People Recognition by RGB and NIR Analysis from Digital Image Database Using Cross-Correlation and Wavelets

This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).

Biometric Finger Vein Recognition Using Evolutionary Algorithm with Deep Learning

In recent years,the demand for biometric-based human recog-nition methods has drastically increased to meet the privacy and security requirements.Palm prints,palm veins,finger veins,fingerprints,hand veins and other anatomic and behavioral features are utilized in the development of different biometric recognition techniques.Amongst the available biometric recognition techniques,Finger Vein Recognition(FVR)is a general technique that analyzes the patterns of finger veins to authenticate the individuals.Deep Learning(DL)-based techniques have gained immense attention in the recent years,since it accomplishes excellent outcomes in various challenging domains such as computer vision,speech detection and Natural Language Processing(NLP).This technique is a natural fit to overcome the ever-increasing biomet-ric detection problems and cell phone authentication issues in airport security techniques.The current study presents an Automated Biometric Finger Vein Recognition using Evolutionary Algorithm with Deep Learning(ABFVR-EADL)model.The presented ABFVR-EADL model aims to accomplish bio-metric recognition using the patterns of the finger veins.Initially,the presented ABFVR-EADL model employs the histogram equalization technique to pre-process the input images.For feature extraction,the Salp Swarm Algorithm(SSA)with Densely-connected Networks(DenseNet-201)model is exploited,showing the proposed method’s novelty.Finally,the Deep-Stacked Denoising Autoencoder(DSAE)is utilized for biometric recognition.The proposed ABFVR-EADL method was experimentally validated using the benchmark databases,and the outcomes confirmed the productive performance of the proposed ABFVR-EADL model over other DL models.

Unconstrained Hand Dorsal Veins Image Database and Recognition System

Hand veins can be used effectively in biometric recognition since they are internal organs that,in contrast to fingerprints,are robust under external environment effects such as dirt and paper cuts.Moreover,they form a complex rich shape that is unique,even in identical twins,and allows a high degree of freedom.However,most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality.Since the start of the COVID-19 pandemic,most handbased biometric systems have become undesirable due to their possible impact on the spread of the pandemic.Consequently,new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness.One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle.This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years.For the other research contribution,a novel geometrical feature extraction method has been developed based on the Curvelet Transform.This method is useful for extracting robust rotation invariance features from vein images.The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.

Extraction of intersecting palm-vein and palmprint features for cancellable identity verification

A novel method based on the cross-modality intersecting features of the palm-vein and the palmprint is proposed for identity verification.Capitalising on the unique geometrical relationship between the two biometric modalities,the cross-modality intersecting points provides a stable set of features for identity verification.To facilitate flexibility in template changes,a template transformation is proposed.While maintaining non-invertibility,the template transformation allows transformation sizes beyond that offered by the con-ventional means.Extensive experiments using three public palm databases are conducted to verify the effectiveness the proposed system for identity recognition.

基于PCA-LSR双约束的多光谱掌脉图像识别方法

为实现高安全性、用户接受度好的生物特征识别系统,设计了一种开放环境下基于多光谱的掌脉图像采集设备,并研究了一种基于主成分分析(Principal Component Analysis,PCA)和最小二乘回归(Least Squares Regression,LSR)双约束的掌脉识别算法。算法在最小二乘回归投影的过程中对主成分提取的主元信息进行约束,共同驱动数据,削弱了光散射对识别性能的不良影响,解决了非接触式图像采集造成的类内间距增大的问题。在中科院自动化所、同济大学、香港理工大学以及自建的掌脉图库上进行了实验,算法最低等误率分别为0.72%、0.50%、0.18%和0.03%,正确识别率分别为99.80%、99.77%、99.90%、99.95%。相比其他典型方法具有优势,系统具有实用价值。

轻量级Transformer的双向交互近红外手指静脉图像识别

针对现有手指静脉识别算法速度慢、复杂度高以及Transformer架构在小数据集上效果不佳的问题,提出轻量级Transformer的双向交互识别方法 .利用轻量级卷积神经网络与改进的Transformer架构组成并行主干网络,用于近红外手指静脉图像的局部和全局特征提取;设计交互结构,在并行结构的基础上,以交互方式融合两条分支上不同尺度的特征.为最大程度地保留近红外图像的局部特征和全局表示,将两条分支提取的信息拼接融合,通过输出层得出识别结果 .结果表明,该算法在多个数据集上的最高识别率可达99.77%,参数量仅1.33 MB.相较于其他指静脉算法,以及改进的Transformer架构,在保持高准确率的同时进一步降低了算法的复杂度.

基于手指静脉识别的艺术实验教学智慧监管系统

当前艺术实验教学中缺乏兼顾安全监管和智慧决策的信息化管理手段。在主流的信息安全技术中,手指静脉识别技术具有活体识别、准确率高、使用方便等优势,适应艺术实验教学监管应用场景。该文提出结合软特征的手指静脉识别算法,进一步提升了识别性能。在此基础上,研究实现艺术实验教学智能监管系统,在实现实验教学监管的同时辅助管理决策,有效解决了监管中普遍存在的问题,全面提升了监管水平。

一种基于改进多任务学习的手背静脉识别方法与系统

针对当前手背静脉识别产品较为缺乏的问题,为了促进深度学习技术在手背静脉识别领域的落地应用,提出一种基于改进多任务学习的手背静脉识别方法,同时开发一套基于软硬件协同的手背静脉识别系统。来自采集手背静脉数据集上的实验结果显示,改进算法在嵌入式设备上能够取得99.59%的准确率、0.437%的等误率、小于1 s的识别时间,足以满足大多数常见应用场景对识别性能的需求,为手背静脉识别方法的落地应用提供了一种有效的解决方案。

采用轻量化神经网络的高安全手指静脉识别系统

针对特殊材料能伪造手指静脉从而欺骗识别系统,以及利用卷积神经网络进行手指静脉识别计算量大的问题,设计了具有活体检测功能和轻量化卷积神经网络结构的手指静脉识别系统。采用光容积法检测手指脉搏波的变化,从而判断被采集对象是否为活体;利用剪枝及通道恢复方法改进了ResNet-18卷积神经网络,并结合L_(1)正则化增加卷积神经网络的特征选择能力,在提升算法准确率的基础上,能有效地降低计算资源的消耗。实验表明,使用改进的剪枝及通道恢复优化结构,参数量降低了75.6%,计算量降低了25.6%,在山东大学和香港理工大学手指静脉数据库上得到的等误率分别为0.025%、0.085%,远低于ResNet-18得到的等误率(0.117%、0.213%)。

基于静脉关键特征和AdaFace损失的轻量级指静脉识别算法

基于深度学习的指静脉识别方法通常需要大量的计算资源,限制了其在嵌入设备上的推广和普及,采用轻量级网络又面临模型参数减少导致准确率下降的问题,为此提出一种基于指静脉关键特征和AdaFace损失的轻量级识别算法。在MicroNet框架中,首先提出一种FMixconv卷积来替代原网络中的深度卷积,减少参数的同时可以获得静脉特征的多尺度信息;其次引入轻量级注意力模块CA模块,从空间和通道上聚焦于静脉特征的关键信息;最后在损失函数中加入AdaFace损失,通过特征范数对图像质量进行评价,以减少图像质量下降对训练的影响。该算法在SDUMLA-HMT、FV-USM和自建数据集上的识别准确率达到99.84%、99.39%和99.42%,而参数量仅有0.82 M。实验结果表明,该算法在准确率和参数量大小上均领先于其他方法。

掌静脉识别的深度学习方法综述

掌静脉识别作为一种新兴的红外生物识别技术,因其高安全性、活体检测性等优势已成为当前生物特征识别领域中的研究热点之一。近年来,该领域的大量研究通过引入深度学习方法推动了掌静脉识别技术的发展。为了掌握掌静脉识别领域最新研究现状及发展方向,对数据采集和数据预处理的主流算法进行了分类和总结,并针对基于深度学习的掌静脉识别的最新进展按照掌脉特征表征、网络设计与优化、轻量级网络进行了分类和详细阐述。针对当前单模态识别达到瓶颈等问题,分析并对比了多模态和多特征融合识别相关算法;探讨了当前掌静脉识别的研究难点挑战,并对未来的发展趋势进行了展望与总结。

基于改进MobileNet的指静脉识别算法

指静脉处于手指皮肤里层不易改变,利用指静脉进行身份识别与验证已经成为生物识别领域的一个研究热点。基于CNN的指静脉识别参数量大、计算量大、运行时间长。针对这些问题,论文提出一种基于改进轻量级网络(MobileNet)的指静脉识别算法。改进后的网络融入粒子群算法(PSO)对MobileNet参数进行优化。实验结果表明,该识别算法在保持高精度的前提下,减少了参数量和运算时间。

基于掌静脉识别技术的城市轨道交通乘车管理系统设计与实现

针对城市轨道交通传统自动售检票系统应用过程中面临的操作复杂度高、运营效率低等问题,基于掌静脉识别技术原理,构建基于掌静脉识别技术的城市轨道交通乘车管理系统,设计了系统架构和功能。通过掌静脉识别的注册认证、算法匹配、电子交互、多元融合等关键技术,实现了该系统与传统自动售检票系统的集成融合,达到了通过掌静脉识别快速乘车注册、识别、支付等通行目标。应用表明,该系统具备更高识别准确率、更低故障率等特点,具有无接触式进出站、掌码合一、实名制出行、多元化出行等优点,对城市轨道交通乘车管理系统进一步提高安全性和稳定性等具有一定的参考价值。

Artificial intelligence can assist with diagnosing retinal vein occlusion

AIM:To assist with retinal vein occlusion(RVO)screening,artificial intelligence(AI)methods based on deep learning(DL)have been developed to alleviate the pressure experienced by ophthalmologists and discover and treat RVO as early as possible.METHODS:A total of 8600 color fundus photographs(CFPs)were included for training,validation,and testing of disease recognition models and lesion segmentation models.Four disease recognition and four lesion segmentation models were established and compared.Finally,one disease recognition model and one lesion segmentation model were selected as superior.Additionally,224 CFPs from 130 patients were included as an external test set to determine the abilities of the two selected models.RESULTS:Using the Inception-v3 model for disease identification,the mean sensitivity,specificity,and F1 for the three disease types and normal CFPs were 0.93,0.99,and 0.95,respectively,and the mean area under the curve(AUC)was 0.99.Using the DeepLab-v3 model for lesion segmentation,the mean sensitivity,specificity,and F1 for four lesion types(abnormally dilated and tortuous blood vessels,cotton-wool spots,flame-shaped hemorrhages,and hard exudates)were 0.74,0.97,and 0.83,respectively.CONCLUSION:DL models show good performance when recognizing RVO and identifying lesions using CFPs.Because of the increasing number of RVO patients and increasing demand for trained ophthalmologists,DL models will be helpful for diagnosing RVO early in life and reducing vision impairment.

深度学习在手指静脉识别中的应用研究综述

手指静脉识别技术由于其非接触、高防伪性以及活体检测等优点,成为新一代生物识别技术中的研究热点。随着深度学习的发展,基于深度神经网络的手指静脉识别技术取得了显著的成果。首先对手指静脉识别领域的常用公开数据集进行了介绍,然后根据神经网络学习任务的不同,对近几年深度学习方法在手指静脉识别中的应用进行了分类,分析了每种类型的技术特点和适用场景。从轻量化网络、数据增广、注意力机制等方面对手指静脉识别中的深度学习设计技巧进行了介绍。从分类损失和度量学习损失两方面,对模型中常用的损失函数进行了阐述。最后介绍了手指静脉识别系统的评价指标并汇总了部分研究在准确率和等错误率方面的成果。此外,还提出了手指静脉识别面临的挑战和潜在的发展方向。

基于深度残差网络的轻量级指静脉识别算法

提出一种基于深度残差网络的轻量级指静脉识别算法。首先,以ResNet34为基础,使用深度可分离卷积代替传统卷积,加入SE(Squeeze and Excitation)注意力机制模块来提取手指静脉空间域上的细节特征,并引入宽度缩放因子,进一步压缩网络;其次,在训练中引入教师-学生网络模式,对轻量级深度残差网络进行知识蒸馏训练,并使用知识蒸馏损失、CurricularFace和交叉熵损失对网络进行联合监督,解决了轻量级深度残差网络因学习参数量较少引起的性能下降问题。分别在FV-USM数据集、Lab-Normal数据集和Lab-Special数据集上进行仿真实验,结果表明,同基于轻量级网络MobileFaceNet的识别算法相比,提出的算法有效提高了零误识识别率和Top1排序性能。

Finger vein recognition using weighted local binary pattern code based on a support vector machine

Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.

基于感兴趣区域的改进型LBP手指静脉识别

为进一步提升手指静脉识别算法的识别率,在图像预处理阶段提出一种快速感兴趣区域(RoI)提取方法,简化候选区域提取的计算过程,缩短手指区域提取时间。识别特征则是在局部二值模式(LBP)的基础上,利用邻域像素的平均值代替中心值,通过邻域像素的关系引入,提升了图像的纹理表达效果。在SDUMLA数据库与天津市智能实验室采集指静脉图像数据库上,分别取得了99.53%,99.74%的识别率,表明了算法优良的识别性能与泛化能力。

基于面部识别和掌静脉识别的AFC支付技术研究

二维码乘车技术能够实现刷码进出站,具有将支付载体从实体卡变成二维码,减少购票环节,降低单程票使用等优点,但乘客必须携带智能手机,同时该方法受运营商网络状态影响较大。因此,文章对面部识别技术和掌静脉识别技术的优缺点、技术原理、技术性能进行对比,并对两种生物识别技术在城市轨道交通中的应用进行对比。结果表明,两种生物识别技术可实现AFC系统无感支付,具有高效运行,提高通行效率,增加乘客刷卡过闸形式多样化,降低闸机设备故障率等优点,可为城市轨道交通的高效运行提供技术参考。

基于质量评价与特征提取网络的手指静脉识别

在利用手指静脉信息进行个人身份识别认证的系统中,采集到的手指静脉图像质量的好坏以及图像特征提取算法的性能直接影响着系统识别的准确性。针对以上两个核心问题,提出了基于神经网络学习的手指静脉图像质量判断与特征提取技术,并将图像质量判断、特征提取与匹配集成为一个完整的识别系统。在采集手指静脉图像时,利用经过训练的轻量级的MobileNet-V2网络,判断图像质量,仅保留质量较好的图像,将其用于后续个人身份识别与验证;对特征提取网络的设计,提出了基于平滑平均准确度损失函数的ResNet模型,提高了原网络的特征提取能力。最后,利用余弦相似度进行特征匹配,获得待识别人的身份信息。实验结果表明,所提出的方法在公开的山东大学和中国香港理工大学手指静脉数据库上分别得到3.855%和3.699%的等误率值,比基于差分图像+VGG网络识别等方法至少降低了1.311%。并且,所设计的识别系统对于自建手指静脉图像数据库,达到了99.30%的识别率。