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。     

基于CS的高质量掌静脉图像获取方法

为了获得高质量的手掌静脉图像,设计了一种能快速采集到高质量掌静脉图像的多光谱自适应采集系统方法及装置。通过研究高质量静脉图像对应的近红外光强度配比区域,结合图像质量评价模型和布谷鸟搜索(CS)算法搜索策略,构建出760,850,940 nm三种混合近红外光强自适应配比模型。该模型能够快速调优混合光源强度配比,以获得复杂场景下高质量手掌静脉图像。实验结果表明:CS算法模型及系统方案能快速有效地适应不同人群手掌静脉特征,在保证图像质量的前提下提供快速有效的光源配比方案,为在线手掌静脉识别的后续处理提供了良好的基础。     

基于分频和多感受野残差密集的指静脉图像超分辨率重建

针对红外线CCD摄像头采集指静脉图像较为模糊造成指静脉识别误检率高的问题,提出了基于分频和多感受野残差密集的指静脉图像超分辨率重建方法。该方法构建了图像高低频信息处理子网络,并将RRFDB结构集成到高频子网络中,以RFB为核心的残差密集块设计提升了感受野并降低计算复杂度,更好地保留了原始指静脉图像的线状纹理特征。实验结果表明,该方法能有效改善指静脉图像质量,与SRCNN、VDSR,DRRN等超分辨率重建方法在FV-USM和MMCBNU-6000数据集上进行对比实验,该方法对指静脉特征提取效果好,重建的图像质量高,PSNR与SSIM均优于其他方法。     

基于CCD的掌纹掌脉采集模块设计

针对掌纹掌脉身份认证的需求,以掌纹和掌静脉图像采集系统作为研究对象,设计了一种基于CCD的掌纹掌脉采集模块.采集模块采用单CCD作为图像感应芯片,在单颗芯片上同时获得掌纹掌脉图像,光源设计上突破性地采用了激光二极管作为红外补充光源,采集的图像以USB数据形式进行上传.实验表明:采集模块能够获得满足图像处理要求的清晰可靠的掌纹掌脉图像.     

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。     

高质量手掌静脉图像获取及ROI提取的研究

针对手掌静脉图像获取困难,获取的手掌静脉图像质量欠佳以及手掌区域定位方法复杂的问题,提出了一种手掌静脉采集装置,使用该装置进行手掌静脉图像采集。采用基于图像二维熵和局部二维熵方法来评价采集的图像质量,并依据反馈的评价结果作为依据,自适应控制近红外LED的亮度和摄像头的参数,重新采集直到获得高质量的手掌静脉图像。并将获得的高质量图像作为手掌感兴趣区域（ROI）提取及后续的图像处理算法的输入图像。本文的图像质量评价方法采用改进型适用于现场可编程门阵列（FPGA）平台的加速实现方法,ROI提取通过构建局部图像快速判别和定位。实验结果表明,使用二维熵评价图像的方法配合反馈控制PWM波的输出所采集的手掌静脉图像,比现有阶段方法采集的图像在灰度特性、静脉特征的效果方面得到提升,同时获得的图像减少了后处理中ROI提取算法的计算量。     

指静脉识别研究综述

指静脉识别因其独特的优势具有巨大的市场潜力,并得到了国内外各研究团体和工业界的高度关注。本文介绍了指静脉识别的主要研究内容及其研究现状,包括指静脉成像方法及图像增强技术、特征提取方法及与指静脉有关的多模态、多特征融合方法。其中详细介绍了指静脉特征提取方法,并将其划分为4类,即指静脉纹路特征、纹理特征、细节点特征及使用机器学习方法获得的特征。在此基础上进一步对指静脉识别及其应用面临的挑战性问题做了分析,这些问题主要包括降低采集设备价格、提高采集图像质量,及减小各种因素,如低质量图像、手指姿态变化、大规模用户群及室外采集等对识别性能的影响,这些问题为今后的指静脉识别的相关研究提供了思路和启迪。     

基于FPGA手指静脉图像采集系统的研制

分析了手指静脉成像原理,研制了以FPGA为主控芯片的手指静脉采集系统,该系统由手指静脉图像采集装置、图像采集控制模块、图像缓存控制模块和LCD显示控制模块等构成。实验结果表明,采用波长为850 nm的近红外光源模块、MT9V034摄像头、VIP_Board Full FPGA开发板、5.0寸TFT LCD显示屏组成的手指静脉采集显示系统体积小、采集实时性强,显示的手指静脉图像纹路清晰,具有良好的稳定性。基于FPGA手指静脉采集系统稳定清晰,具有巨大市场价值。     

一种基于分时光源的掌纹图像获取方法

随着生物技术、计算机技术以及电子技术的迅速发展,生物识别手段被应用到越来越多的领域,越来越受到人们的重视。生物识别具有简单易行、安全可靠等特点,往往被应用到身份认证、权限认证等方面。而掌纹认证、指纹认证则是生物识别手段中的重要内容,本文将研究一种基于分时光源的掌纹图像获取方法。本文将系统探究一种基于分时光源的掌纹图像获取方法,并基于这一方法设计出掌纹图像获取系统。     

基于FPGA自适应调光的指静脉成像及传输系统

随着指静脉技术的发展，指静脉识别技术运用于各种复杂场合，因此，指静脉成像质量的要求越来越高。根据近红外光对不同手指的穿透效果不一，提出一种由Xilinx Artix-7FPGA与MT9V034图像传感器以及近红外LED阵列组成的指静脉成像光源的自适应反馈调光方式，保证近红外光强度稳定在一定范围，并与USB3.0开发了一套高速图像采集传输系统。经实验测试结果表明，上位机图像显示稳定，测量不同的手指，其指静脉清晰可见，图像数据上传速率可达42 Mbps以上。     

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.     

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.

     

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.     

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.     

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.     

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.     

Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach

Biometrics has emerged as a powerful technology for person authentication in various scenarios including forensic and civilian applications. Deployment of biometric solutions that use cues from multiple modalities enhances the reliability and robustness of authentication necessary to meet the increasingly stringent security requirements. However, there are two drawbacks typically associated with multimodal biometrics. Firstly, the image acquisition process in such systems is not very user-friendly, primarily due to the time and effort required to capture biometric samples belonging to multiple modalities. Secondly, the overall cost is higher as they employ multiple biometric sensors. To overcome these drawbacks, we employ a single NIR sensor-based image acquisition in the proposed approach for hand-vein recognition. From the input hand image, a palm-vein and four finger-vein subimages are extracted. These images are then enhanced by CLAHE and transformed into illumination invariant representation using center-symmetric local binary pattern (CS-LBP). Further, a hierarchical non-rigid matching technique inspired by the architecture of deep convolutional networks is employed for matching the CS-LBP features. Finally, weighted sum rule-based matching score-level fusion is performed to combine the palm-vein and the four finger-vein modalities. A set of rigorous experiments has been performed on an in-house database collected from the left and right hands of 185 subjects and the publicly available CASIA dataset. The proposed approach achieves equal error rates of 0.13% and 1.21%, and rank-1 identification rates of 100% and 100% on the in-house and CASIA datasets, respectively. Additionally, we compare the proposed approach with the state-of-the-art techniques proposed for vascular biometric recognition in the literature. The important findings are (1) the proposed approach outperforms all the existing techniques considered in this study, (2) the fusion of palm-vein and finger-vein modalities consistently leads to better performance for all the feature extraction techniques considered in this work. (3) Furthermore, our experimental results also suggest that considering the constituent palm-vein and finger-vein images instead of the entire hand-vein images achieves better performance.