生物特征模板保护

对当前国内外生物特征模板保护技术发展的现状进行综述和探索,对该方向的研究内容进行详细的梳理和分类.首先阐述传统生物特征识别系统存在的本质缺陷和易于遭受到的攻击的形式,进而从理论上引出了生物特征模板保护的必要性及其难点所在.然后以模板保护算法的具体操作方式为分类标准,详细阐述了当前在这个领域出现的比较有代表性的算法,诸如Biohashing和模糊保险箱(Fuzzy Vault)等.通过实验验证了Biohashing算法的优势和缺陷,并且在FVC2002 DB2数据库上对提出的改进Fuzzy Vault算法进     

Updating the Biometric Template by Assessing the Quality of the Original Data

Journal of Computer and Systems Sciences International - Biometric features used in recognition systems are subject to aging. In addition, there may be variations in working conditions that are not...     

生物特征识别模板保护综述

生物特征识别(biometric authentication, BA)已经成为一种重要的身份鉴别手段,但当前部署的很多BA系统在保护用户生物特征数据的安全性和隐私性方面考虑不足,成为阻碍BA技术推广应用的一个关键障碍.BA系统可能面临来自软件和硬件的多种攻击,针对生物特征模板的攻击是其中最常见的一种．已经有很多技术文献致力于应对这种类型的攻击,但现有的综述性文献存在论述不全面或内容冲突等问题．为系统总结针对生物特征模板的攻击与保护技术,首先介绍了BA系统的相关概念、体系架构以及安全性与隐私性的内涵,然后阐述了BA系统面临的典型模板攻击方法.随后,将BA系统模板保护技术归纳为基于变换的方法和基于加密的方法2个类别,阐述并分析了每个类别中的经典方法与新兴技术．最后,指出了构建安全BA系统可能面临的几个主要困难与可能的解决思路.     

基于Biohashing的指纹模板保护算法

针对Biohashing指纹模板保护算法存在用户令牌泄露时识别性能严重退化的问题,提出了两种改进的Biohashing指纹模板保护算法.该算法在指纹数据预处理的基础上,采用可变的步长参数和滑动窗口产生固定大小的二值特征矩阵,减少了指纹数据特征值之间的关联性,离散化的非线性处理过程能够获得更大的密钥空间,有效提高了算法的安全性.理论分析和实验结果表明,改进算法具有更好的安全和识别性能.     

Standardisierung in der Gesundheits-Telematik

Zusammenfassung  Die Schaffung einer fl?chendeckenden Rahmenarchitektur und Infrastruktur für das deutsche Gesundheitswesen ist im GKV-Modernisierungegestz festgeschrieben und wird in den n?chsten Jahren umgesetzt werden. Wiederkehrende Stichworte sind hierbei Interoperabilit?t und Kompatibilit?t. In einem Atemzug mit diesen ist immer auch die Standardisierung zu nennen.     

Standardisierung der europaweiten Berichterstattung im Rahmen der Messung der Gesamtenergieeffizienz von Gebäuden

The introduction of the European Directive on the Energy Performance of Buildings is a further step taken by the European Union (EU) in order to harmonise the legislation within the 27 member states. However, achieving a high level of legal standardisation, each member country is left alone as far as technical harmonisation is being considered. But this situation leads to different implementations of the directive across the member states and it complicates the comparability across the EU. A supporting approach can be observed in the Basel II and International Financial Reporting Standards (IFRS) implementation of the EU by the Committee of European Banking Supervisors (CEBS) using the eXtensible Business Reporting Language (XBRL). The use of XBRL, being constructed mainly for business reporting data, can be also considered in various other areas. The paper presents an approach to use XBRL as a mean of standardisation for the reporting concerning the energy performance of buildings. The paper addresses the issue of such an approach and can be generalised and applied in other domains.     

Biometric authentication

In this age of digital impersonation, biometric techniques are being used increasingly as a hedge against identity theft. The premise is that a biometric - a measurable physical characteristic or behavioral trait - is a more reliable indicator of identity than legacy systems such as passwords and PINs. There are three general ways to identify yourself to a computer system, based on what you know, what you have, or who you are. Biometrics belong to the "who you are" class and can be subdivided into behavioral and physiological approaches. Behavioral approaches include signature recognition, voice recognition, keystroke dynamics, and gait analysis. Physiological approaches include fingerprints; iris and retina scans; hand, finger, face, and ear geometry; hand vein and nail bed recognition; DNA; and palm prints. In this article, we focus on the two most popular biometric techniques: fingerprints and iris scans.     

基于随机映射技术的声纹识别模板保护

声纹识别实现了一种非接触式、不易伪造、可远程认证的简便快捷的生物特征认证方式,这种生物特征认证方式不是完全安全的,因为在身份认证过程中,将用户数据存储在第三方会带来许多安全和隐私问题.为了解决这一挑战,研究了基于身份向量(i-Vector)和线性判别分析技术(LDA)的声纹模板保护方案,提出一种改进的随机映射技术.利用改进的随机映射算法对声纹特征进行随机化处理,构造了一个声纹识别的模板保护方案,允许用户在随机域注册并完成声纹识别.随后,基于公开的中文语音数据集AISHELL对所提出的方案进行了实验仿真.结果表明:该方案不会对声纹识别的准确性造成显著影响,且实现了声纹模板的保密比对,能够有效保证语音数据的安全.     

The Biometric Menagerie

It is commonly accepted that users of a biometric system may have differing degrees of accuracy within the system. Some people may have trouble authenticating, while others may be particularly vulnerable to impersonation. Goats, wolves, and lambs are labels commonly applied to these problem users. These user types are defined in terms of verification performance when users are matched against themselves (goats) or when matched against others (lambs and wolves). The relationship between a user's genuine and impostor match results suggests four new user groups: worms, doves, chameleons, and phantoms. We establish formal definitions for these animals and a statistical test for their existence. A thorough investigation is conducted using a broad range of biometric modalities, including 2D and 3D faces, fingerprints, iris, speech, and keystroke dynamics. Patterns that emerge from the results expose novel, important, and encouraging insights into the nature of biometric match results. A new framework for the evaluation of biometric systems based on the biometric menagerie, as opposed to collective statistics, is proposed.     

BinaryFace:基于深层卷积神经网络的人脸模板保护模型

随着生物识别技术的广泛应用,人们越来越担心生物模板信息的安全性和隐私性。为此人们提出很多生物模板信息的保护算法,但其一般需要牺牲可识别性来换取高安全性。为了在保证高安全性的同时尽可能提高可识别性,本文提出一种新的由特征转换和生物加密组成的二阶段人脸模板保护方案。在特征转换阶段,基于VGGFace提出一种新的基于卷积神经网络的BinaryFace网络,通过设计新的随机正交映射矩阵、量化损失函数和最大熵损失函数实现人脸模板的二进制转换。同时为了减少网络参数,设计新的深度可分离瓶颈卷积层,BinaryFace相比VGGFace在参数和浮点数（Flops）上分别减少约75%和约35%。在生物加密阶段,将人脸二进制模板转换中随机正交映射生成的纠错码输入模糊承诺方案,生成加密的人脸模板并存储到数据库中。在验证阶段,通过相同的流程恢复出纠错码,并与原始纠错码进行哈希校验得到最终的匹配结果。在评测阶段,本文提出的方法在CMU-PIE、FEI、Color FERET等3个数据集上,相比之前的工作在GAR上有约6.5%的提升,同时将EER降低了约4倍。     

Biometric template protection

The objective of this paper is to outline the potential threats to security and privacy that are associated with biometric-enabled applications, to summarize the resulting requirements to ensure secure and private handling of personal data, and to explain why standardization in this area is required. The currently ongoing standardization efforts in ISO/IEC in the area of biometric template protection are described.     

Biometric walk recognizer

This paper presents an approach to gait recognition based on a single consumer accelerometer, built in most present mobile devices. It does not propose a completely novel algorithm, but rather investigates better ways to exploit the Dynamic Time Warping (DTW), which is still one of the most used at present in literature. To this aim, the paper presents both a new segmentation algorithm to split the gait signal into cycles/steps, and investigates the best way to use the possibly segmented signal for recognition. Summarizing, the first contribution of the present work is the proposal of a new segmentation algorithm for the gait signal, which does not require any pre-processing, either interpolation or noise reduction, to enhance the original signal, and its comparison with two other state-of-the-art step segmentation algorithms. The second contribution is related to the extensive tests performed with the five different investigated matching methods. The tests are carried out exploiting all compared segmentation algorithms and three different datasets, collected using different sensors: the originally exploited BWR dataset, that includes walk templates from 30 volunteers, and two huge datasets used for this kind of testing, namely the ZJU-gaitacc and the OU-ISIR Inertial Sensor Database. Tests have been performed in both verification mode, either single-template or multiple-template, and identification mode, both closed and open set. The latter is rarely found in literature though representing the most frequently predictable applicative setting. It is worth underlining that the final goal is to allow using low-cost, built-in sensors that nowadays equip most smartphones. The best result in closed set identification, which is the identification mode usually reported in literature, is achieved using the most constrained method, i.e., limiting the walks in the gallery and in the probe to have a similar number of steps. It reaches ≈93 % of Recognition Rate (RR) on ZJU-gaitacc dataset. The best result obtained with methods exploiting segmentation to overcome the mentioned limitation reaches ≈83 % of Recognition Rate (RR) on the same dataset, using our proposed algorithm. The best results in verification is achieved using multiple templates per user, again without segmentation, with an Equal Error Rate (EER) of 0.09, while the best results with segmentation is achieved again with our algorithm and is and EER of 0.10. This is a very good result for a soft biometrics as gait if often considered. As expected, open set identification achieves lower performance.     

Biometric dispersion matcher

In this paper we propose a new classifier called a dispersion matcher. Our proposal is especially well adapted to those scenarios where a large number of classes and a small number of samples per class are available for training. This is the situation of biometric systems where just three to five measures per person are acquired during enrollment. This is just the opposite situation of other pattern recognition applications where a small number of classes and a large amount of training samples are available, such as handwritten digit recognition (10 classes) for ZIP code identification.The dispersion matcher trains a quadratic discriminant classifier to solve the dichotomy “Do these two feature vectors belong to the same person?”. In this way, we solve an important set of topics: (a) we can classify an open world problem and we do not need to train the model again if a new user is added, (b) we find a natural solution for feature selection, (c) experimental results with a priori threshold provides good results.We evaluate the proposed system with hand-geometry and face recognition problems (identification and verification). In hand geometry, we get a minimum detection cost function (DCF) for verification of 0.21% and a maximum identification rate of 99.1%, which compares favorably with other state-of-the-art methods. In face verification we achieve 5.59% DCF and 92.77% identification rate, which also compares favorably with the literature.     

生物特征识别技术研究进展

生物特征身份鉴别方法是根据人体各器官或个人行为之间的差异来鉴别个人身份。随着计算机技术的迅速发展,生物特征鉴别技术将在军事和人们的日常生活等各个方面得到广泛的应用。文章介绍了生物特征的概念及基于生物特征识别的身份鉴别技术,对不同的识别方法的原理、特征做了较详细的分析与评价。对生物特征身份鉴别技术的应用前景和发展方向也做了分析。