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  • 1.
    Alonso-Fernandez, Fernando
    et al.
    University de Madrid, Madrid, Spain.
    Fierrez, J.
    Universidad Autonoma de Madrid.
    Ortega-Garcia, J.
    Universidad Autónoma de Madrid.
    Gonzalez-Rodriguez, J.
    Universidad Autónoma de Madrid.
    Fronthaler, Hartwig
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Kollreider, Klaus
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    A Comparative Study of Fingerprint Image-Quality Estimation Methods2007In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 2, no 4, p. 734-743Article in journal (Refereed)
    Abstract [en]

    One of the open issues in fingerprint verification is the lack of robustness against image-quality degradation. Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system. Therefore, it is important for a fingerprint recognition system to estimate the quality and validity of the captured fingerprint images. In this work, we review existing approaches for fingerprint image-quality estimation, including the rationale behind the published measures and visual examples showing their behavior under different quality conditions. We have also tested a selection of fingerprint image-quality estimation algorithms. For the experiments, we employ the BioSec multimodal baseline corpus, which includes 19 200 fingerprint images from 200 individuals acquired in two sessions with three different sensors. The behavior of the selected quality measures is compared, showing high correlation between them in most cases. The effect of low-quality samples in the verification performance is also studied for a widely available minutiae-based fingerprint matching system.

  • 2.
    Bhanu, Bir
    et al.
    University of California at Riverside, USA.
    Ratha, Nalini K.
    IBM T.J. Watson Research Center, USA.
    Kumar, Vijay
    Carnegie Mellon University, USA.
    Chellappa, Rama
    University of Maryland.
    Bigun, Josef
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Guest Editorial: Special Issue on Human Detection and Recognition2007In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 2, no 3 part 2, p. 489-490Article in journal (Refereed)
    Abstract [en]

    The 12 regular papers and three correspondences in this special issue focus on human detection and recognition. The papers represent gait, face (3-D, 2-D, video), iris, palmprint, cardiac sounds, and vulnerability of biometrics and protection against the spoof attacks.

  • 3.
    Fronthaler, Hartwig
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Kollreider, Klaus
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bigun, Josef
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Fierrez, Julian
    Univ Autonoma Madrid, Escuela Politec Super, ATVS, Madrid, Spain.
    Alonso-Fernandez, Fernando
    Univ Autonoma Madrid, Escuela Politec Super, ATVS, Madrid, Spain.
    Ortega-Garcia, Javier
    Univ Autonoma Madrid, Escuela Politec Super, ATVS, Madrid, Spain.
    Gonzalez-Rodriguez, Joaquin
    Univ Autonoma Madrid, Escuela Politec Super, ATVS, Madrid, Spain.
    Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification2008In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 3, no 2, p. 331-338Article in journal (Refereed)
    Abstract [en]

    Signal-quality awareness has been found to increase recognition rates and to support decisions in multisensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here, we study the orientation tensor of fingerprint images to quantify signal impairments, such as noise, lack of structure, blur, with the help of symmetry descriptors. A strongly reduced reference is especially favorable in biometrics, but less information is not sufficient for the approach. This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ), as well as the human perception of fingerprint quality on several public databases. Furthermore, quality measurements are extensively reused to adapt fusion parameters in a monomodal multialgorithm fingerprint recognition environment. In this study, several trained and nontrained score-level fusion schemes are investigated. A Bayes-based strategy for incorporating experts' past performances and current quality conditions, a novel cascaded scheme for computational efficiency, besides simple fusion rules, is presented. The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively (by training).

  • 4.
    Gonzalez-Sosa, Ester
    et al.
    Nokia Bell-Labs, Madrid, Spain & Universidad Autonoma de Madrid, Madrid, Spain.
    Fierrez, Julian
    Universidad Autonoma de Madrid, Madrid, Spain.
    Vera-Rodriguez, Ruben
    Universidad Autonoma de Madrid, Madrid, Spain.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation and Evaluation2018In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 13, no 8, p. 2001-2014Article in journal (Refereed)
    Abstract [en]

    The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard, and moustache. We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems (COTS). All experiments are reported using the labeled faces in the wild (LFW) database. First, we study the discrimination capabilities of soft biometrics standalone. Then, experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning. We observe that soft biometrics is a valuable complement to the face modality in unconstrained scenarios, with relative improvements up to 40%/15% in the verification performance when using manual/automatic soft biometrics estimation. Results are reproducible as we make public our manual annotations and COTS outputs of soft biometrics over LFW, as well as the face recognition scores. © 2018 IEEE.

  • 5.
    Kollreider, Klaus
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Fronthaler, Hartwig
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Faraj, Maycel
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bigun, Josef
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment2007In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 2, no 3 part 2, p. 548-558Article in journal (Refereed)
    Abstract [en]

    A robust face detection technique along with mouth localization, processing every frame in real time (video rate), is presented. Moreover, it is exploited for motion analysis onsite to verify "liveness" as well as to achieve lip reading of digits. A methodological novelty is the suggested quantized angle features ("quangles") being designed for illumination invariance without the need for preprocessing (e.g., histogram equalization). This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. A major benefit is reduced processing time (i.e., that the training of effective cascaded classifiers is feasible in very short time, less than 1 h for data sets of order 104). Scale invariance is implemented through the use of an image scale pyramid. We propose "liveness" verification barriers as applications for which a significant amount of computation is avoided when estimating motion. Novel strategies to avert advanced spoofing attempts (e.g., replayed videos which include person utterances) are demonstrated. We present favorable results on face detection for the YALE face test set and competitive results for the CMU-MIT frontal face test set as well as on "liveness" verification barriers.

  • 6.
    Poh, Norman
    et al.
    University of Surrey, United Kingdom.
    Bourlai, Thirimachos
    West Virginia University, United States.
    Kittler, Josef
    University of Surrey, United Kingdom.
    Allano, Lorene
    CEA LIST, CEA Saclay, PC 72-91191 Gif-sur-Yvette Cedex, France.
    Alonso-Fernandez, Fernando
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Ambekar, Onkar
    Centrum Wiskunde and Informatica (CWI), 1098 XG, Amsterdam, Netherlands.
    Baker, John
    Johns Hopkins University, United States .
    Dorizzi, Bernadette
    Electronics and Physics Department, Institut Telecom, Telecom and Management SudParis, 91011 Evry, France.
    Fatukasi, Omolara
    University of Surrey, United Kingdom.
    Fierrez, Julian
    Universidad Autónoma de Madrid, Spain .
    Ganster, Harald
    Institute of Digital Image Processing, Joanneum Research, Austria.
    Ortega-Garcia, Javier
    Universidad Autónoma de Madrid, Spain .
    Maurer, Donald
    Johns Hopkins University, United States.
    Salah, Albert Ali
    ISLA-ISIS, University of Amsterdam, 1098 XG Amsterdam, Netherlands.
    Scheidat, Tobias
    Brandenburg University of Applied Sciences, Germany.
    Vielhauer, Claus
    Otto-von-Guericke-University of Magdeburg, Germany.
    Benchmarking Quality-dependent and Cost-sensitive Score-level Multimodal Biometric Fusion Algorithms2009In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 4, no 4, p. 849-866Article in journal (Refereed)
    Abstract [en]

    Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing image quality which may be due to a change of acquisition devices and/or device capturing configurations, we observe that the top performing fusion algorithms are those that exploit automatically derived quality measurements. Our evaluation also suggests that while using all the available biometric sensors can definitely increase the fusion performance, this comes at the expense of increased cost in terms of acquisition time, computation time, the physical cost of hardware, and its maintenance cost. As demonstrated in our experiments, a promising solution which minimizes the composite cost is sequential fusion, where a fusion algorithm sequentially uses match scores until a desired confidence is reached, or until all the match scores are exhausted, before outputting the final combined score. © 2009 IEEE.

1 - 6 of 6
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  • ieee
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  • nn-NO
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  • Other locale
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Output format
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