Heart sound as a biometric

In this paper, we propose a novel biometric method based on heart sound signals. The biometric system comprises an electronic stethoscope, a computer equipped with a sound card and the software application. Our approach consists of a robust feature extraction scheme which is based on cepstral analysis with a specified configuration, combined with Gaussian mixture modeling. Experiments have been conducted to determine the relationship between various parameters in our proposed scheme. It has been demonstrated that heart sounds should be processed within segments of 0.5 s and using the full resolution in frequency domain. Also, higher order cepstral coefficients that carry information on the excitation proved to be useful. A preliminary test of 128 heart sounds from 128 participants was collected to evaluate the uniqueness of the heart sounds. The HTK toolkit produces a 99% recognition rate with only one mismatch. Next, a more comprehensive test consisting almost 1000 heart sounds collected from 10 individuals over a period of 2 months yields a promising matching accuracy of 96% using the proposed feature and classification algorithm. A real-time heart sound authentication system is then built and can be used in two modes: to identify a particular individual or to verify an individual's claimed identity.     

Finger surface as a biometric identifier

We present a novel approach for personal identification and identity verification which utilizes 3D finger surface features as a biometric identifier. Using 3D range images of the hand, a surface representation for the index, middle, and ring finger is calculated and used for comparison to determine subject similarity. We use the curvature based shape index to represent the fingers’ surface. Gallery and probe shape index signatures are compared using the normalized correlation coefficient to compute a match score. A large unique database of hand images supports the research. We use data sets obtained over time to examine the performance of each individual finger surface as a biometric identifier as well as the performance obtained when combining them. Both identification and verification experiments are conducted. In addition, probe and gallery sets sizes are increased to further improve recognition performance in our experiments. Our approach yields good results for a first-of-its-kind biometric technique, indicating that this approach warrants further research.     

Lip as biometric and beyond: a survey

Owing to the recent fuel in investigation of lip as a biometrics trait both in physiological and behavioral aspect, and in healthcare application, it is imperative to comprehensively study and document the literature. Hence, in this work we enlist the works related to the lip as a biometirc trait and other applications-based on its behavior and physiology. Consequently, in this survey we first discuss the lip anatomy that permits to identify the physiological and behavioral properties. Followed by the list of challenges to characterize lip behavior and physiology. Next, we proceed to provide an insight of several inspiring ideas proposed in the literature to establish lip behavior and physiology for identity and other applications such as health monitoring and forensic. Whilst, it can be concluded at the end of the survey that their are several open research area and unsolved issues, which we discuss in details at the end of this survey to attract the attention of researchers.

     

Proposal on a multifactor biometric authentication method based on cryptosystem keys containing biometric signatures

Conventional biometric authentication methods have a serious problem – it is easy to steal the biological information of another person, such as fingerprints, iris patterns, and facial forms. Accordingly, it is essential to protect authentication systems from an attack like impersonation with an artificial finger. To solve this problem, in this paper, we would like to propose a multifactor biometric authentication method based on cryptosystem keys containing biometric signatures. This paper has two new aspects: one is to propose a method for embedding two kinds of data – personal biological information and a confidential item corresponding to a conventional secret key – into a cryptosystem key to detect any impersonation even if the biological information is stolen, and the other is to demonstrate the safety of the method, a factor which has never been referenced. From a privacy protection point of view, we believe that the multifactor biometric authentication method is of great significance. Moreover, it can economically establish certification authorities having the biological information. Meanwhile, in order to uniquely embed the biological information into the cryptosystem key, an algorithm is needed to uniquely select a representative template as described in this paper. In this paper, we propose only the concept of this algorithm and hope that concrete methodologies will be studied in the future.     

Lessons from collecting a million biometric samples

Over the past decade, independent evaluations have become commonplace in many areas of experimental computer science, including face and gesture recognition. A key attribute of many successful independent evaluations is a curated data set. Desired aspects associated with these data sets include appropriateness to the experimental design, a corpus size large enough to allow statistically rigorous characterization of results, and the availability of comprehensive metadata that allow inferences to be made on various data set attributes. In this paper, we review a ten-year biometric sampling effort that enabled the creation of several key biometrics challenge problems. We summarize the design and execution of data collections, identify key challenges, and convey some lessons learned.     

Towards a measure of biometric feature information

This paper develops an approach to measure the information content of a biometric feature representation. We define biometric information as the decrease in uncertainty about the identity of a person due to a set of biometric measurements. We then show that the biometric feature information for a person may be calculated by the relative entropy D(p||q){D(p\|q)} between the population feature distribution q and the person’s feature distribution p. The biometric information for a system is the mean D(p||q){D(p\|q)} for all persons in the population. In order to practically measure D(p||q){D(p\|q)} with limited data samples, we introduce an algorithm which regularizes a Gaussian model of the feature covariances. An example of this method is shown for PCA and Fisher linear discriminant (FLD) based face recognition, with biometric feature information calculated to be 45.0 bits (PCA), 37.0 bits (FLD) and 55.6 bits (fusion of PCA and FLD features). Finally, we discuss general applications of this measure.     

BiolD: a multimodal biometric identification system

Biometric identification systems, which use physical features to check a person's identity, ensure much greater security than password and number systems. Biometric features such as the face or a fingerprint can be stored on a microchip in a credit card, for example. A single feature, however, sometimes fails to be exact enough for identification. Another disadvantage of using only one feature is that the chosen feature is not always readable. Dialog Communication Systems (DCS AG) developed BioID, a multimodal identification system that uses three different features-face, voice, and lip movement-to identify people. With its three modalities, BioID achieves much greater accuracy than single-feature systems. Even if one modality is somehow disturbed-for example, if a noisy environment drowns out the voice-the ether two modalities still lead to an accurate identification. This article goes into detail about the system functions, explaining the data acquisition and preprocessing techniques for voice, facial, and lip imagery data. The authors also explain the classification principles used for optical features and the sensor fusion options (the combinations of the three results-face, voice, lip movement-to obtain varying levels of security)     

Hiding biometric data

With the wide spread utilization of biometric identification systems, establishing the authenticity of biometric data itself has emerged as an important research issue. The fact that biometric data is not replaceable and is not secret, combined with the existence of several types of attacks that are possible in a biometric system, make the issue of security/integrity of biometric data extremely critical. We introduce two applications of an amplitude modulation-based watermarking method, in which we hide a user's biometric data in a variety of images. This method has the ability to increase the security of both the hidden biometric data (e.g., eigen-face coefficients) and host images (e.g., fingerprints). Image adaptive data embedding methods used in our scheme lead to low visibility of the embedded signal. Feature analysis of host images guarantees high verification accuracy on watermarked (e.g., fingerprint) images.     

Federal biometric technology legislation

Voters in the United States expect the government to deliver more services to an increasing population in a more efficient, cost-effective, and fraud-free manner, while limiting the size and scope of the governmental infrastructure. Encouraged or mandated by federal legislation, governmental agencies at all levels have turned to technology in an attempt to meet these competing requirements. However, the very personal nature of this technology raises concerns about its potential impact on personal freedoms. Some people, however, are concerned with the potential impact that government use of these technologies might have on personal freedoms. In contrast with more impersonal alternatives like passwords or PINs, perhaps it is the very personal nature of biometric identification that raises these concerns. This article explains the varied areas of human life that will be affected by these technological advances: commercial licenses, immigration, employment eligibility, and welfare. The author believes the government's interest in biometric technologies is motivated by the desire to improve the delivery of services to citizens by increasing efficiency and convenience, while decreasing costs and fraud     

多模式生物特征识别的身份验证系统

提出了一种有效的基于多模式生物特征识别的嵌入式身份验证系统,介绍了系统的流程和硬件组成,以及虹膜和掌纹两种生物特征的融合算法及其优化.该算法在图像层对虹膜和掌纹两种生物特征图像进行融合,既融合了两种模式的生物特征信息,又有效地减小了特征模版的大小,与其他传统的生物特征识别算法相比,该算法低运算复杂度的特点使其很适合在嵌入式系统实现.实验结果表明,该多模式生物特征识别身份验证系统准确、有效、安全.     

Validating a biometric authentication system: sample size requirements

Authentication systems based on biometric features (e.g., fingerprint impressions, iris scans, human face images, etc.) are increasingly gaining widespread use and popularity. Often, vendors and owners of these commercial biometric systems claim impressive performance that is estimated based on some proprietary data. In such situations, there is a need to independently validate the claimed performance levels. System performance is typically evaluated by collecting biometric templates from n different subjects, and for convenience, acquiring multiple instances of the biometric for each of the n subjects. Very little work has been done in 1) constructing confidence regions based on the ROC curve for validating the claimed performance levels and 2) determining the required number of biometric samples needed to establish confidence regions of prespecified width for the ROC curve. To simplify the analysis that addresses these two problems, several previous studies have assumed that multiple acquisitions of the biometric entity are statistically independent. This assumption is too restrictive and is generally not valid. We have developed a validation technique based on multivariate copula models for correlated biometric acquisitions. Based on the same model, we also determine the minimum number of samples required to achieve confidence bands of desired width for the ROC curve. We illustrate the estimation of the confidence bands as well as the required number of biometric samples using a fingerprint matching system that is applied on samples collected from a small population     

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.     

A biometric identity-based signcryption scheme

Signcryption is a high performance cryptographic primitive that fulfills both the functions of digital signature and public key encryption simultaneously, at a cost significantly lower than that required by the traditional signature-then-encryption approach. In this paper, we introduce biometrics into identity-based signcryption. We formalize the notion of biometric identity-based signcryption and propose an efficient biometric identity-based signcryption scheme that uses biometric information to construct the public key. We prove that our scheme satisfies confidentiality and unforgeability in the random oracle model. We show that both the computational costs and the communication overheads of our scheme are lower than those of the signature-then-encryption approach.     

Privacy for biometric identification information

Zusammenfassung  Die Autoren stellen ein Verfahren vor, das durch den Einsatz von Hashfunktionen, Fehlerkorrekturcodes und Zufallszahlen biometrische Referenzdaten vor unberechtigtem Zugriff schützt und deren Sperrung erlaubt — unabh?ngig von der natürlichen Begrenzung der Anzahl nutzbarer biometrischer Merkmale eines Menschen. Tom Kevenaar Philips priv-ID Netherlands Michiel van der Veen Philips priv-ID Netherlands Xuebing Zhou Fraunhofer Institute for Computer Graphics Research (IGD), Germany Christoph Busch Hochschule Darmstadt, Germany / Gj?vik University College, Norway     

Likelihood ratio-based biometric score fusion

Multibiometric systems fuse information from different sources to compensate for the limitations in performance of individual matchers. We propose a framework for the optimal combination of match scores that is based on the likelihood ratio test. The distributions of genuine and impostor match scores are modeled as finite Gaussian mixture model. The proposed fusion approach is general in its ability to handle 1) discrete values in biometric match score distributions, 2) arbitrary scales and distributions of match scores, 3) correlation between the scores of multiple matchers, and 4) sample quality of multiple biometric sources. Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.     

生物特征识别技术之虹膜识别

人眼睛的外观图由巩膜、虹膜、瞳孔三部分构成。巩膜即眼球外围的白色部分,约占总面积的30%;眼睛中心为瞳孔部分,约占5%;虹膜位于巩膜和瞳孔之间,包含了最丰富的纹理信息,占据65%。外观上看,虹膜由许多腺窝、皱褶、色素斑等构成(见图1),是人体中最独特的结构之一。虹膜的形成由遗传基因决定,人体基因表达决定了虹膜的形态、生理、颜色和总的外观。人发育到八个月左右,虹膜就基本上发育到了足够尺寸,进入了相对稳定的时期。除非极少见的反常状况、身体或精神上大的创伤才可能造成虹膜外观上     

An introduction evaluating biometric systems

On the basis of media hype alone, you might conclude that biometric passwords will soon replace their alphanumeric counterparts with versions that cannot be stolen, forgotten, lost, or given to another person. But what if the actual performance of these systems falls short of the estimates? The authors designed this article to provide sufficient information to know what questions to ask when evaluating a biometric system, and to assist in determining whether performance levels meet the requirements of an application. For example, a low-performance biometric is probably sufficient for reducing-as opposed to eliminating-fraud. Likewise, completely replacing an existing security system with a biometric-based one may require a high-performance biometric system, or the required performance may be beyond what current technology can provide. Of the biometrics that give the user some control over data acquisition, voice, face, and fingerprint systems have undergone the most study and testing-and therefore occupy the bulk of this discussion. This article also covers the tools and techniques of biometric testing