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  • 1.
    Alonso-Fernandez, Fernando
    et al.
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Madrid, Spain.
    Fierrez-Aguilar, Julian
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Madrid, Spain.
    Ortega-Garcia, Javier
    Escuela Politecnica Superior, Univ. Autonoma de Madrid, Madrid, Spain.
    A Review Of Schemes For Fingerprint Image Quality Computation2005In: COST Action 275: Proceedings of the third COST 275 Workshop Biometrics on the Internet / [ed] Aladdin Ariyaeeinia, Mauro Falcone & Andrea Paoloni, Luxembourg: EU Publications Office (OPOCE) , 2005, p. 3-6Conference paper (Refereed)
    Abstract [en]

    Fingerprint image quality affects heavily the performance of fingerprint recognition systems. This paper reviews existing approaches for fingerprint image quality computation. We also implement, test and compare a selection of them using the MCYT database including 9000 fingerprint images. Experimental results show that most of the algorithms behave similarly.

  • 2.
    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.

  • 3.
    Krish, Ram P.
    et al.
    Universidad Autonóma de Madrid, Madrid, Spain.
    Fierrez, Julian
    Universidad Autonóma de Madrid, Madrid, Spain.
    Ramos, Daniel
    Universidad Autonóma de Madrid, Madrid, Spain.
    Ortega-Garcia, Javier
    Universidad Autonóma de Madrid, Madrid, Spain.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pre-registration for Improved Latent Fingerprint Identification2014In: 2014 22nd International Conference on Pattern Recognition (ICPR) / [ed] Lisa O’Conner, Los Alamitos: IEEE Computer Society, 2014, p. 696-701Conference paper (Refereed)
    Abstract [en]

    Comparing a latent fingerprint minutiae set against a ten print fingerprint minutiae set using an automated fingerprint identification system is a challenging problem. This is mainly because latent fingerprints obtained from crime scenes are mostly partial fingerprints, and most automated systems expect approximately the same number of minutiae between query and the reference fingerprint under comparison for good performance. In this work, we propose a methodology to reduce the minutiae set of ten print with respect to that of query latent minutiae set by registering the orientation field of latent fingerprint with the ten print orientation field. By reducing the search space of minutiae from the ten print, we can improve the performance of automated identification systems for latent fingerprints. We report the performance of our registration algorithm on the NIST-SD27 database as well as the improvement in the Rank Identification accuracy of a standard minutiae-based automated system. © 2014 IEEE.

  • 4.
    Krish, Ram Prasad
    et al.
    Universidad Autonóma de Madrid, Madrid, Spain.
    Fierrez, Julian
    Universidad Autonóma de Madrid, Madrid, Spain.
    Ramos, Daniel
    Universidad Autonóma de Madrid, Madrid, Spain.
    Ortega-Garcia, Javier
    Universidad Autonóma de Madrid, Madrid, Spain.
    Bigun, Josef
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pre-registration of latent fingerprints based on orientation field2015In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 4, no 2, p. 42-52Article in journal (Refereed)
    Abstract [en]

    In this study, the authors present a hierarchical algorithm to register a partial fingerprint against a full fingerprint using only the orientation fields. In the first level, they shortlist possible locations for registering the partial fingerprint in the full fingerprint using a normalised correlation measure, taking various rotations into account. As a second level, on those candidate locations, they calculate three other similarity measures. They then perform score fusion for all the estimated similarity scores to locate the final registration. By registering a partial fingerprint against a full fingerprint, they can reduce the search space of the minutiae set in the full fingerprint, thereby improving the result of partial fingerprint identification, particularly for poor quality latent fingerprints. They report the rank identification improvements of two minutiae-based automated fingerprint identification systems on the National Institute of Standards and Technology (NIST)-Special Database 27 database when they use the authors hierarchical registration as a pre-alignment. © The Institution of Engineering and Technology 2015.

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