刚性变形成像条件下掌纹识别方法研究

目前广泛使用的掌纹图像采集装置是非接触式,这种方式适应了掌纹识别生活化的实用要求。但是构成了不稳定的成像环境,拍摄过程中会产生平移、旋转、扭曲,我们将这些不会影响掌纹线结构特征的变形称之为刚性变形。本文从手掌长度和宽度两个角度衡量掌纹图像刚性变形程度,设计了一种归一化校正方法。建立不同变形程度的掌纹图库,对掌纹特征匹配结果进行比较实验;实验结果表明,这种方法能够降低由于刚性变形对识别率产生的影响。     

Stereo-based palmprint recognition in various 3D postures

In order to increase performance in palmprint recognition systems, various devices are normally used to restrict the movement of the hand. These can cause problems, especially for those users with physical disabilities. They also cause significant hygiene problems in multi-user systems. Recently, studies on palmprint recognition systems have progressed towards the development of unconstrained, contactless and unrestricted background techniques. The most common problem encountered in these studies is the alignment arising from the free movement of the hand. Despite 3D hand-acquisition devices which offer extra recognition features to overcome this problem, the applicability of these devices is low because of their increased cost. In this study, a stereo camera was proposed. Although due to matching problems, it is difficult to achieve precise, distinct feature extraction in the unrestricted 3D environment used for palmprint recognition, the orientation of the hand in 3D space can be determined by obtaining depth information. In this study, the depth information was extracted by using the binocular stereo approach. First, the orientation of the hand was estimated by fitting a surface model associated with the eigenvectors of the depth information. Pose correction was then accomplished by establishing a relationship between the orientation and the images. The pose correction greatly relieved the perspective distortion that usually occurs within the various poses of the hands. Next, the region of interest was determined by performing segmentation on the corrected images using the Active Appearance Model (AAM). The palmprint features were then extracted via Gabor-based Kernel Fisher Discriminant Analysis. In order to demonstrate the performance of the proposed approach, a new dataset was compiled from stereo images within various scenarios collected from 138 different individuals. As a result of these experimental studies, the EER values, especially on the images captured from different hand orientations in 3D, were reduced from around 14–0.75%. With the help of this suggested approach, the palmprint recognition system was transformed into a more portable form by removing the closed-box mechanisms and equipment restricting movement of the hand. This system can automatically perform pose estimation, hand segmentation and recognition processes without any special intervention.     

Palmprint Recognition by Applying Wavelet-Based Kernel PCA

This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coeffcients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coeffcients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.     

改进的单幅近红外掌纹掌静脉图像融合识别*

现有的单幅近红外掌静脉掌纹融合识别算法不能很好的突出掌纹与掌静脉结构。针对这个问题,提出一种改进的融合识别算法。首先,采用分块模型去除图像中的掌静脉得到掌纹结构,通过隶属度函数对掌纹结构进行模糊化,再进行反锐化掩模增强,突出掌纹结构信息;然后,使用边缘检测加权引导滤波对掌静脉结构进行增强,突出掌静脉结构;最后,将掌纹和掌静脉图像进行融合。实验结果表明,改进后的融合识别算法的识别率达到了99.81%。     

Palmprint based recognition system using phase-difference information

This paper presents an efficient palmprint based human recognition system. Each palmprint is divided into several square overlapping blocks. Reconstruction error using principle component analysis (PCA) is used to classify these blocks into either a good block or a non-palmprint block. Features from each good block of a palmprint are obtained by binarising the phase-difference of vertical and horizontal phase. The Hamming distance is used to compute the matching score between the features of corresponding good blocks of enrolled and live palmprint. These matching scores are fused using weighted sum rule, where weights are based on the average discriminating level of a block relative to other blocks. The performance of the proposed system is analysed on different datasets of hand images and it has been observed that it achieves a Correct Recognition Rate of 100% with a low Equal Error Rate for all the datasets. The system is also evaluated for noisy and bad palmprint images. It is found to be robust as long as the noise density is less than 50% or the bad region is less than 64% of the images.     

Person recognition by fusing palmprint and palm vein images based on “Laplacianpalm” representation

Unimodal analysis of palmprint and palm vein has been investigated for person recognition. One of the problems with unimodality is that the unimodal biometric is less accurate and vulnerable to spoofing, as the data can be imitated or forged. In this paper, we present a multimodal personal identification system using palmprint and palm vein images with their fusion applied at the image level. The palmprint and palm vein images are fused by a new edge-preserving and contrast-enhancing wavelet fusion method in which the modified multiscale edges of the palmprint and palm vein images are combined. We developed a fusion rule that enhances the discriminatory information in the images. Here, a novel palm representation, called “Laplacianpalm” feature, is extracted from the fused images by the locality preserving projections (LPP). Unlike the Eigenpalm approach, the “Laplacianpalm” finds an embedding that preserves local information and yields a palm space that best detects the essential manifold structure. We compare the proposed “Laplacianpalm” approach with the Fisherpalm and Eigenpalm methods on a large data set. Experimental results show that the proposed “Laplacianpalm” approach provides a better representation and achieves lower error rates in palm recognition. Furthermore, the proposed multimodal method outperforms any of its individual modality.     

Local image quality measurement for multi-scale forensic palmprints

Numerous studies show that palmprint image quality has a significant effect on every stage of a palmprint recognition system. Although some palmprint image quality measurement(PIQM) methods are proposed, some insufficiency in classification accuracy occurs and attention to detail in measuring local area image quality of multi-scale palmprint images is lacking. On the one hand, the classification accuracy is not very high for 2-class classification and it degrades significantly as the number of classes increases. On the other hand, local area image quality measurement of multi-scale palmprint images has not yet been resolved since the handcrafted features designed through domain knowledge usually works for certain scale image blocks. Meanwhile, the intricate domain knowledge used in the previous methods is difficult for some common users to acquire. In this paper, we propose an end-to-end deep-learning method of strengthening representation ability that learns more abstract, essential, and reliable features to measure the local image quality for multi-scale forensic palmprints. Popular convolutional neural networks (CNNs) are considered because of their powerful representation ability in learning complex features. However, the powerful existing CNNs usually have complex architectures with a large amount of parameters, which need the support of high-performance computers. They are not suitable to be used directly for palmprint image quality assignment and the follow-up palmprint recognition work, which prefers real-time response on commonly available personal computers or even mobile devices. Hence, a new lightweight CNN must be designed to achieve a trade-off between high classification accuracy and practical usability. Considering the attributes of under-processed input images, we reduce the weight of the CNN architecture by reducing the amount of some parameters, and finally a lightweight CNN is designed. As a result, a raw rectangular palmprint image of variable size can be put into the trained model directly and a quality label quickly predicted with high accuracy. After comparison with previous methods, results show that the proposed method can deal with un-pre-processed raw images of a multi-scale input size. Furthermore, it can acquire a richer amount of quality classes with a higher accuracy, which are stable on many different datasets. It also leads to finer and more precise full palmprint image quality maps when compared to previous methods.

     

Palmprint Verification for Controlling Access to Shared Computing Resources

You can build an effective palmprint verification system using a combination of mostly off-the-shelf components and techniques. Access security is an important aspect of pervasive computing systems. It offers the system developer and end users a certain degree of trust in the use of shared computing resources. Biometrics verification offers many advantages over the username-plus-password approach for access control. Users don't have to memorize any codes or passwords, and biometric systems are more reliable because biometric characteristics can't easily be duplicated, lost, or stolen. Researchers have studied such biometric characteristics as faces, fingerprints, irises, voices, and palmprints.Facial appearance and features change with age. Fingerprints can be affected by surface abrasions or otherwise compromised. Capturing iris images is relatively difficult, and iris scans can be intrusive. Voices are susceptible to noise corruption and can be easily copied and manipulated. Palmprints are potentially a good choice for biometric applications because they're invariant with a person, easy to capture, and difficult to duplicate. They offer greater security than fingerprints because palm veins are more complex than finger veins. However, compared to other biometric characteristics, they have perhaps seen less research. This provides a big opportunity for advancing palmprint technology and applications. We've developed an effective prototype palmprint verification system using a combination of mostly off-the-shelf (and therefore tried and tested) components and techniques. Such an approach should make palmprint verification an appealing proposition.     

Fusion of palm-phalanges print with palmprint and dorsal hand vein

To ensure the high performance of a biometric system, various unimodal systems are combined to evade their constraints to form a multimodal biometric system. Here, a multimodal personal authentication system using palmprint, dorsal hand vein pattern and a novel biometric modality “palm-phalanges print” is presented. Firstly, we have collected a new anterior hand database of 50 individuals with 500 images at the institute referred to as NSIT Palmprint Database 1.0 by using NSIT palmprint device. Then from these anterior hand images, database for palmprint and palm-phalanges is created. In this biometric system, the individuals do not have to undergo the distress of using two different sensors since the palmprint and palm-phalanges print features can be captured from the same image, using NSIT palmprint device, at the same time. For dorsal hand vein, Bosphorus Hand Vein Database is used because of the stability and uniqueness of hand vein patterns. We propose fusion of three different biometric modalities which includes palmprint (PP), palm-phalanges print (PPP) and dorsal hand vein (DHV) and perform score level fusion of PP-PPP, PP-DHV, PPP-DHV and PP-PPP-DHV strategies. Lastly, we use K-nearest neighbor, support vector machine and random forest to validate the matching stage. The results proved the validity of our proposed modality and show that multimodal fusion has an edge over unimodal fusion.     

Palmprint verification using hierarchical decomposition

A reliable and robust personal verification approach using palmprint features is presented in this paper. The characteristics of the proposed approach are that no prior knowledge about the objects is necessary and the parameters can be set automatically. In our work, a flatbed scanner is adopted as an input device for capturing palmprint images; it has the advantages of working without palm inking or a docking device. In the proposed approach, two finger-webs are automatically selected as the datum points to define the region of interest (ROI) in the palmprint images. The hierarchical decomposition mechanism is applied to extract principal palmprint features inside the ROI, which includes directional and multi-resolution decompositions. The former extracts principal palmprint features from each ROI. The latter process the images with principal palmprint feature and extract the dominant points from the images at different resolutions. A total of 4800 palmprint images were collected from 160 persons to verify the validity of the proposed palmprint verification approach and the results are satisfactory with acceptable accuracy (FRR: 0.75% and FAR: 0.69%). Experimental results demonstrate that our proposed approach is feasible and effective in palmprint verification.     

单幅近红外手掌图像掌静脉和掌纹多特征识别

传统的掌静脉和掌纹图像融合识别一般需分别采集掌静脉和掌纹两类图像,而单幅近红外手掌图像中实际上同时包含了掌静脉和掌纹结构信息。由于二者局部纹理细节差异较大,且像素值分布范围不同,因此,可以先分离再分别增强处理。首先,提出了改进的引导滤波算法以便去除掌纹结构,并设计了反模糊细节增强模型增强掌静脉结构图像;然后,提出了一种改进的分块增强算法,可以在增强掌纹结构图像的同时滤除掌静脉结构信息,再利用基于Sobel算子的反锐化掩模算法以便突出掌纹主线条结构信息;最后,对单幅近红外手掌图像中获取的掌静脉和掌纹图像进行融合识别。在香港理工大学近红外手掌数据库上进行了实验,结果表明：所提出的算法识别率达到了99.63%,与其他已有算法相比等误率平均降低了0.66%,验证了所提出算法的有效性。     

非接触式掌纹图像采集装置的研究

基于摄影技术中的伞灯原理,提出一种非接触式的掌纹采集装置,在摄像头镜头的景深范围内,用户随意将手伸到镜头前便能采集到清晰的掌纹图像,解决了以往采集装置定位难不易被用户接受的缺点,更接近掌纹识别在实际应用中的模式.装置以数字信号处理器TMS320DM6437为核心,以TVP5146和CMOSPO188为辅助,采集到的是整个手的图像,方便与其他生物特征识别相结合,利用该装置采集到的图像比较清晰、大小适中,能够满足识别算法的要求.     

应用非负矩阵分解和RBPNN模型的掌纹识别方法

提出一种基于非负矩阵分解（NMF）和径向基概率神经网络的掌纹识别方法。NFM是一种有效的图像局部特征提取算法,用于图像分类时能得到较高的识别率。考虑PolyU掌纹图像数据库,应用NMF、局部NMF（LNMF）、稀疏NMF（SNMF）和具有稀疏度约束的NMF（NMFSC）算法分别对掌纹图像进行特征提取,并对提取到的局部特征基图像进行分析对比;在特征提取的基础上,应用径向基概率神经网络（RBPNN）模型对掌纹特征进行分类,分类结果表明了RBPNN模型对掌纹特征具有较好的识别能力。实验对比结果证明了基于RBPNN的NMF掌纹识别方法在掌纹识别中的有效性,具有一定的理论研究意义和实用性。     

一种新的掌纹图像预处理方法

掌纹图像预处理主要包括图像分割和图像增强。在分析现有掌纹图像预处理方法中存在的不足之后,提出了一种新的掌纹图像预处理方法。该方法以手掌外侧轮廓作为定位参考线,首先实现手掌定位;然后以手掌的最大内切圆圆心为掌纹图像坐标系的原点构建坐标系,并截取出该圆的内接正方形区域内的掌纹图像,以完成图像分割。另外,该方法将模糊理论引入到反锐化掩模系统框架中,并用方向可调滤波器替代传统反锐化掩模算法中的Lap lac ian滤波器来获取掌纹图像中的高频成分,以实现在线掌纹图像的增强。实验结果表明,该方法不仅在手指完全并拢的情况下,仍然能提取出掌纹中心子图,而且能有效增强掌纹图像中主线和皱纹线的对比度。     

掌纹图像的获取及其预处理

提出了一种无限制的获取掌纹图像的方法,并使用该方法构建了掌纹库.该掌纹库可以成为掌纹识别算法训练集和测试集的来源,也可以成为进一步推进掌纹识别研究与发展的基础.在该掌纹库的基础上,对掌纹库中的掌纹图像进行了预处理,将原始图像二值化后利用定位点自动检测技术检测出掌纹图像中两个关键的定位点,并以此为基础对掌纹图像进行旋转校正,最后切取一定区域的掌纹子图,为进一步提取掌纹特征打下了较好的基础.     

Contact-free hand geometry-based identification system

This paper presents an approach for personal identification using hand geometrical features, in which the infrared illumination device is employed to improve the usability of this hand recognition system. In the proposed system, prospective users can place their hand freely in front of the camera without any pegs or templates. Moreover, the proposed system can be widely used under dark environment and complex background scenarios. To achieve better detection accuracy, in total 13 important points are detected from a palm image, and 34 features calculated from these points are used to further recognition. Experimental results demonstrate that the averaged Correct Identification Rate (CIR) is 96.23% and averaged False Accept Rate (FAR) is 1.85%. These results prove that the proposed contact-free system can be considered as an effective identity verification system for practical applications.     

掌纹识别研究进展综述

掌纹图像蕴含丰富特征,容易与手背静脉、指节纹及手形特征进行多模态融合,因此成为生物特征识别领域的热点.文中主要从掌纹的采集、感兴趣区域的检测、特征提取与匹配3方面介绍掌纹识别的基本流程.探讨基于不同特征融合的多模态识别策略.根据特征提取方法的不同,掌纹识别算法可分为基于手工设计的算法(如编码特征、结构特征、统计特征、子空间特征)和基于特征学习的算法(如机器学习和深度学习),文中对上述算法进行详细对比和分析.最后讨论未来掌纹识别面临的挑战和发展,特别是复杂场景下跨平台的掌纹识别系统.     

Robust palmprint verification using 2D and 3D features

This paper presents a new personal authentication system that simultaneously exploits 2D and 3D palmprint features. The objective of our work is to improve accuracy and robustness of existing palmprint authentication systems using 3D palmprint features. The proposed multilevel framework for personal authentication efficiently utilizes the robustness (against spoof attacks) of the 3D features and the high discriminating power of the 2D features. The developed system uses an active stereo technique, structured light, to simultaneously capture 3D image or range data and a registered intensity image of the palm. The surface curvature feature based method is investigated for 3D palmprint feature extraction while Gabor feature based competitive coding scheme is used for 2D representation. We comparatively analyze these representations for their individual performance and attempt to achieve performance improvement using the proposed multilevel matcher that utilizes fixed score level combination scheme to integrate information. Our experiments on a database of 108 subjects achieved significant improvement in performance with the integration of 3D features as compared to the case when 2D palmprint features alone are employed. We also present experimental results to demonstrate that the proposed biometric system is extremely difficult to circumvent, as compared to the currently proposed palmprint authentication approaches in the literature.     

掌纹ROI分割算法的研究与实现

掌纹感兴趣区(ROI)分割是掌纹识别的关键步骤,目前掌纹分割方法主要存在定位点不易确定和同类图像ROI提取偏移度较大等问题,为改善这些问题,提出一种新的ROI分割算法。首先确定手掌图像中的两个指谷点;然后利用手掌轮廓特定区域边界点拟合直线,以该直线为基准,以固定角度的方式建立直角坐标系,利用指谷点找到掌纹信息丰富的区域,确定掌纹的ROI,最后提取特征矢量进行匹配识别。实验结果表明,该算法分割掌纹ROI的准确度高、速度快,对同类图像分割的偏移度更小,掌纹ROI的提取率达98.2%,掌纹正确识别率提高了3%左右,为基于掌纹的身份认证系统的实现提供了理论和实验依据。     

Palmprint authentication using a symbolic representation of images

A new branch of biometrics, palmprint authentication, has attracted increasing amount of attention because palmprints are abundant of line features so that low resolution images can be used. In this paper, we propose a new texture based approach for palmprint feature extraction, template representation and matching. An extension of the SAX (Symbolic Aggregate approXimation), a time series technology, to 2D data is the key to make this new approach effective, simple, flexible and reliable. Experiments show that by adopting the simple feature of grayscale information only, this approach can achieve an equal error rate of 0.3%, and a rank one identification accuracy of 99.9% on a 7752 palmprint public database. This new approach has very low computational complexity so that it can be efficiently implemented on slow mobile embedded platforms. The proposed approach does not rely on any parameter training process and therefore is fully reproducible. What is more, besides the palmprint authentication, the proposed 2D extension of SAX may also be applied to other problems of pattern recognition and data mining for 2D images.