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Online ISSN 1751-9640 Print ISSN 1751-9632

IET Computer Vision

Volume 5, Issue 6, November 2011

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Volume 5, Issue 6

November 2011

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  • Future trends in biometric processing

    • Editorial: Future trends in biometric processing
      Quantitative evaluation of the effects of aging on biometric templates
      • Author(s): A. Lanitis  and  N. Tsapatsoulis
      • Source: IET Computer Vision, Volume 5, Issue 6, p. 338 –347
      • DOI:  10.1049/iet-cvi.2010.0197
      • Type: Article
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      • p. 338 –347 (10)
        The long-term performance of biometric authentication systems is highly depended on the permanence of biometric features stored in biometric templates. Aging variation causes modifications on biometric features that affect the matching between stored and captured biometric templates causing in that way deterioration in the performance of biometric authentication systems. In this study the authors attempt to quantify the effects of aging for different biometric modalities, so that it is possible to draw conclusions related to the effect of aging on different types of biometric templates. In this context variations between distributions containing biometric features from different age groups are quantified, allowing in that way the definition of age-sensitive and age-invariant biometric features. An important aspect of the proposed approach is the standardised and generic nature of the approach that allows the derivation of comparative results between different modalities and different feature vectors. The work presented in this study provides a valuable tool for selecting, either age-invariant features for use in identity authentication applications, or for selecting age-sensitive features for age-estimation-related applications.
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      Performance of active appearance model-based pose-robust face recognition
      • Author(s): L. Teijeiro-Mosquera  and  J.L. Alba-Castro
      • Source: IET Computer Vision, Volume 5, Issue 6, p. 348 –357
      • DOI:  10.1049/iet-cvi.2010.0184
      • Type: Article
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      • p. 348 –357 (10)
        In this study, the authors study the performance of a novel active appearance model (AAM)-based fully automatic system for pose robust face recognition that allows faster fitting to a no frontal view and generation of virtual views. The system follows a multiresolution scheme, where the first level is used to initialise a generic AAM, pose angle is automatically estimated using eigenvector analysis, and then a pose-dependent AAM model is selected. Next level refines AAM model fitting and registration. Finally, a virtual frontal view is created before face matching. Recognition results over CMU PIE database show similar values compared with the performance achieved with manually landmarked faces. Compared with a classical view-based approach, this multiresolution scheme performs similarly but is sensibly faster.
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      Analysis of physical ageing effects in iris biometrics
      • Author(s): M. Fairhurst  and  M. Erbilek
      • Source: IET Computer Vision, Volume 5, Issue 6, p. 358 –366
      • DOI:  10.1049/iet-cvi.2010.0165
      • Type: Article
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      • p. 358 –366 (9)
        Physical ageing is an important issue for practical biometrics, since it is known that the associated physiological changes can impair performance for most modalities. Understanding the effects of ageing is necessary, therefore both to optimise attainable performance but also to understand how to manage biometric templates, especially as the time elapsed between enrolment and use increases. Many factors can affect the performance of an iris recognition system, and in this study the authors report an experimental investigation of the interrelationship between some characteristics which are particularly relevant in understanding how to manage the practical effects of ageing with respect to this modality. Moreover, template ageing will itself be affected by issues related to the biological age of a subject, and this is also explored here.
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      Unobtrusive multi-modal biometric recognition using activity-related signatures
      • Author(s): A. Drosou ; G. Stavropoulos ; D. Ioannidis ; K. Moustakas ; D. Tzovaras
      • Source: IET Computer Vision, Volume 5, Issue 6, p. 367 –379
      • DOI:  10.1049/iet-cvi.2010.0166
      • Type: Article
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      • p. 367 –379 (13)
        The present study proposes a novel multimodal biometrics framework for identity recognition and verification following the concept of the so called ‘on-the-move’ biometry, which sets as the final objective the non-stop authentication in an unobtrusive manner. Gait, that forms the major modality of the scheme, is complemented by new dynamic biometric signatures extracted from several activities performed by the user. Gait recognition is performed through a robust scheme that is based on geometric descriptors of gait energy images and is able to compensate for undesired gait behaviour like walking direction variations and stops. On the other hand, the biometric signatures, based on the user activities, are extracted by tracking of three points of interest and are seen to provide a powerful auxiliary biometric trait. Finally, score level fusion is performed and the experimental results illustrate that the proposed multimodal biometric scheme provides very promising results in realistic application scenarios.
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      Analysis of spatial domain information for footstep recognition
      • Author(s): R. Vera-Rodriguez ; J.S.D. Mason ; J. Fierrez ; J. Ortega-Garcia
      • Source: IET Computer Vision, Volume 5, Issue 6, p. 380 –388
      • DOI:  10.1049/iet-cvi.2010.0189
      • Type: Article
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      • p. 380 –388 (9)
        This study reports an experimental analysis of footsteps as a biometric. The focus here is on information extracted from the spatial domain of signals collected from an array of piezoelectric sensors. Results are related to the largest footstep database collected to date, with almost 20 000 valid footstep signals and more than 120 persons. A novel feature approach is proposed, obtaining three-dimensional images of the distribution of the footstep pressure along the spatial course. Experimental work is based on a verification mode with a holistic approach based on principal component analysis and support vector machines, achieving results in the range of 6–10% equal error rate (EER) depending on the experimental conditions of quantity of data used in the client models (200 and 40 signals per model, respectively). Also, this study includes the analysis of two interesting factors affecting footstep signals and especially spatial domain features, namely, sensor density and the special case of high heels.
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      Context-based biometric key generation for Iris
      • Author(s): C. Rathgeb  and  A. Uhl
      • Source: IET Computer Vision, Volume 5, Issue 6, p. 389 –397
      • DOI:  10.1049/iet-cvi.2010.0176
      • Type: Article
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      • p. 389 –397 (9)
        In this study, a generic treatment of how to generate biometric keys from binary biometric templates is presented. A context-based analysis of iris biometric feature vectors based on which stable biometric keys are extracted is proposed. Most reliable bits in binary iris codes are detected and utilised to construct keys from fuzzy biometric data. The proposed key-generation scheme is adapted to diverse iris biometric feature extraction algorithms, evaluated on a comprehensive database and compared against existing iris biometric cryptosystems. In addition, the scheme is extended to provide fully revocable biometric keys, long enough to be applied in generic cryptosystems. Experimental results confirm the soundness of the approach.
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      Hand vein biometry based on geometry and appearance methods
      • Author(s): A. Yuksel ; L. Akarun ; B. Sankur
      • Source: IET Computer Vision, Volume 5, Issue 6, p. 398 –406
      • DOI:  10.1049/iet-cvi.2010.0175
      • Type: Article
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      • p. 398 –406 (9)
        Many biometric systems, such as face, fingerprint and iris have been studied extensively for personal verification and identification purposes. Biometric identification with vein patterns is a more recent approach that uses the vast network of blood vessels underneath a person's skin. These patterns in the hands are assumed to be unique to each individual and they do not change over time except in size. As veins are under the skin and have a wealth of differentiating features, an attempt to copy an identity is extremely difficult. These properties of uniqueness, stability and strong immunity to forgery of the vein patterns make it a potentially good biometric trait which offers greater security and reliable features for personal identification. In this study, the authors present a novel hand vein database and a biometric technique based on the statistical processing of the hand vein patterns. The BOSPHORUS hand vein database has been collected under realistic conditions in that subjects had to undergo the procedures of holding a bag, pressing an elastic ball and cooling with ice, all exercises that force changes in the vein patterns. The applied recognition techniques are a combination of geometric and appearance-based techniques and good identification performances have been obtained on the database.
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      Towards contactless palmprint authentication
      • Author(s): A. Morales ; M.A. Ferrer ; A. Kumar
      • Source: IET Computer Vision, Volume 5, Issue 6, p. 407 –416
      • DOI:  10.1049/iet-cvi.2010.0191
      • Type: Article
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      • p. 407 –416 (10)
        This study examines the issues related to two of the most palmprint promising approaches applied to the contactless biometric authentication and presents a performance evaluation on three different scenarios. The presence of significant scale, rotation, occlusion and translation variations in the contactless palmprint images requires the feature extraction approaches that can accommodate such within class image variations. Therefore the usage and performance of traditional palmprint feature extraction methods on contactless imaging schemes remain questionable and hence all/popular palmprint feature extraction methods may not be effective in contactless frameworks. The experimental results on more than 6000 images from three contactless databases acquired in different environments suggest that the scale invariant feature transform (SIFT) features perform significantly better for the contactless palmprint images than the promising orthogonal line ordinal features (OLOF) approach employed earlier on the more conventional touch-based palmprint imaging. The experimental results further suggest that the combination of robust SIFT matching scores along with those from OLOF can be employed to achieve more reliable performance improvement. The use of publicly available databases ensures repeatability in the experiments. Therefore this study provides a new/challenging contactless hand database acquired in uncontrolled environments for further research efforts.
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