基于相对距离的手指静脉识别方法

提出了一种新的手指静脉识别方法,即以静脉内部拓扑结构的本质特性为原理进行手指静脉的匹配,首先对细化修复后的图像提取端点和交叉点,并计算这些特征点之间的相对距离,最后通过比对这些距离值来完成手指静脉图像的识别。该方法结合静脉自身特征,充分利用了拓扑结构的本质属性,无须定位,简单易行。实验结果表明,该方法能够快速准确地进行身份识别,在一定程度上克服了平移、旋转对识别结果的影响,具有实际应用价值。     

基于MMNBP的手指静脉识别方法

针对传统的局部二值模式(LBP)手指静脉特征识别率不高的问题,提出基于多块均值近邻二值模式(MMNBP)的手指静脉识别方法。对LBP算法改进,提出基于近邻二值模式(NBP)的特征提取方法;将指静脉图像分块并取子块均值,对所有子块均值构成的图像采用NBP方法提取特征,从而形成MMNBP方法;利用汉明距离进行匹配。在国外和国内两个图库上与几种典型算法进行对比实验,结果表明,提出的方法可获得最低等误率分别为2. 4611%和0. 3137%,证明MMNBP方法能够进一步提高身份识别的鲁棒性,具有较好的稳定性和有效性。     

采用旋转校正的指静脉图像感兴趣区域提取方法

针对静脉图像采样过程中存在的旋转、平移等非线性因素造成手指静脉图像定位困难的问题,考虑图像非接触式采集特点,提出一种采用旋转校正的手指静脉图像感兴趣区域提取方法.首先对读入的手指静脉图像采用Kapur熵阈值法分割出手指区域,再依据图像的质心对图像进行旋转校正,最后根据图像中每列像素竖直方向上的投影值和手指区域的边缘轮廓,确定出感兴趣区域的位置.实验结果表明,该方法能够准确地提取出静脉图像的感兴趣区域,有效地提高识别系统的性能.     

近红外指静脉图像采集系统设计

针对指静脉身份认证需求,以手指静脉图像采集系统作为研究对象,设计了基于单侧光源与反射镜面相结合红外光源可调控的指静脉图像采集系统。研究了LED光源位置与角度对静脉图像质量的影响,提出了基于图像质量评价的指静脉认证方法,并运用实测方法进行了验证。实验结果表明:可以得到与传统正面光源采集同等质量的静脉图像,具有更高的认证通过率,达到98.8%,并更易于用户使用。     

一种基于2DHOL特征与(2D)~2FPCA结合的手指静脉识别方法

文中提出一种基于二维方向线直方图统计(2DHOL)特征与双向二维费希尔主成分分析((2D)~2FPCA))相结合的手指静脉识别方法.首先针对手指静脉图像纹路走向的特点,改进基于梯度直方图(HOG)特征中有关梯度幅值和方向的计算方法,采用二维Gabor滤波器获取静脉图像的线形响应和方向,提取2DHOL特征;然后综合考虑行列相关性和类别信息,采用(2D)~2FPCA对2DHOL特征进行降维处理,得到手指静脉特征向量;最后计算特征向量的欧氏距离.应用不同手指静脉数据库进行实验的结果表明,该方法能够有效地提高手指静脉识别率,并对训练样本数变化具有较强的鲁棒性.     

一种基于模板匹配的手指静脉识别算法

相对于指纹识别等传统生物特征识别手段,手指静脉识别是一种新兴的具有较好应用前景的生物特征识别技术。本文设计了具有自适应光源系统的手指静脉采集仪,能够自动获得亮度均匀的手指静脉图像;提出了一种基于模板匹配的手指静脉识别算法,采用基于多方向灰度谷底搜寻方法提取手指静脉特征,然后将从同一手指多个图像中提取的静脉特征合成模板,并通过门限阈值消除模板中的随机差异信息。实验结果表明,运用本研究提出的基于模板匹配的手指静脉识别算法能有效提高识别准确性,具有99.10%的识别准确率和1.03%的等错误率。     

融合局部特征和全局特征的手指静脉识别方法

手指静脉识别是利用人体手指静脉结构的唯一性实现个体身份认证,具有高度安全和使用便捷等优点。为了进一步提高手指静脉识别系统的性能,提出了一种融合局部特征和全局特征的手指静脉识别方法。应用局部二元模式方法提取手指静脉局部特征,利用海明距离计算匹配得分;应用双向两维主成分分析方法提取手指静脉全局特征,利用欧式距离计算匹配得分;在得分级上融合二者的匹配得分以产生识别结果。实验结果表明,局部特征与全局特征具有较好的互补性,有效地提高了识别精度。     

基于ARM的手指静脉识别系统的设计与实现

手指静脉识别是第二代生物认证的高端手段.为了实现识别设备的小型化,针对嵌入式系统独立运行的优点,设计并实现了一种基于ARM11和Windows CE的手指静脉识别系统.ARM11处理器从数字摄像头获取手指静脉图像,通过开发的应用软件进行图像预处理和特征提取,与建立的手指静脉图像库中的对应模板比对,完成身份认证.实验结果...     

移动互联网中基于手指静脉图像信息加密的研究

移动互联网下如何更好的进行图像的加密一直都是研究的热点,针对手指静脉图像在移动互联网下传输中可能出现的不安全因素,本文首先对手指静脉图像的提取进行了改进,采用基于灰度形态学的膨胀运算,加权方式,模糊增强和灰度拉伸的方法获得更加清晰的图像,其次对静脉图像采用基于小波置换,Arnold映射,二次Logistic 映射和k阶Backer变换的混合加密方式对进行加密。仿真实验表明本文算法在统计分析,相关系分析和差分攻击分析等方面具有的较好安全性和有效性。     

基于Fisher准则的多指静脉融合

针对单特征手指静脉识别中识别率难以继续提高的技术瓶颈,采用多特征融合技术不仅可以提高识别率,而且可以降低误识率.为此提出一种基于Fisher准则的手指静脉融合算法.首先对手指静脉图像进行特征点提取,分别计算待匹配图像特征点与注册图像特征点的正向平均豪斯道夫距离(FMHD)和反向平均豪斯道夫距离(RMHD),然后基于Fisher准则确定FMHD和RMHD的融合参数,将融合得到的豪斯道夫距离作为新的匹配分数;在上述算法的基础上,将得到的食指、中指和无名指3根手指静脉的匹配分数进行融合,以进一步提高手指静脉的识别率.实验结果表明,与通常采用的FMHD相比,采用融合后的豪斯道夫距离的误识率有明显降低;而采用三指静脉融合后,误识率由单个手指的1.95％降低到0.27％.     

Finger-vein recognition based on parametric-oriented corrections

The two key factors in a biometric identification system are its high identification rate and convenience of device usage. In a finger-vein identification task, these two problems often occur since the captured device of finger-vein image should accommodate the high identification rate as well as the easy-to-use device design. The finger-vein is visually invisible inside the human skin. This work develops a new finger-vein capturing device using Near-Infrared (NIR) LED light and proposes an efficient technique for finger-vein identification. The vein image may contain noise and shadows due to device lighting conditions. Parametric-Oriented Histogram Equalization (POHE) is utilized to enhance image contrast and reduce the noise effect. This work also discusses normalized issues related to the angle correction of the finger edge and Region of Interest (ROI) for width normalization. In the experimental result, the proposed method yields a clear finger-vein pattern with a superior identification rate in the recognition task compared to the state-of-the-art methods.

     

Driver identification using finger-vein patterns with Radon transform and neural network

A driver identification system using finger-vein technology and an artificial neural network is presented in this paper. The principle of the proposed system is based on the function of near infra-red finger-vein patterns for biometric authentication. Finger-vein patterns are required by transmitting near infra-red through a finger and capturing the image with an infra-red CCD camera. The algorithm of the proposed system consists of a combination of feature extraction using Radon transform and classification using the neural network technique. The Radon transform can concentrate the information of an image in a few high-valued coefficients in the transformed domain. The neural networks are used to develop the training and testing modules. The artificial neural network techniques using radial basis function network and probabilistic neural network are proposed to develop a driver identification system. The experimental results indicated the proposed system performs well for personal identification. The average identification rate of PNN network is over 99.2%. The details of the image processing technique and the characteristic of system are also described in this paper.     

Personal identification based on finger-vein features

Finger-vein recognition refers to a recent biometric technique which exploits the vein patterns in the human finger to identify individuals. The advantages of finger vein over traditional biometrics (e.g. face, fingerprint, and iris) lie in low-risk forgery, noninvasiveness, and noncontact. This paper here presents a new method of personal identification based on finger-vein recognition. First, a stable region representing finger-vein network is cropped from the image plane of an imaging sensor. A bank of Gabor filters is then used to exploit the finger-vein characteristics at different orientations and scales. Based on the filtered image, both local and global finger-vein features are extracted to construct a finger-vein code (FVCode). Finally, finger-vein recognition is implemented using the cosine similarity measure classifier, and a fusion scheme in decision level is adopted to improve the reliability of identification. Experimental results show that the proposed method exhibit an exciting performance in personal identification.     

Finger-vein pattern identification using SVM and neural network technique

This paper presents a support vector machine (SVM) technique for finger-vein pattern identification in a personal identification system. Finger-vein pattern identification is one of the most secure and convenient techniques for personal identification. In the proposed system, the finger-vein pattern is captured by infrared LED and a CCD camera because the vein pattern is not easily observed in visible light. The proposed verification system consists of image pre-processing and pattern classification. In the work, principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the image pre-processing as dimension reduction and feature extraction. For pattern classification, this system used an SVM and adaptive neuro-fuzzy inference system (ANFIS). The PCA method is used to remove noise residing in the discarded dimensions and retain the main feature by LDA. The features are then used in pattern classification and identification. The accuracy of classification using SVM is 98% and only takes 0.015 s. The result shows a superior performance to the artificial neural network of ANFIS in the proposed system.     

Finger-vein pattern identification using principal component analysis and the neural network technique

This paper presents a personal identification system using finger-vein patterns with component analysis and neural network technology. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis. The proposed biometric system for verification consists of a combination of feature extraction using principal component analysis (PCA) and pattern classification using back-propagation (BP) network and adaptive neuro-fuzzy inference system (ANFIS). Finger-vein features are first extracted by PCA method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed ANFIS in the pattern classification, the BP network is compared with the proposed system. The experimental results indicated the proposed system using ANFIS has better performance than the BP network for personal identification using the finger-vein patterns.     

Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification

We propose a method of personal identification based on finger-vein patterns. An image of a finger captured under infrared light contains not only the vein pattern but also irregular shading produced by the various thicknesses of the finger bones and muscles. The proposed method extracts the finger-vein pattern from the unclear image by using line tracking that starts from various positions. Experimental results show that it achieves robust pattern extraction, and the equal error rate was 0.145% in personal identification.Received: 27 October 2003, Accepted: 25 February 2004, Published online: 21 July 2004     

基于指部关联特征的多模态图像采集系统

针对基于单个生物特征的身份认证安全性和稳定性不足的问题,设计了基于指部关联特征的多模态图像采集系统,采用单个双波段摄像头分时采集同一根手指的指纹、指节纹和指静脉图像。指纹和指节纹采用非接触反射采集方式,指静脉采用单侧近红外光源与反射镜面相结合的透射采集方式,并根据静脉图像质量评价动态调控光源,根据特征点信息量动态调整各个特征的权重。实验结果表明,该多模态采集系统在认证通过率、误识率和拒登率等指标都优于指纹或指静脉的单模态采集系统,认证通过率达到99.1%,误识率为0.000 1%,不存在拒登现象。     

A real-time near infrared image acquisition system based on image quality assessment

This paper presents a real-time image acquisition system with an improved image quality assessment module to acquire high-quality near infrared (NIR) images. Thermal imaging plays a vital role in a wide range of medical and military applications. The demand for high-throughput image acquisition and image processing has continuously increased especially for critical medical and military purposes where executions under real-time constraints are required. This work implements an NIR image quality assessment module, which utilizes improved two-dimensional entropy and mask-based edge detection algorithms. The effectiveness of the proposed image quality assessment algorithms is demonstrated through the implementation of a complete finger-vein biometric system. The proposed model is implemented as an embedded system on a field programmable gate array prototyping platform. By including the image quality assessment module, the proposed system is able to achieve a recognition accuracy of 0.87 % equal error rate, and can handle real-time processing at 15 frames/s (live video rate). This is achieved through hardware acceleration of the proposed image quality assessment algorithms via a novel streaming architecture.     

Finger-vein authentication based on deformation-tolerant feature-point matching

A novel method for finger-vein authentication based on feature-point matching is proposed and evaluated. A finger-vein image captured by infrared light contains artifacts such as irregular shading and vein posture deformation that can degrade accuracy of finger-vein authentication. Therefore, a method is proposed for extracting features from vein patterns and for matching feature points that is robust against irregular shading and vein deformation. In the proposed method, curvature of image-intensity profiles is used for feature point extraction because such image profiles are a robust feature against irregular shading. To increase the number of feature points, these points are extracted from any positions where vein shape is non-linear. Moreover, a finger-shape model and non-rigid registration method are proposed. Both the model and the registration method correct a deformation caused by the finger-posture change. It is experimentally shown that the proposed method achieves more robust matching than conventional methods. Furthermore, experiments on finger-vein identification show that the proposed method provides higher identification accuracy than conventional methods.