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Online ISSN 2047-4946 Print ISSN 2047-4938

IET Biometrics

Volume 1, Issue 1, March 2012

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Volume 1, Issue 1

March 2012

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Editorial: First Issue Editorial

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    • Face spoofing detection from single images using texture and local shape analysis
      • Author(s): J. Määttä ; A. Hadid ; M. Pietikäinen
      • Source: IET Biometrics, Volume 1, Issue 1, p. 3 –10
      • DOI:  10.1049/iet-bmt.2011.0009
      • Type: Article
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      • p. 3 –10 (8)
        Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterisation of printing artefacts and differences in light reflection, the authors propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture and local shape features. Hence, the authors present a novel approach based on analysing facial image for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyses the texture and gradient structures of the facial images using a set of low-level feature descriptors, fast linear classification scheme and score level fusion. Compared to many previous works, the authors proposed approach is robust and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on three publicly available databases showed excellent results compared to existing works.
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      Security evaluation of biometric authentication systems under real spoofing attacks
      • Author(s): B. Biggio ; Z. Akhtar ; G. Fumera ; G.L. Marcialis ; F. Roli
      • Source: IET Biometrics, Volume 1, Issue 1, p. 11 –24
      • DOI:  10.1049/iet-bmt.2011.0012
      • Type: Article
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      • p. 11 –24 (14)
        Multimodal biometric systems are commonly believed to be more robust to spoofing attacks than unimodal systems, as they combine information coming from different biometric traits. Recent work has shown that multimodal systems can be misled by an impostor even by spoofing only one biometric trait. This result was obtained under a ‘worst-case’ scenario, by assuming that the distribution of fake scores is identical to that of genuine scores (i.e. the attacker is assumed to be able to perfectly replicate a genuine biometric trait). This assumption also allows one to evaluate the robustness of score fusion rules against spoofing attacks, and to design robust fusion rules, without the need of actually fabricating spoofing attacks. However, whether and to what extent the ‘worst-case’ scenario is representative of real spoofing attacks is still an open issue. In this study, we address this issue by an experimental investigation carried out on several data sets including real spoofing attacks, related to a multimodal verification system based on face and fingerprint biometrics. On the one hand, our results confirm that multimodal systems are vulnerable to attacks against a single biometric trait. On the other hand, they show that the ‘worst-case’ scenario can be too pessimistic. This can lead to two conservative choices, if the ‘worst-case’ assumption is used for designing a robust multimodal system. Therefore developing methods for evaluating the robustness of multimodal systems against spoofing attacks, and for designing robust ones, remain a very relevant open issue.
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      Comprehensive analysis of spectral minutiae for vein pattern recognition
      • Author(s): D. Hartung ; M. Aastrup Olsen ; H. Xu ; H. Thanh Nguyen ; C. Busch
      • Source: IET Biometrics, Volume 1, Issue 1, p. 25 –36
      • DOI:  10.1049/iet-bmt.2011.0013
      • Type: Article
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      • p. 25 –36 (12)
        Similar to biometric fingerprint recognition, characteristic minutiae points – here end and branch points – can be extracted from skeletonised vein images to distinguish individuals. An approach to extract those vein minutiae and to transform them into a fixed-length, translation and scale invariant representation where rotations can be easily compensated is presented in this study. The proposed solution based on spectral minutiae is evaluated against other comparison strategies on three different datasets of wrist and palm dorsal vein samples. The authors' analysis shows a competitive biometric performance while producing features that are compatible with state-of-the-art template protection systems. In addition, a modified and more distinctive, but not transform or rotation invariant, representation is proposed and evaluated.
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      Perturbation-enhanced feature correlation filter for robust iris recognition
      • Author(s): M. Zhang ; Z. Sun ; T. Tan
      • Source: IET Biometrics, Volume 1, Issue 1, p. 37 –45
      • DOI:  10.1049/iet-bmt.2012.0002
      • Type: Article
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      • p. 37 –45 (9)
        Visual pattern of human iris provides rich texture information for personal identification. However, it is challenging to match intra-class iris images with large variations in applications. This study proposes a perturbation-enhanced feature correlation filter (PFCF) for robust iris matching. PFCF is developed based on quad-phase minimum average correlation energy filter, but it has two significant improvements. First, PFCF is performed on Gabor filtered iris images to encode both local and global features. On the one hand, Gabor images can enhance the local details of iris texture. On the other hand, correlation filters describe the regional appearance information and measure the global similarity between iris images efficiently. Secondly, artificially perturbed iris images are generated to model intra-class variations. Also, a set of additional correlation filters are developed accordingly as the gallery templates. The decision is determined by the fusion result of multiple correlation filters. Therefore PFCF not only takes the advantages of Gabor images and correlation filters but also enlarges the amount of enrolled templates for robust iris matching. Extensive experiments on three challenging iris image databases demonstrate that the proposed method outperforms the state-of-the-art methods according to its robustness against deformation, rotation, occlusion, blurring and illumination changes in iris images.
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      Secure speech biometric templates for user authentication
      • Author(s): K. Inthavisas  and  D. Lopresti
      • Source: IET Biometrics, Volume 1, Issue 1, p. 46 –54
      • DOI:  10.1049/iet-bmt.2011.0008
      • Type: Article
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      • p. 46 –54 (9)
        The security of biometrics against attacks is a serious concern in biometric personal authentication systems. In particular, the security of biometric templates is a topic of rapidly growing importance in the area of user authentication. The authors first demonstrate the security of a protected speech biometric template and devised an algorithm to attack a speech biometric user authentication system where templates are protected by a cryptographic framework. The experimental result showed an improvement for attackers in gaining access to the system. Then, a way to combine a password with a speech biometric cryptosystem is proposed. The authors present two schemes to enhance verification performance in a biometric cryptosystem using a password. Both can resist a password brute-force search if the biometrics are not compromised. Even if the biometrics are compromised, the attackers have to make many more attempts in searching for cryptographic keys in the system described in this study, compared to a traditional password-based approach. Finally, it is shown that the error rate of the proposed scheme is the same as in a traditional password-based approach even when genuine biometrics or templates are compromised.
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      Maximisation of mutual information for gait-based soft biometric classification using Gabor features
      • Author(s): M. Hu ; Y. Wang ; Z. Zhang
      • Source: IET Biometrics, Volume 1, Issue 1, p. 55 –62
      • DOI:  10.1049/iet-bmt.2011.0004
      • Type: Article
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      • p. 55 –62 (8)
        Besides identity, soft biometric characteristics, such as gender and age can also be derived from gait patterns. With Gabor enhancement, supervised learning and temporal modelling, the authors present a robust framework to achieve state-of-the-art classification accuracy for both gender and age. Gabor filter and maximisation of mutual information are used to extract low-dimensional features, whereas Bayes rules based on hidden Markov models (HMMs) are adopted for soft biometric classification. The multi-view soft biometric classification problem is defined as two different cases, saying, one-to-one view and many-to-one view, according to the number of available gallery views. In case more than one gallery view is available, the multi-view soft biometric classification problem is hierarchically solved with a view-related population HMM, in which the estimated view angle is treated as the intermediate result in the first stage. Performance has been evaluated on benchmark databases, which verify the advantages of the proposed algorithm.
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      Unconditionally provably secure cancellable biometrics based on a quotient polynomial ring
      • Author(s): K. Takahashi  and  K. Naganuma
      • Source: IET Biometrics, Volume 1, Issue 1, p. 63 –71
      • DOI:  10.1049/iet-bmt.2011.0007
      • Type: Article
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      • p. 63 –71 (9)
        Biometric authentication has attracted attention because of its high security and usability. However, biometric features such as fingerprints are unchangeable throughout the life of individuals. Thus, once biometric data have been compromised, they cannot be used for authentication securely ever again. To address this issue, an authentication scheme called cancellable biometrics has been studied. However, there remains a major challenge to achieve both strong security and practical accuracy. The correlation invariant random filtering (CIRF) is an algorithm for cancellable biometrics with provable security and practical accuracy. However, the security proof requires an unrealistically strong assumption with regard to biometric features. The authors examine the security of the CIRF when the assumption is not satisfied, and show that there are vulnerabilities. To address the problems, the authors interpret the CIRF from an algebraic point of view and generalise it based on a quotient polynomial ring. Then several theorems are proved, which derive a new transformation algorithm. The security of the algorithm without any condition on the biometric features is proved. The authors also evaluate the accuracy of the algorithm by applying it to the chip matching algorithm for fingerprint verification and show that it does not degrade the matching accuracy.
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      Grid-based feature distributions for off-line signature verification
      • Author(s): K. Tselios ; E.N. Zois ; E. Siores ; A. Nassiopoulos ; G. Economou
      • Source: IET Biometrics, Volume 1, Issue 1, p. 72 –81
      • DOI:  10.1049/iet-bmt.2011.0011
      • Type: Article
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      • p. 72 –81 (10)
        In this work, a feature extraction method is presented for handwritten signature verification. The proposed algorithm models the handwritten elements of a signature trace by probabilistically counting the distribution of fixed two- and three-step pixel paths, conditioned that they are confined within predetermined Chebyshev distances of two and three, respectively. This representation correlates the pixel transitions along the signature trace, with the writing style of an individual. Various partitions of the signature image into a group of sub-images were applied in order to define the overall dimensionality of the feature. In order to evaluate the classification efficiency of the introduced method, a number of verification strategies are implemented by making use of two internationally accepted and one domestic datasets. In all schemes, similarity scores and hard margin support vector machines (SVMs) are combined or evaluated as separate entities. Additionally, zoning the extracted feature vector into combinations of tetrads and heptads, which in turn are fed into the afore-mentioned classification schemes, is exploited. Results, derived from random or simple imitations as well as simulated (skilled) forgery indicate that the proposed method achieves noticeably low equal error rates and it is expected to provide a powerful discriminative representation of the handwritten signature.
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      Reducing descriptor measurement error through Bayesian estimation of fingerprint minutia location and direction
      • Author(s): N.J. Short ; A. Lynn Abbott ; M.S. Hsiao ; E.A. Fox
      • Source: IET Biometrics, Volume 1, Issue 1, p. 82 –90
      • DOI:  10.1049/iet-bmt.2011.0010
      • Type: Article
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      • p. 82 –90 (9)
        Fingerprint-based recognition relies on the matching of features derived from the ridges and valleys of the friction ridge surface. When a large quantity of good-quality features are available, identification can be made with a high level of confidence. When portions of the fingerprint image are of lower quality, accuracy will suffer in the feature extraction and subsequent matching steps. This study is concerned with the correction of descriptor errors in minutiae (ridge endings and bifurcations), and with evaluation of the effect of feature accuracy on matching performance. The approach applies Bayesian filtering to refine minutia location and direction descriptors, using Sequential Monte Carlo approximation of a joint probability distribution near each minutia. The distribution approximates the location and orientation of the minutia, given measurements on local greyscale information, from which an expectation can be determined. Experimental results have been presented which demonstrate improvement of localisation accuracy with respect to ground truth data when using a well-known minutia extractor. These results are shown to be statistically significant, and to lead to improved matching performance. In addition, the authors were able to reduce the average minutia position error for a set of reference minutiae by 83% when using the proposed refinement method.
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