Palm Vein Recognition Research Articles

CGAN-DA: A Cross-Modality Domain Adaptation Model for Hand-Vein Biometric-based Authentication

Palm-vein recognition has been the focus of large research efforts over the last years. However, despite the effectiveness of deep learning models, in particular Convolutional Neural Networks (CNNs), in automatically learning robust feature representations, thereby obtaining good accuracy, such good performance is usually obtained at the expense of annotating a large training dataset. Labeling vein images, however, is an expensive and tedious process. Although handcrafted schemes for data augmentation usually increase slightly performance, they are unable to cover complex variations inherently characterizing such images. To overcome this issue, we propose a new unsupervised domain adaptation model, called CycleGAN-based domain adaptation (CGAN-DA), that extracts discriminant representation from the palmvein images, without requiring any image labeling. Our CGAN-DA models allows a conjoint adaptation, at the image and feature levels. Specifically, in order to enhance the extracted features’ domain-invariance, image appearance is transformed across two domains, palm-vein domain and retinal domain. We employ several adversarial losses namely a segmentation loss and a cycle consistence loss to train our model without any annotation from the target domain (palm-vein images). Our experiments on the public CASIA palm-vein dataset demonstrates that our models significantly outperforms the s tart of the art in terms of verification accuracy.

Recognizing Palm Vein in Smartphones Using RGB Images

Recently, demand for biometric access controls and online payments in smartphones increased, necessitating further investigation and development in this area. This article proposes a new low-cost palm vein recognition system for smartphones using red, green, blue (RGB) images. First, we detect and enhance palm vein patterns, using the saturation channel instead of the red channel as in the existing approaches. Then, to address the challenging contactless capturing problems of smartphones—such as scale variants, rotation, closed fingers, or rings on hand—we introduce an improved method for the region of interest extraction, based on the convex hull, with a new idea for key vector use. We also designed a new lightweight deep learning-based model for smartphones, which was overlooked in previous palm vein recognition studies. The proposed model comprises suitable blocks of convolution, depthwise separable convolution, inverted residual bottleneck, and spatial pyramid pooling module; in addition, the accuracy is enhanced with fusion strategy. Results show that the proposed model is both smaller and more accurate than related models. The integrated proposed model obtains the best equal error rate, 0.49%, and an inference time of 8 ms.

Towards the generation of synthetic images of palm vein patterns: A review

With the recent success of computer vision and deep learning, remarkable progress has been achieved on automatic personal recognition using vein biometrics. However, collecting large-scale real-world training data for palm vein recognition has turned out to be challenging, mainly due to the noise and irregular variations included at the time of acquisition. Meanwhile, existing palm vein recognition datasets are usually collected under near-infrared light, lacking detailed annotations on attributes (e.g., pose), so the influences of different attributes on vein recognition have been poorly investigated. Therefore, this paper examines the suitability of synthetic vein images generated to compensate for the urgent lack of publicly available large-scale datasets. Firstly, we present an overview of recent research progress of palm vein recognition, from the basic background knowledge to vein anatomical structure, data acquisition, public database, and quality assessment procedures. Then, we focus on the state-of-the-art methods that have allowed the generation of vascular structures for biometric purposes and the modeling of biological networks with their respective application domains. In addition, we review the existing research on the generation of style transfer and biological nature-based synthetic palm vein images algorithms. Afterward, we formalize a general flowchart for the creation of a synthetic database comparing real palm vein images and generated synthetic samples to obtain some understanding into the development of the realistic vein imaging system. Ultimately, we conclude by discussing the challenges, insights, and future perspectives in generating synthetic palm vein images for further works.

Biometric Identification System Based on Contactless Palm-Vein Using Residual Attention Network

Palm vein recognition technology is a one of the most effective biometric technologies for personal identification. Palm acquisition techniques are either contact-based or contactless-based. The contactless-based palm vein system is considered more accurate and efficient when used in modern applications, but it may suffer from problems like pose variations and the delay in the matching process. This paper proposes a contactless-based identification system for palm vein that involves two main steps; First, the central region of the palm is cropped using fast extract region of interest algorithm, then the features are extracted and classified using altered structure of Residual Attention Network, which is a developed version of convolutional neural network that uses an attention mechanism. The altered structure is constructed by stacking multiple attention modules and pre-activation residual unit with additional down sampling layers in between. The proposed system was tested on contactless CASIA multispectral palm vein databases that contains palm images with obvious pose variations taken from 100 persons. The results show that our system has outperformed other state-of-the-art systems with 95.55% accuracy and fast identification process of 0.06 second per person.

Experimental results on palmvein-based personal recognition by multi-snapshot fusion of textural features

In this paper, we investigate multiple snapshot fusion of textural features for palmvein recognition including identification and verification. Although the literature proposed several approaches for palmvein recognition, the palmvein performance is still affected by identification and verification errors. As well-known, palmveins are usually described by line-based methods which enhance the vein flow. This is claimed to be unique from person to person. However, palmvein images are also characterized by texture that can be pointed out by textural features, which relies on recent and efficient hand-crafted algorithms such as Local Binary Patterns, Local Phase Quantization, Local Tera Pattern, Local directional Pattern, and Binarized Statistical Image Features (LBP, LPQ, LTP, LDP and BSIF, respectively), among others. Finally, they can be easily managed at feature-level fusion, when more than one sample can be acquired for recognition. Therefore, multi-snapshot fusion can be adopted for exploiting these features complementarity. Our goal in this paper is to show that this is confirmed for palmvein recognition, thus allowing to achieve very high recognition rates on a well-known benchmark data set.

Experimental results on palmvein-based personal recognition by multi-snapshot fusion of textural features

In this paper, we investigate multiple snapshot fusion of textural features for palmvein recognition including identification and verification. Although the literature proposed several approaches for palmvein recognition, the palmvein performance is still affected by identification and verification errors. As well-known, palmveins are usually described by line-based methods which enhance the vein flow. This is claimed to be unique from person to person. However, palmvein images are also characterized by texture that can be pointed out by textural features, which relies on recent and efficient hand-crafted algorithms such as Local Binary Patterns, Local Phase Quantization, Local Tera Pattern, Local directional Pattern, and Binarized Statistical Image Features (LBP, LPQ, LTP, LDP and BSIF, respectively), among others. Finally, they can be easily managed at feature-level fusion, when more than one sample can be acquired for recognition. Therefore, multi-snapshot fusion can be adopted for exploiting these features complementarity. Our goal in this paper is to show that this is confirmed for palmvein recognition, thus allowing to achieve very high recognition rates on a well-known benchmark data set.

A Palm Vein Recognition Approach by Multiple Convolutional Neural Network Models

A palm vein recognition system is proposed in this paper. The efficiency of three convolutional neural network models (VGG16, VGG19 and AlexNet) in palm vein biometrics is compared and then this study proposes to fuse them with Decision-Level Fusion. These models employ the use of high number of filters during training which leads to very high computation time, therefore, the filters are reduced in this study to drastically reduce computation time while maintaining the efficiency of the models. The proposed method is tested on three datasets secured from FYO, PUT and VERA databases. The proposed system significantly increases the accuracy of the system in comparison with the individual models and achieves 99.06 %, 99.83 % and 99.26 % on FYO, PUT and VERA datasets, respectively.

A multimodal architecture using Adapt‐HKFCT segmentation and feature‐based chaos integrated deep neural networks (Chaos‐DNN‐SPOA) for contactless biometricpalm vein recognition system

In the recent past, the fusion of various unimodal biometrics has gained increasing attention from researchers dedicated to the use of practical biometrics. In this paper, a Chaos Integrated Deep Neural Networks (Chaos-DNN) using Sandpiper Optimization Algorithm (SPOA) is proposed to enhance the performance of the multimodal contactless biometric pattern recognition system. The most significant advantage of palm vein and palm dorsal vein recognition is that it provides relatively high accuracy, reliability and also liveness detection. In this recognition process, the palm and its dorsal vein images are segmented using multilevel segmentation of region of interest (ROI) depending on Adaptive Hyper Kernel Optimized Fuzzy Clustering Technique (Adapt-HKFCT), and then ROI extracted based on the valley points among the index and ring finger using the Knuckle points. Then, the features of the ROI amid the index and ring finger are extracted using centre-symmetric local binary pattern algorithm and fed to the Chaos-DNN for training and classification. The efficiency of the features is optimized using SPOA. The extracted feature points help in authenticating a person using his/her chosen biometric traits for security purposes. The objective of this system is to increase the efficiency of the biometric system, and the performance is calculated depending on equal error rate, correct recognition rate, decidability index, false acceptance rate and false rejection rate. The proposed algorithm has been implemented in MATLAB platform. The experimental outcomes demonstrate that the proposed palm vein recognition depending Adapt-HKFCT with Chaos-DNN using SPOA method (PVR-AHKFCT-CDNN-SPOA) attains higher performance metrics with an accuracy of 98.3% and is also more efficient likened to the other existing methods, such as K Nearest Neighbour with Particle Swarm Optimization (PVR-KNN-PSO), Fuzzy Brain Storm Optimization based on Adaptive Thresholding (PVR-FBSO-AT), multiscale local binary pattern with ant colony optimization (PVR-MSLBP-ACO) and Deep Learning with Bayesian Optimization (PVR-DL-BO).

Palm vein recognition based on convolution neural network

This paper presents a new validation method using a convolutional neural network for palm vein recognition. Unlike fingerprint and face. Vein patterns are endogenous biometric features that do not change over time and that make them difficult to identify and replicate in people. The proposed paper aims to provide a new way to identify people through their veins. This paper used the CASIA dataset, which consists of several wavelengths, in this research used the 850nm wavelength, which is clear in the veins, In addition, we divided the data into 3 cases. The first case is when the training and testing ratio is 50/50, the second case when it is 70/30, and the last case when it is 90/10. Obtained an accuracy of 98% in the case 90/10. In addition, to the proposed network, and used a well-known global network, the AlexNet network, where did the same work on it to compare the results of our proposed network with it. As proposed network outperformed it in terms of accuracy and speed, where the accuracy was 96% in the case 90/10.

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.

Multispectral Palm Print and Palm Vein Acquisition Platform and Recognition Method Based on Convolutional Neural Network

Abstract Biometrics recognition takes advantage of feature extraction and pattern recognition to analyze the physical and behavioral characteristics of biological individuals to achieve the purpose of individual identification. As a typical biometric technology, palm print and palm vein have the characteristics of high recognition rate, stable features, easy location and good image quality, which have attracted the attention of researchers. This paper designs and develops a multispectral palm print and palm vein acquisition platform, which can quickly acquire palm spectrum and palm vein multispectral images with seven different wavelengths. We propose a multispectral palm print palmar vein recognition framework, and feature-level image fusion is performed after extracting features of palm print palmar vein images at different wavelengths. Through the multispectral palm print palm vein image fusion experiment, a more feasible multispectral palm print and palm vein image fusion scheme is proposed. Based on the results of image fusion, we further propose an improved convolutional neural network (CNN) for model training to achieve identity recognition based on multispectral palm print palm vein images. Finally, the effects of different CNN network structures and learning rates on the recognition results were analyzed and compared experimentally.

Palm Vein Recognition Based on NPE and KELM

Palm veins have become a research hotspot of biometric recognition due to their own advantages of universality, uniqueness, collectability and stability. This paper proposes a palm vein recognition algorithm based on Neighborhood Preserving Embedding (NPE) and Kernel Extreme Learning Machine (KELM). The algorithm firstly performs gray-scale normalization processing on vein images, then extracts neighborhood preserving embedding dimensionality reduction features, and finally uses extreme learning machine for classification and recognition. The method is tested on the multispectral palmprint database of Hong Kong Polytechnic University. The experimental results show that this method can effectively reduce the vein dimensions to less than 30, and achieve an ideal recognition effect, if the parameters are selected appropriately. The algorithm is also verified on the palmvein database of Tongji University and FYO palmvein database for verifying the robustness, and also get ideal experimental results.

Outside Box and Contactless Palm Vein Recognition Based on a Wavelet Denoising ResNet

Palm vein recognition is a high-security biometric. Outside the NIR capture box and contactless palm vein recognition are more popular but challenging. The users feel comfortable outside the NIR capture box but face more optical blurring brought by visible light. Contactless capture gestures solve the hygienic problem but face the image rotation, position translation, and scale transformation which makes classification difficult especially in large-scale databases. To address these problems, we develop a wavelet denoising ResNet, which consists of two models: the wavelet denoising (WD) model and the squeeze-and-excitation ResNet18 (SER) model. The WD model focuses on removing noise from skin scattering and optical blurring from palm vein images. The WD model enhances the low-frequency feature into a deep learning feature by residual learning technology. This strategy increases the weight of an effective handcrafted feature in the deep learning network. The SER model overcomes rotation, position translation, and scale transformation by selectively emphasizing classification features and weakening less useful features. To train and verify the network, an inside box palm vein image database and an outside box palm vein image database are set up. The Tongji contactless palm vein image database was also employed in the experiments. The validity and superiority of our network are verified in a series of experiments.

Palmvein Recognition Using Block-Based WLD Histogram of Gabor Feature Maps and Deep Neural Network With Bayesian Optimization

The demand on devices and systems empowered by biometric verification and identification mechanisms has been increasing in recent years as they have become a significant part of our lives. Palm vein biometric is an emerging technology that has drawn considerable attention from researchers and scientists over the last decade. In the present work, a novel feature extraction methodology named GPWLD combing the Gabor features with positional Weber's local descriptor (PWLD) features is proposed. WLD is a highly representative micro-pattern descriptor that performs well against noise and illumination changes in images. However, it lacks the ability to capture the vein pattern features at different orientations. Moreover, its descriptor packs the local information content into a single histogram that does not take the spatial locality of micro-patterns into consideration. To solve these two issues, firstly, the palm vein image is passed through Gabor filter with different orientations to capture the salient rotational features found in the output feature maps. Secondly, the spatiality is achieved by uniformly dividing each feature map into several blocks. Next, Weber's law feature descriptor (WLD) histogram is computed for each block in every feature map. Finally, these histograms are concatenated to compose the final feature vector. Due to the high dimensionality of the final feature vector, Principal component analysis (PCA) algorithm is utilized for feature size reduction. In the classification stage, a deep neural network (DNN) comprising an optimized stacked autoencoder (SAE) with Bayesian optimization and a softmax layer is used. Optimization of the SAE is carried out by using Bayesian optimization to find the optimal SAE structure and the options of the training algorithm. The Experimental results verify the discriminative power of the extracted features and the accuracy of the proposed DNN. For both Identification and verification experiments, the proposed method attains higher identification rate and lower EER than state-of-the-art methods.

Contactless Multispectral Palm-Vein Recognition With Lightweight Convolutional Neural Network

The development of information technology has made it possible to replace traditional keys and passwords with biometric recognition. Among the various human recognition technologies, contactless palm-vein authentication is becoming increasingly popular because it is hygienic and safe. In the field of deep learning (DL), system security and multispectral compatibility are crucial issues that require outright solutions. One of the most widely investigated DL algorithms is the convolutional neural network (CNN), which has been proven to have strong feature extraction capability. However, the training of CNN requires large samples and thus entails a heavy computational load, resulting in high hardware and software costs. Therefore, this paper proposes an adaptive Gabor filter with enhanced imaging features and triplet loss function that captures sufficient palm-vein data. A multispectral palm database from the CASIA public database was employed in this study to analyze the proposed system. The experimental results show that the proposed method has a low recognition error rate of 0.0556% and uses only a few network parameters in a multispectral environment.

Multi-Scale and Multi-Direction GAN for CNN-Based Single Palm-Vein Identification

Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples. These solutions, therefore, still lack the capability to extract robust and discriminative hand-vein features from a single training image sample. To overcome this problem, we propose a single-sample-per-person (SSPP) palm-vein identification approach, where only a single sample per class is enrolled in the gallery set for training. Our approach, named MSMDGAN + CNN, consists of a multi-scale and multi-direction generative adversarial network (MSMDGAN) for data augmentation and a convolutional neural network (CNN) for palm-vein identification. First, a novel data augmentation approach, MSMDGAN, is developed to learn the internal distribution of patches in a single image. The proposed MSMDGAN consists of multiple fully convolutional GANs, each of which is responsible for learning the patch distribution within an image at a different scale and at a different direction. Second, given the resulting augmented data by MSMDGAN, we design a CNN for single sample palm-vein recognition. The experimental results on two public hand-vein databases demonstrate that MSMDGAN is able to generate realistic and diverse samples, which, in turn, improves the stability of the CNN. In terms of accuracy, MSMDGAN + CNN outperforms other representative approaches and achieves state-of-the-art recognition results.

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

Unsupervised Palm Vein Image Segmentation

In this article the new hybrid algorithm for palm vein image segmentation using convolutional neural network and principal curvatures is proposed. After palm vein image preprocessing vein structure is detected using unsupervised learning approach based on W-Net architecture, that ties together into a single autoencoder two fully convolutional neural network architectures, each simi-lar to the U-Net. Then segmentation results are improved using principal cur-vatures technique. Some vein points with highest maximum principal curva-ture values are selected, and the other vein points are found by moving from starting points along the direction of minimum principal curvature. To obtain the final vein image segmentation the result of intersection of the principal curvatures-based and neural network-based segmentations is taken. The evaluation of the proposed unsupervised image segmentation method based on palm vein recognition results using multilobe differential filters is given. Test results using CASIA multi-spectral palmprint image database show the effectiveness of the proposed segmentation approach.

Human Identification Using Contrast Enhanced Segmented Palm-Vein Images

This paper presents new approaches in biometrics is palm vein recognition system. By coding the alignment protective features by having a potential deformation, translational and rotational change. A palm vein authentication done by image analysis technique of vascular image of a person. Here we use four different algorithm to process the Near-Infrared Image of palm vein image. The algorithm are 1)juncture point algorithm 2)hand geometry algorithm 3) pose invariant algorithm 4) rank of the matrix. The output is kept in database as a each person as unique vascular pattern, the authentication is done by comparing Database pattern and plam vein pattern of a person to be authenticated.

An extensive survey on finger and palm vein recognition system

The evolution of the internet drastically increases the utilization of online data, which needs security using unique identification. The traditional methods of security, such as password, personal identification number (PIN), could not reach the user-friendly requirement of the users. There is a necessity to provide high security for the data by using a unique identification system. Today, in many situations, biometrics plays a crucial role in today’s authentication and recognition. Biometrics deals with unique physical and behavioral characteristics. Fingerprint, face recognition, iris/retinal recognition, voice recognition and vein recognition are some of the biometrics which is used for authentication purpose. In this paper, we highlight different processes that involve finger veins and palm veins recognition and authentication. These finger and palm vein is identified as one of the important unique identifications that can be used for the authentication which provide the security for the important personal data. In this study, we noticed that the advantage of using a vein for the authentication process is that it provides high security because it can be detected even when a finger or palm got injured, and it works only when the person is alive otherwise the authentication fails.