manhattan Distance (MHD) matching method is proposed to verify the test palm vein images. The experimental result shows the EER to the proposed method is 0.17488%.

Palm Vein Verification Using Multiple Features and Locality Preserving Projections

Biometrics is defined as identifying people by their physiological characteristic, such as iris pattern, fingerprint, and face, or by some aspects of their behavior, such as voice, signature, and gesture. Considerable attention has been drawn on these issues during the last several decades. And many biometric systems for commercial applications have been successfully developed. Recently, the vein pattern biometric becomes increasingly attractive for its uniqueness, stability, and noninvasiveness. A vein pattern is the physical distribution structure of the blood vessels underneath a person's skin. The palm vein pattern is very ganglion and it shows a huge number of vessels. The attitude of the palm vein vessels stays in the same location for the whole life and its pattern is definitely unique. In our work, the matching filter method is proposed for the palm vein image enhancement. New palm vein features extraction methods, global feature extracted based on wavelet coefficients and locality preserving projections (WLPP), and local feature based on local binary pattern variance and locality preserving projections (LBPV_LPP) have been proposed. Finally, the nearest neighbour matching method has been proposed that verified the test palm vein images. The experimental result shows that the EER to the proposed method is 0.1378%.

Finger Vein Verification Based on Neighbor Pattern Coding

Finger Vein Verification Based on Neighbor Pattern Coding

Finger vein verification system based on sparse representation

Finger vein verification is a promising biometric pattern for personal identification in terms of security and convenience. The recognition performance of this technology heavily relies on the quality of finger vein images and on the recognition algorithm. To achieve efficient recognition performance, a special finger vein imaging device is developed, and a finger vein recognition method based on sparse representation is proposed. The motivation for the proposed method is that finger vein images exhibit a sparse property. In the proposed system, the regions of interest (ROIs) in the finger vein images are segmented and enhanced. Sparse representation and sparsity preserving projection on ROIs are performed to obtain the features. Finally, the features are measured for recognition. An equal error rate of 0.017% was achieved based on the finger vein image database, which contains images that were captured by using the near-IR imaging device that was developed in this study. The experimental results demonstrate that the proposed method is faster and more robust than previous methods.