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     

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.

     

A finger-vein verification system using mean curvature

The finger-vein pattern is one of the human biometric signatures that can be used for personal verification. The first task of a verification process using finger-vein patterns is extracting the pattern from an infrared finger image. As a robust extraction method, we propose the mean curvature method, which views the vein image as a geometric shape and finds the valley-like structures with negative mean curvatures. When the matched pixel ratio is used in matching vein patterns, experimental results show that, while maintaining low complexity, the proposed method achieves 0.25% equal error rate, which is significantly lower than what existing methods can achieve.     

Finger-vein network enhancement and segmentation

Recently, an emerging biometric recognition based on human finger-vein patterns has received considerable attention. Due to light attenuation in imaging finger tissues, the finger-vein imagery is often seriously degraded. This makes network-based finger-vein feature representation greatly difficult in practice. In order to obtain perfect finger-vein networks, in this paper, we propose a novel scheme for venous region enhancement and finger-vein network segmentation. First, a method aimed at scattering removal, directional filtering and false vein information suppression is put forward to effectively enhance finger-vein images. Then, to achieve the high-fidelity extraction of finger-vein networks in an automated manner, a matting-based segmentation approach is presented considering the variations of veins in intensity and diameter. Extensive experiments are finally conducted to validate the proposed method.     

Feature-level fusion of fingerprint and finger-vein for personal identification

Multimodal biometrics based on feature-level fusion is a significant topic in personal identification research community. In this paper, a new fingerprint-vein based biometric method is proposed for making a finger more universal in biometrics. The fingerprint and finger-vein features are first exploited and extracted using a unified Gabor filter framework. Then, a novel supervised local-preserving canonical correlation analysis method (SLPCCAM) is proposed to generate fingerprint-vein feature vectors (FPVFVs) in feature-level fusion. Based on FPVFVs, the nearest neighborhood classifier is employed for personal identification finally. Experimental results show that the proposed approach has a high capability in fingerprint-vein based personal recognition as well as multimodal feature-level fusion.     

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.     

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.     

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.     

对抗图像旋转的手指静脉身份识别新算法的设计

为了克服图像旋转对手指静脉身份识别系统正确率的影响,在图像预处理部分提出一种基于手指指尖点的旋转定位方法,改进了基于方向模板和局部动态阈值分割提取静脉特征的方法并用改进Hausdorff距离（MHD）距离进行匹配验证。实验结果表明,同一根手指的图片在平面偏移角度小于20°时,可以达到0.75%的等误率,正确识别率达97.25%,而且整个处理过程在VC++6.0上面执行耗时仅为161.6949ms,系统具有很好的实时性能,对实际手指静脉身份识别产品的开发具有一定的现实意义。     

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.     

基于图像采集质量评价的指纹与指静脉双模态识别决策级融合方法

针对单一模态生物特征识别系统性能受图像质量影响较大问题,提出一种基于图像采集质量评价的指纹与指静脉识别的决策级融合方法。该方法不仅对指纹图像进行质量评价,并首次根据指静脉图像特性设计图像采集质量评价指标,以达到克服图像质量对识别结果影响的目的。再针对这两种模态图像特点分别进行分类器设计,得出各自的识别结果后,结合上述得到的图像采集质量评价分数进行决策级融合,将融合后的结果作为最终的识别结果。实验表明,该方法有效克服图像质量对识别结果的影响,提高识别系统的性能,为多生物特征身份识别提供一种有效途径。     

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。     

Fake finger-vein image detection based on Fourier and wavelet transforms

Recently, finger-vein recognition has received considerable attention. It is widely used in many applications because of its numerous advantages, such as the small capture device, high accuracy, and user convenience. Nevertheless, finger-vein recognition faces a number of challenges. One critical issue is the use of fake finger-vein images to carry out system attacks. To overcome this problem, we propose a new fake finger-vein image-detection method based on the analysis of finger-vein images in both the frequency and spatial domains.This research is novel in five key ways. First, very little research has been conducted to date on fake finger-vein image detection. We construct a variety of fake finger-vein images, printed on A4 paper, matte paper, and overhead projector film, with which we evaluate the performance of our system. Second, because our proposed method is based on a single captured image, rather than a series of successive images, the processing time is short, no additional image alignment is required, and it is very convenient for users. Third, our proposed method is software-based, and can thus be easily implemented in various finger-vein recognition systems without special hardware. Fourth, Fourier transform features in the frequency domain are used for the detection of fake finger-vein images; further, both spatial and frequency characteristics from Haar and Daubechies wavelet transforms are used for fake finger-vein image detection. Fifth, the detection accuracy of fake finger-vein images is enhanced by combining the features of the Fourier transform and Haar and Daubechies wavelet transforms based on support vector machines.Experimental results indicate that the equal error rate of fake finger-vein image detection with our proposed method is lower than that with a Fourier transform, wavelet transform, or other fusion methods.     

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。     

Finger-vein verification based on the curvature in Radon space

Finger-vein verification has drawn increasing attention because it is highly secured and private biometric in practical applications. However, as the imaging environment is affected by many factors, the captured image contains not only the vein pattern but also the noise and irregular shadowing which can decrease the verification accuracy. To address this problem, in this paper, we proposed a new finger-vein extraction approach which detects the valley-like structures using the curvatures in Radon space. Firstly, given a pixel, we obtain eight patches centered on it by rotating a window along eight different orientations and project the resulting patches into Radon space using the Radon transform. Secondly, the vein patches create prominent valleys in Radon space. The vein patterns are enhanced according to the curvature values of the valleys. Finally, the vein network is extracted from the enhancing image by a binarization scheme and matched for personal verification. The experimental results on both contacted and contactless finger-vein databases illustrate that our approach can significantly improve the accuracy of the finger-vein verification system.     

基于MB-CSLBP的手指静脉加密算法研究

为解决在传统的生物特征加密技术的安全性上的不足,对手指静脉特征加密方法进行了探讨和研究。提出了基于MB-CSLBP编码的手指静脉加密方案。首先对LBP算子以及改进的CSLBP、MB-CSLBP算子进行了研究,提取了手指静脉的MB-CSLBP二进制特征编码。然后研究了传统的模糊承诺加密方案,在此基础上将提取的手指静脉MB-CSLBP二进制特征编码作为加密特征,对加密信息进行BCH编码后与加密特征以异或的方式结合完成加密,同时使用SHA-1散列算法对加密信息进行哈希变换,保留得到的哈希值以用于解密。实验结果表明,当密钥长度为400 b时,FAR达到了0.47%,文中提出的基于MB-CSLBP编码的手指静脉加密方案具有很高的鲁棒性和安全性。     

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...     

Special point representations for reducing data space requirements of finger-vein recognition applications

Due to the uniqueness of the finger-vein patterns hidden beneath the skin, forgery is very difficult. Providing fast and accurate finger-vein recognition represents the answer to biometric security system as we need more secure and reliable authentication methods. However, the finger-vein based recognition system is limited by the storage space and time complexity, which significantly reduce the accuracy of the identification. In this paper, we present an effective method of matching in a finger-vein recognition system to overcome the disadvantage of requiring significant data storage and heavy CPU computation requirements. Our proposed solution involved considering special points characterizing complex finger-vein information and their connections, thereby retaining only the evidence related to matching to perform subsequent identification. Experimental results show that our method achieves robust matching with an error rate of 0.216 % and confirm that the proposed mechanism can reduce the quantity of data that requires storage and maintain a certain level of authentication accuracy.     

级联优化CNN的手指静脉图像质量评估

目的 针对手动设计的手指静脉质量特征计算过程复杂、鲁棒性差、表达效果不理想等问题,提出了基于级联优化CNN（卷积神经网络）进行多特征融合的手指静脉质量评估方法。方法 以半自动化方式对手指静脉公开数据库MMCBNU_6000进行质量标注并用R-SMOTE（radom-synthetic minority over-sampling technique）算法平衡类别;将深度学习中的CNN结构应用到手指静脉质量评估并研究了不同的网络深度对表征手指静脉质量的影响;受到传统方法中将二值图像和灰度图像结合进行质量评估的启发,设计了两种融合灰度图像和二值图像的质量特征的模型：多通道CNN（MC-CNN）和级联优化CNN（CF-CNN）,MC-CNN在训练和测试时均需要同时输入二值图像和灰度图像,CF-CNN在训练时分阶段输入二值图像和灰度图像,测试时只需输入灰度图像。结果 本文设计的3种简单CNN结构（CNN-K,K=3,4,5）在MMCBNU_6000数据库上对测试集图像的分类正确率分别为93.31%、93.94%、85.63%,以灰度图像和二值图像分别作为CNN-4的输入在MMCBNU_6000数据库上对测试集图像的分类正确率对应为93.94%、91.92%,MC-CNN和CF-CNN在MMCBNU_6000数据库上对测试集图像的分类正确率分别为91.44%、94.62%,此外,与现有的其他算法相比,CF-CNN在MMCBNU_6000数据库上对高质量测试图像、低质量测试图像、整体测试集图像的分类正确率均最高。结论 实验结果表明,基于CF-CNN学习到的融合质量特征比现有的手工特征和基于单一静脉形式学习到的特征表达效果更好,可以有效地对手指静脉图像进行高、低质量的区分。     

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。