Document scanners for minutiae-based palmprint recognition: a feasibility study

Highly expensive capturing devices and barely existent high-resolution palmprint datasets have slowed the development of forensic palmprint biometric systems in comparison with civilian systems. These issues are addressed in this work. The feasibility of using document scanners as a cheaper option to acquire palmprints for minutiae-based matching systems is explored. A new high-resolution palmprint dataset was established using an industry-standard Green Bit MC517 scanner and an HP Scanjet G4010 document scanner. Furthermore, a new enhancement algorithm to attenuate the negative effect of creases in the process of minutiae extraction is proposed. Experimental results highlight the potentialities of document scanners for forensic applications. Advantages and disadvantages of both technologies are discussed in this context as well.

     

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.     

一种用于掌纹识别的线特征表示和匹配方法

作为一种较新的生物特征,掌纹可用来进行人的身份识别.在用于身份识别的诸多特征中,掌纹线,包括主线和皱褶,是最重要的特征之一.本文为掌纹识别提出一种有效的掌纹线特征的表示和匹配方法,该方法定义了一个矢量来表示一个掌纹上的线特征,该矢量称为线特征矢量(1ine feature vector,简称LFV).线特征矢量是用掌纹线上各点的梯度大小和方向来构造的.该矢量不但含有掌纹线的结构信息,而且还含有这些线的强度信息,因而,线特征矢量不但能区分具有不同线结构的掌纹,同时也能区分那些具有相似的线结构但各线强度分布不同的掌纹.在掌纹匹配阶段,用互相关系数来衡量不同线特征矢量的相似性.实验表明,LFV方法无论是在速度、精度,还是在存储量方面都能满足联机生物识别的要求.     

Dual-source discrimination power analysis for multi-instance contactless palmprint recognition

Due to the benefits of palmprint recognition and the advantages of biometric fusion systems, it is necessary to study multi-source palmprint fusion systems. Unfortunately, the research on multi-instance palmprint feature fusion is absent until now. In this paper, we extract the features of left and right palmprints with two-dimensional discrete cosine transform (2DDCT) to constitute a dual-source space. Normalization is utilized in dual-source space to avoid the disturbance caused by the coefficients with large absolute values. Thus complicated pre-masking is needless and arbitrary removing of discriminative coefficients is avoided. Since more discriminative coefficients can be preserved and retrieved with discrimination power analysis (DPA) from dual-source space, the accuracy performance is improved. The experiments performed on contactless palmprint database confirm that dual-source DPA, which is designed for multi-instance palmprint feature fusion recognition, outperforms single-source DPA.     

Analysis of brute-force break-ins of a palmprint authentication system.

Biometric authentication systems are widely applied because they offer inherent advantages over classical knowledge-based and token-based personal-identification approaches. This has led to the development of products using palmprints as biometric traits and their use in several real applications. However, as biometric systems are vulnerable to replay, database, and brute-force attacks, such potential attacks must be analyzed before biometric systems are massively deployed in security systems. This correspondence proposes a projected multinomial distribution for studying the probability of successfully using brute-force attacks to break into a palmprint system. To validate the proposed model, we have conducted a simulation. Its results demonstrate that the proposed model can accurately estimate the probability. The proposed model indicates that it is computationally infeasible to break into the palmprint system using brute-force attacks.     

Fast fingerprint identification for large databases

Fingerprint matching has emerged as an effective tool for human recognition due to the uniqueness, universality and invariability of fingerprints. Many different approaches have been proposed in the literature to determine faithfully if two fingerprint images belong to the same person. Among them, minutiae-based matchers highlight as the most relevant techniques because of their discriminative capabilities, providing precise results. However, performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms.In this paper, we propose a distributed framework for fingerprint matching to tackle large databases in a reasonable time. It provides a general scheme for any kind of matcher, so that its precision is preserved and its time of response can be reduced.To test the proposed system, we conduct an extensive study that involves both synthetic and captured fingerprint databases, which have different characteristics, analyzing the performance of three well-known minutiae-based matchers within the designed framework. With the available hardware resources, our distributed model is able to address up to 400 000 fingerprints in approximately half a second. Additional details are provided at http://sci2s.ugr.es/ParallelMatching.     

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.     

Tuning the ensemble selection process of schema matchers

Schema matching is the task of providing correspondences between concepts describing the meaning of data in various heterogeneous, distributed data sources. It is recognized to be one of the basic operations required by the process of data and schema integration and its outcome serves in many tasks such as targeted content delivery and view integration. Schema matching research has been going on for more than 25 years now. An interesting research topic, that was largely left untouched involves the automatic selection of schema matchers to an ensemble, a set of schema matchers. To the best of our knowledge, none of the existing algorithmic solutions offer such a selection feature. In this paper we provide a thorough investigation of this research topic. We introduce a new heuristic, Schema Matcher Boosting (SMB). We show that SMB has the ability to choose among schema matchers and to tune their importance. As such, SMB introduces a new promise for schema matcher designers. Instead of trying to design a perfect schema matcher, a designer can instead focus on finding better than random schema matchers. For the effective utilization of SMB, we propose a complementary approach to the design of new schema matchers. We separate schema matchers into first-line and second-line matchers. First-line schema matchers were designed by-and-large as applications of existing works in other areas (e.g., machine learning and information retrieval) to schemata. Second-line schema matchers operate on the outcome of other schema matchers to improve their original outcome. SMB selects matcher pairs, where each pair contains a first-line matcher and a second-line matcher. We run a thorough set of experiments to analyze SMB ability to effectively choose schema matchers and show that SMB performs better than other, state-of-the-art ensemble matchers.     

基于语言和约束匹配器融合的元素匹配研究

在模式匹配方面已经出现了许多使用于特定应用领域的部分自动匹配方法,这种匹配方法结合了多种匹配技术以便能够在大规模的多样匹配环境中得到高的匹配率。提出了一种基于模式的元素匹配方法,它融合了语言和约束匹配器,使用了复合元素名称匹配器和神经网络匹配器,结合基于语言的匹配算法和最大优先策略的原则,以多重标准条件下复合名称匹配器的结果作为约束对模式元素进行归类。通过组合使用复合名称匹配器和神经网络匹配器,使得本方法可以应用于更复杂的匹配环境。     

Automated flexion crease identification using internal image seams

Palmar flexion crease recognition is a palmprint identification method for verifying biometric identity. This paper proposes a method of automated flexion crease recognition that can be used to identify palmar flexion creases in online palmprint images. A modified image seams algorithm is used to extract the flexion creases, and a matching algorithm, based on kd-tree nearest neighbour searching, is used to calculate the similarity between them. Experimental results show that in 1000 images from 100 palms, when compared to manually identified flexion creases, a genuine acceptance rate of 100% can be achieved, with a false acceptance rate of 0.0045%.     

A hybrid wavelet-based fingerprint matcher

In this work we present a hybrid fingerprint matcher system based on the multi-resolution analysis of the fingerprint pattern and on minutiae-based registration module. Two fingerprints are first aligned using their minutiae, then the images are divided in sub-windows and each sub-window is decomposed into frequency sub-bands at different decomposition levels using a set of wavelet functions, finally a distinct classifier is trained on each sub-band to distinguish matching pairs of fingerprint from non-matching one (defining a two-class matching problem). The features extracted for the matching are the standard deviation of the image convolved with 16 Gabor filters. The selection among the pool of matchers, is performed by running Sequential Forward Floating Selection. The retained matchers are weighted by a novel localized quality measure and combined by a fusion rule. Extensive experiments conducted over the four FVC2002 fingerprint databases show the effectiveness of the proposed approach.     

Likelihood ratio based features for a trained biometric score fusion

In this work, we present a novel trained method for combining biometric matchers at the score level. The new method is based on a combination of machine learning classifiers trained using the match scores from different biometric approaches as features. The parameters of a finite Gaussian mixture model are used for modelling the genuine and impostor score densities during the fusion step.Several tests on different biometric verification systems (related to fingerprints, palms, fingers, hand geometry and faces) show that the new method outperforms other trained and non-trained approaches for combining biometric matchers.We have tested some different classifiers, support vector machines, AdaBoost of neural networks, and their random subspace versions, demonstrating that the choice for the proposed method is the Random Subspace of AdaBoost.     

Palmprint matching using feature points and SVD factorisation

In this paper, a new palmprint matching system based on the extraction of feature points is suggested. Using a scale-space representation, the points in question are corners formed by the intersection of creases and lines. Unlike minutiae, such points can still be extracted even on low resolution palmprints. Matching is carried out using an SVD factorisation of a proximity matrix and takes account of the coordinates of the detected points and their local texture. Our experiments have yielded some very good results evidenced by an EER of 0.10%.     

A supervised method to discriminate between impostors and genuine in biometry

In this paper, we describe a supervised technique that allows to develop a more robust biometric system with respect to those based directly on the similarities of the biometric matchers or on the similarities normalised by the unconstrained cohort normalisation.In order to discriminate between genuine and impostors a quadratic discriminant classifier is trained using four features: the similarities of the biometric matcher; the similarities of the biometric matcher after the unconstrained cohort normalisation (UCN); the average scores among the test pattern and the users that belong to the background model; the difference between the user-specific threshold and the user-independent threshold.The proposed technique is validated by extensive experiments carried out on several biometric datasets (palm, finger, 2D and 3D faces, and ear). The experimental results demonstrate that the capabilities provided by our supervised method can significantly improve the performance of a standard biometric matcher or the performance of the standard UCN.     

掌纹识别算法综述

掌纹识别作为一种新兴的生物识别技术, 近年来得到了广泛的关注与研究. 与其他生物特征相比, 掌纹有许多独特的优势,包括识别率高、采集设备价格低廉、用户可接受性好等. 这些优势使得掌纹识别成为一种有着广泛应用前景的生物识别方法. 本文首先介绍了掌纹的特点、掌纹的采集设备和预处理方法, 之后详细介绍了近几年来提出的各种掌纹识别方法. 根据特征提取以及匹配方法的不同, 本文将掌纹识别方法分为基于结构的、基于子空间的、基于编码的和基于统计的四类方法. 在回顾和比较了各种算法的特点之后, 对未来的掌纹识别方法的发展方向作了展望.     

Palmprint verification based on principal lines

In this paper, we propose a novel palmprint verification approach based on principal lines. In feature extraction stage, the modified finite Radon transform is proposed, which can extract principal lines effectively and efficiently even in the case that the palmprint images contain many long and strong wrinkles. In matching stage, a matching algorithm based on pixel-to-area comparison is devised to calculate the similarity between two palmprints, which has shown good robustness for slight rotations and translations of palmprints. The experimental results for the verification on Hong Kong Polytechnic University Palmprint Database show that the discriminability of principal lines is also strong.     

Two novel characteristics in palmprint verification: datum point invariance and line feature matching

As the first attempt of automatic personal identification by palmprint, in this paper, two novel characteristics, datum point invariance and line feature matching, are presented in palmprint verification. The datum points of palmprint, which have the remarkable advantage of invariable location, are defined and their determination using the directional projection algorithm is developed. Then, line feature extraction and line matching are proposed to detect whether a couple of palmprints are from the same palm. Various palmprint images have been tested to illustrate the effectiveness of the palmprint verification with both characteristics.     

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.     

Rank Aggregation for Automatic Schema Matching

Schema matching is a basic operation of data integration, and several tools for automating it have been proposed and evaluated in the database community. Research in this area reveals that there is no single schema matcher that is guaranteed to succeed in finding a good mapping for all possible domains and, thus, an ensemble of schema matchers should be considered. In this paper, we introduce schema metamatching, a general framework for composing an arbitrary ensemble of schema matchers and generating a list of best ranked schema mappings. Informally, schema metamatching stands for computing a "consensus" ranking of alternative mappings between two schemata, given the "individual" graded rankings provided by several schema matchers. We introduce several algorithms for this problem, varying from adaptations of some standard techniques for general quantitative rank aggregation to novel techniques specific to the problem of schema matching, and to combinations of both. We provide a formal analysis of the applicability and relative performance of these algorithms and evaluate them empirically on a set of real-world schemata