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...     

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.     

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。     

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.     

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.     

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.     

Computational Intelligence-Based Biometric Technologies

Computational intelligence (CI) technologies are robust, can be successfully applied to complex problems, are efficiently adaptive, and usually have a parallel computational architecture. For those reasons they have been proved to be effective and efficient in bio-metric feature extraction and biometric matching tasks, sometimes used in combination with traditional methods. In this article, we briefly survey two kinds of major applications of CI in biometric technologies, CI-based feature extraction and CI-based biometric matching. Varieties of evolutionary computation and neural networks techniques have been successfully applied to biometric data representation and dimensionality reduction. CI-based methods, including neural network and fuzzy technologies, have also been extensively investigated for biometric matching. CI-based biometric technologies are powerful when used in the representation and recognition of incomplete biometric data, discriminative feature extraction, biometric matching, and online template updating, and promise to have an important role in the future development of biometric technologies     

生物特征识别技术概述

生物特征识别技术是为了进行身份验证而采用自动技术测量其身体特征或是个人行为特点,并将这些特征或特点与数据库的模板数据进行比较,完成认证的一种解决方案。人的生物特征是惟一的,生物特征识别技术的基本工作就是对这些基本的、可测量或可自动识别和验证的生理特征进行统计分析。过程大多包括四个步骤:图像获取、抽取特征、比较和匹配。生物特征识别系统捕捉到生物特征的样品,惟一的特征将会被提取并且被转化成数字的符号,这些符号被存成个人的特征模板,模板可能会在识别系统中,也可能在各种各样的存储器中,如计算机的数据库、智能卡或…     

Authentication by Biometric Verification

Biometric systems encompass the development of capture devices to acquire biometric signals, software algorithms for effective identification, and databases that store biometric data for comparison.