Biometric Samples Research Articles

A measure of information gained through biometric systems

We propose a measure of information gained through biometric matching systems. Firstly, we discuss how the information about the identity of a person is derived from biometric samples through a biometric system, and define the “biometric system entropy” or BSE based on mutual information. We present several theoretical properties and interpretations of the BSE, and show how to design a biometric system which maximizes the BSE. Then we prove that the BSE can be approximated asymptotically by the relative entropy D(fG(x)∥fI(x)) where fG(x) and fI(x) are probability mass functions of matching scores between samples from individuals and among population. We also discuss how to evaluate the BSE of a biometric system and show experimental evaluation of the BSE of face, fingerprint and multimodal biometric systems.

E-Voting System using Multimode Bio-Metric Analysis for Authentication

countries like India Voting takes places in the form of electronic voting and paper based voting. The proposed system is an extension of the Electronic voting. It has major advantage by using multimode biometric analysis to identify a correct individual. As there are many problems related to unimodal biometric system, such as noisy sensor data, non- universality, lack of individuality, non-availability of invariant representations. To solve this problems we will use multimode biometric system. For this purpose, multimode biometric system will take input of two biometric trait and fuse together the biometric sample. There are many possible combinations for fusion such as Hand Geometry and IRIS pattern, fusion hand geometry and palm print, fusion of face and palm print, etc. In this paper we are taking fusion of hand geometry and iris pattern by considering the problem of non- universality. .

Bi-modal biometric authentication on mobile phones in challenging conditions

This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone. To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database. We evaluate state-of-the-art face, speaker and bi-modal authentication techniques and show that inter-session variability modelling using Gaussian mixture models provides a consistently robust system for face, speaker and bi-modal authentication. It is also shown that multi-algorithm fusion provides a consistent performance improvement for face, speaker and bi-modal authentication. Using this bi-modal multi-algorithm system we derive a state-of-the-art authentication system that obtains a half total error rate of 6.3% and 1.9% for Female and Male trials, respectively.

Person Verification using Fusion of Palm Geometry and Palm Print

This paper proposes a biometric person verification system based using hand images. This paper attempts to improve the performance of palm print-based verification system by integrating hand geometry features at different levels of fusion. In this work geometrical features such as finger lengths and finger widths are used. For extraction of the palm print features Gabor filter based approach is utilized. For matching, the newly acquired biometric samples are compared with those stored in the system database, at the enrolment stage. Unlike other bimodal biometric systems, the users do not have to undergo the inconvenience of using two different sensors since the palm print and hand geometry features can be acquired from the same image, using single capturing device at the same time. The capturing device has a resolution of 640X480 pixels. Our experimental results on the image dataset from 300 users confirm the utility of combining hand geometry features with those from palm prints using a simple image acquisition setup. It also illustrates the performance of different fusion methods such as decision level, score level. FAR of 0.28% and FRR of 2.0% is achieved for the database using score level fusion of palm geometry and palm print.

Entropy-based template analysis in face biometric identification systems

The accuracy of a biometric matching algorithm relies on its ability to better separate score distributions for genuine and impostor subjects. However, capture conditions (e.g. illumination or acquisition devices) as well as factors related to the subject at hand (e.g. pose or occlusions) may even take a generally accurate algorithm to provide incorrect answers. Techniques for face classification are still too sensitive to image distortion, and this limit hinders their use in large-scale commercial applications, which are typically run in uncontrolled settings. This paper will join the notion of quality with the further interesting concept of representativeness of a biometric sample, taking into account the case of more samples per subject. Though being of excellent quality, the gallery samples belonging to a certain subject might be very (too much) similar among them, so that even a moderately different sample of the same subject in input will cause an error. This seems to indicate that quality measures alone are not able to guarantee good performances. In practice, a subject gallery should include a sufficient amount of possible variations, in order to allow correct recognition in different situations. We call this gallery feature representativeness. A significant feature to consider together with quality is the sufficient representativeness of (each) subject’s gallery. A strategy to address this problem is to investigate the role of the entropy, which is computed over a set of samples of a same subject. The paper will present a number of applications of such a measure in handling the galleries of the different users who are registered in a system. The resulting criteria might also guide template updating, to assure gallery representativeness over time.

Critical analysis of adaptive biometric systems

Biometric-based person recognition poses a challenging problem because of large variability in biometric sample quality encountered during testing and a restricted number of enrollment samples for training. Solutions in the form of adaptive biometrics have been introduced to address this issue. These adaptive biometric systems aim to adapt enrolled templates to variations in samples observed during operations. However, despite numerous advantages, few commercial vendors have adopted auto-update procedures in their products. This is due in part to the limited understanding and limitations associated with existing adaptation schemes. In view of that the topic of adaptive biometrics has not been systematically investigated, this study works towards filling this gap by surveying the topic from a growing body of the recent literature and by providing a coherent view (critical analysis) of the limitations of the existing systems. In addition, the authors have also identified novel research directions and proposed a novel framework. The overall aim is to advance the state-of-the-art and improve the quality of discourse in this field.

Maximum Key Size and Classification Performance of Fuzzy Commitment for Gaussian Modeled Biometric Sources

Template protection techniques are used within biometric systems in order to protect the stored biometric template against privacy and security threats. A great portion of template protection techniques are based on extracting a key from, or binding a key to the binary vector derived from the biometric sample. The size of the key plays an important role, as the achieved privacy and security mainly depend on the entropy of the key. In the literature, it can be observed that there is a large variation on the reported key lengths at similar classification performance of the same template protection system, even when based on the same biometric modality and database. In this work, we determine the analytical relationship between the classification performance of the fuzzy commitment scheme and the theoretical maximum key size given as input a Gaussian biometric source. We show the effect of the system parameters such as the biometric source capacity, the number of feature components, the number of enrolment and verification samples, and the target performance on the maximum key size. Furthermore, we provide an analysis of the effect of feature interdependencies on the estimated maximum key size and classification performance. Both the theoretical analysis, as well as an experimental evaluation using the MCYT fingerprint database showed that feature interdependencies have a large impact on performance and key size estimates. This property can explain the large deviation in reported key sizes in literature.

A novel framework for automatic 3D face recognition using quality assessment

The quality of biometric samples plays an important role in biometric authentication systems because it has a direct impact on verification or identification performance. In this paper, we present ...

Measuring Biometric Sample Quality in terms of Biometric Feature Information in Iris Images

This paper develops an approach to measure the information content in a biometric feature representation of iris images. In this context, the biometric feature information is calculated using the relative entropy between the intraclass and interclass feature distributions. The collected data is regularized using a Gaussian model of the feature covariances in order to practically measure the biometric information with limited data samples. An example of this method is shown for iris templates processed using Principal‐Component Analysis‐ (PCA‐) and Independent‐Component Analysis‐ (ICA‐) based feature decomposition schemes. From this, the biometric feature information is calculated to be approximately 278 bits for PCA and 288 bits for ICA iris features using Masek′s iris recognition scheme. This value approximately matches previous estimates of iris information content.

Enhancing Identity Prediction Using a Novel Approach to Combining Hard- and Soft-Biometric Information

The effectiveness with which individual identity can be predicted in, for example, an antiterrorist scenario can benefit from seeking a broad base of identity evidence. The issue of improving performance can be addressed in a number of ways, but system configurations based on integrating different information sources (often involving more than one biometric modality) are a widely adopted means of achieving this. This paper presents a new approach to improving identification performance, where both direct biometric samples and “soft-biometric” knowledge are combined. Specifically, however, we propose a strategy based on an intelligent agent-based decision-making process, which predicts both absolute identity and also other individual characteristics from biometric samples, as a basis for a more refined and enhanced overall identification decision based on flexible negotiation among class-related agents.

Hardware Architecture Optimized for Iris Recognition

One of the remaining problems in iris recognition is the implementation of its efficient hardware systems. The difficulty arises from the fact that the methods forming part of this type of authentication process are fairly complex. Furthermore, next generation algorithms for iris recognition will become even more complex to enhance the reliability and functionality of currently used solutions. An example of such an intensive processing task is iris image quality assessment and segmentation using video signal obtained at-a-distance and on-the-move. Thus, processing robustness and predictable analysis time in such dedicated architectures may be particularly important. This paper describes a hardware system designed to analyze iris biometric data developed as a part of the work conducted by the authors on the complete iris identification system (1:N). This specialized architecture is mainly composed of digital signal processors and field-programmable gate arrays. Several issues concerning efficient biometric sample processing are discussed. Algorithms for iris recognition previously developed by the authors on a PC platform were adapted to the described architecture. The obtained results are presented, and the developed system is compared with chosen commercially available iris authentication systems.

Evaluation of a template protection approach to integrate fingerprint biometrics in a PIN-based payment infrastructure

Biometric authentication has a great potential to improve the security, reduce cost, and enhance the customer convenience of payment systems. Despite these benefits, biometric authentication has not yet been adopted by large-scale point-of-sale and automated teller machine systems. This paper aims at providing a better understanding of the benefits and limitations associated with the integration of biometrics in a PIN-based payment authentication system. Based on a review of the market drivers and deployment hurdles, a method is proposed in which biometrics can be seamlessly integrated in a PIN-based authentication infrastructure. By binding a fixed binary, renewable string to a noisy biometric sample, the data privacy and interoperability between issuing and acquiring banks can improve considerably compared to conventional biometric approaches. The biometric system security, cost aspects, and customer convenience are subsequently compared to PIN by means of simulations using fingerprints. The results indicate that the biometric authentication performance is not negatively influenced by the incorporation of key binding and release processes, and that the security expressed as guessing entropy of the biometric key is virtually identical to the current PIN. The data also suggest that for the fingerprint database under test, the claimed benefits for cost reduction, improved security and customer convenience do not convincingly materialize when compared to PIN. This result can in part explain why large-scale biometric payment systems are virtually non-existent in Europe and the United States, and suggests that other biometric modalities than fingerprints may be more appropriate for payment systems.

Exploiting Samples Quality in Evaluating and Improving Performance of Biometric Systems

Many approaches have been proposed to enhance the performance of biometric-based recognition when using poor quality biometric samples. It has been shown that reporting average accuracy, to cover a wide range of quality of biometric samples, is not enough to reflect the actual performance. This raises the need to evaluate biometric systems at each quality level separately. Therefore, challenging biometric databases have been recorded with variety of quality conditions and made publically available. This paper highlights the importance of using Adaptive Quality-Based Thresholding (AQBT) when evaluating the performance of biometric systems under different quality conditions. Furthermore, it shows that, in many cases, recognition accuracy evaluation reported in the literature under different quality conditions has two problems. First, the performance is evaluated under hidden assumption, which is AQBT. Second, the reported results cannot be achieved in real-life applications. In order to remedy the two problems, two requirements are to be imposed: 1) the matching criteria should be based on AQBT and 2) at the verification stage the quality level of an input biometric sample should be determined and classified into one of a nonoverlapping predefined quality levels. Finally, we propose the use of adaptive quality-based feature extraction to enhance the accuracy of biometric systems. Although this paper focuses on face biometric as a case study, the discussion can be equally applied to other biometric treats. We illustrate our ideas by experiments conducted on the extended Yale B face benchmark dataset.

8.1.1 Enhancing the Usability of Human Machine Interface on the Handheld Interagency Identification Detection Equipment (HIIDE™)

AbstractAn essential element of a biometric system is the human machine interface (HMI), both from the user (administrator) and subject perspectives. The HMI has a significant impact on the performance of the system, as it is a key factor in the quality of the biometric sample collected. This paper will consider the implications of the HMI from the user perspective, analyzing the results of user experiences and training with the Handheld Interagency Identity Detection Equipment (HIIDE) Series 4, a device used by the Department of Defense in Iraq and Afghanistan. Results of this task analysis will be in the form of recommendations and considerations for the development of future collection systems. The paper will also provide a preliminary review of the recently released HIIDE Series 5 device and conclude by presenting the Systems Engineering (SE) based process used in this analysis as well as recommendations for including select SE practices in future biometric system design and development.

A Unified Framework for Biometric Expert Fusion Incorporating Quality Measures

This paper proposes a unified framework for quality-based fusion of multimodal biometrics. Quality-dependent fusion algorithms aim to dynamically combine several classifier (biometric expert) outputs as a function of automatically derived (biometric) sample quality. Quality measures used for this purpose quantify the degree of conformance of biometric samples to some predefined criteria known to influence the system performance. Designing a fusion classifier to take quality into consideration is difficult because quality measures cannot be used to distinguish genuine users from impostors, i.e., they are nondiscriminative yet still useful for classification. We propose a general Bayesian framework that can utilize the quality information effectively. We show that this framework encompasses several recently proposed quality-based fusion algorithms in the literature--Nandakumar et al., 2006; Poh et al., 2007; Kryszczuk and Drygajo, 2007; Kittler et al., 2007; Alonso-Fernandez, 2008; Maurer and Baker, 2007; Poh et al., 2010. Furthermore, thanks to the systematic study concluded herein, we also develop two alternative formulations of the problem, leading to more efficient implementation (with fewer parameters) and achieving performance comparable to, or better than, the state of the art. Last but not least, the framework also improves the understanding of the role of quality in multiple classifier combination.

Optimal Time-Resource Allocation for Energy-Efficient Physical Activity Detection.

The optimal allocation of samples for physical activity detection in a wireless body area network for health-monitoring is considered. The number of biometric samples collected at the mobile device fusion center, from both device-internal and external Bluetooth heterogeneous sensors, is optimized to minimize the transmission power for a fixed number of samples, and to meet a performance requirement defined using the probability of misclassification between multiple hypotheses. A filter-based feature selection method determines an optimal feature set for classification, and a correlated Gaussian model is considered. Using experimental data from overweight adolescent subjects, it is found that allocating a greater proportion of samples to sensors which better discriminate between certain activity levels can result in either a lower probability of error or energy-savings ranging from 18% to 22%, in comparison to equal allocation of samples. The current activity of the subjects and the performance requirements do not significantly affect the optimal allocation, but employing personalized models results in improved energy-efficiency. As the number of samples is an integer, an exhaustive search to determine the optimal allocation is typical, but computationally expensive. To this end, an alternate, continuous-valued vector optimization is derived which yields approximately optimal allocations and can be implemented on the mobile fusion center due to its significantly lower complexity.

Multimodal Biometric Fusion: Performance under Spoof Attacks

Biometrics is essentially a pattern recognition system that recognizes an individual using their unique anatomical or behavioral patterns such as face, fingerprint, iris, signature etc. Recent researches have shown that many biometric traits are vulnerable to spoof attacks. Moreover, recent works showed that, contrary to a common belief, multimodal biometric systems in parallel fusion mode can be intruded even if only one trait is spoofed. However, most of the results were obtained using simulated spoof attacks, under the assumption that the spoofed and genuine samples are indistinguishable, which may not be true for all biometric traits. In addition, so far vulnerability of multimodal biometric systems in serial fusion mode against spoof attacks has not been investigated. These issues raise a demand to investigate the robustness of multimodal systems under realistic spoof attacks. In this paper, we empirically investigate the performance of serial and parallel biometric fusion modes under realistic spoof attacks. Preliminary empirical results on real biometric systems made up of face, fingerprint and iris confirm that multimodal biometric systems in both fusion modes are not intrinsically robust against spoof attacks as believed so far. In particular, multimodal biometric systems in serial fusion mode can be even less robust than systems in parallel mode. We also experimentally found that incorporating the biometric sample quality in biometric fusion increases the robustness of the multimodal systems against spoof attacks. In the end, we study the trade-off between performance and robustness of the biometric systems under spoof attacks.

SIGNATURE AND IRIS AUTHENTICATION BASED ON DERIVED KINEMATIC VALUES

Authentication systems which are covenant with a measurable behavioral and physiological traits are essential for an online system. In this paper, two types of biometric sample authentication from different databases on a common algorithm using Continuous Dynamic Programming [CDP] are discussed. Using a common algorithm, a method for user-dependent threshold decisions can be achieved for both biometrics in a uniform fashion. The integration of static iris information and dynamic signature information are done at decision level. Inferences are drawn using voting techniques. The derived kinematic feature, acceleration, is used in this paper.

Binary Biometrics: An Analytic Framework to Estimate the Performance Curves Under Gaussian Assumption

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In recent years, the protection of biometric data has gained increased interest from the scientific community. Methods such as the fuzzy commitment scheme, helper-data system, fuzzy extractors, fuzzy vault, and cancelable biometrics have been proposed for protecting biometric data. Most of these methods use cryptographic primitives or error-correcting codes (ECCs) and use a binary representation of the real-valued biometric data. Hence, the difference between two biometric samples is given by the Hamming distance (HD) or bit errors between the binary vectors obtained from the enrollment and verification phases, respectively. If the HD is smaller (larger) than the decision threshold, then the subject is accepted (rejected) as genuine. Because of the use of ECCs, this decision threshold is limited to the maximum error-correcting capacity of the code, consequently limiting the false rejection rate (FRR) and false acceptance rate tradeoff. A method to improve the FRR consists of using multiple biometric samples in either the enrollment or verification phase. The noise is suppressed, hence reducing the number of bit errors and decreasing the HD. In practice, the number of samples is empirically chosen without fully considering its fundamental impact. In this paper, we present a Gaussian analytical framework for estimating the performance of a binary biometric system given the number of samples being used in the enrollment and the verification phase. The error-detection tradeoff curve that combines the false acceptance and false rejection rates is estimated to assess the system performance. The analytic expressions are validated using the Face Recognition Grand Challenge v2 and Fingerprint Verification Competition 2000 biometric databases. </para>

Biometric Authentication System Using Non-linear Chaos

A major concern nowadays for any Biometric Credential Management System is its potential vulnerability to protect its information sources; i.e. protecting a genuine user's template from both internal and external threats. These days' biometric authentication systems face various risks. One of the most serious threats is the vulnerability of the template's database. An attacker with access to a reference template could try to impersonate a legitimate user by reconstructing the biometric sample and by creating a physical spoof. Susceptibility of the database can have a disastrous impact on the whole authentication system. The potential disclosure of digitally stored biometric data raises serious concerns about privacy and data protection. Therefore, we propose a method which would integrate conventional cryptography techniques with biometrics. In this work, we present a biometric crypto system which encrypts the biometric template and the encryption is done by generating pseudo random numbers, based on non-linear dynamics.