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  • 1.
    Alonso-Fernandez, Fernando
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Farrugia, Reuben A.
    University of Malta, Msida, Malta.
    Fierrez, Julian
    Universidad Autonoma 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.
    Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris2019In: Selfie Biometrics: Advances and Challenges / [ed] Ajita Rattani, Reza Derakhshani & Arun A. Ross, Cham: Springer, 2019, 1, p. 105-128Chapter in book (Refereed)
    Abstract [en]

    Biometric research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severly affecting the accuracy of recognition systems if not tackled appropriately. In this chapter, we give an overview of recent research in super-resolution reconstruction applied to biometrics, with a focus on face and iris images in the visible spectrum, two prevalent modalities in selfie biometrics. After an introduction to the generic topic of super-resolution, we investigate methods adapted to cater for the particularities of these two modalities. By experiments, we show the benefits of incorporating super-resolution to improve the quality of biometric images prior to recognition. © Springer Nature AG 2019

  • 2.
    Ribeiro, Eduardo
    et al.
    University of Salzburg, Department of Computer Sciences, Salzburg, Austria & Federal University of Tocantins, Department of Computer Sciences, Tocantins, Brazil.
    Uhl, Andreas
    University of Salzburg, Department of Computer Sciences, Salzburg, Austria.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Farrugia, Reuben A.
    University of Malta, Department of CCE, Msida, Malta.
    Exploring Deep Learning Image Super-Resolution for Iris Recognition2017In: 25th European Signal Processing Conference (EUSIPCO 2017): 28 August-2 September 2017, Kos island, Greece, Institute of Electrical and Electronics Engineers (IEEE), 2017, Vol. 2017-January, p. 2176-2180, article id 8081595Conference paper (Refereed)
    Abstract [en]

    In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem. Here, we propose the use of two deep learning single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and Convolutional Neural Networks (CNN) with the most possible lightweight structure to achieve fast speed, preserve local information and reduce artifacts at the same time. We validate the methods with a database of 1.872 near-infrared iris images with quality assessment and recognition experiments showing the superiority of deep learning approaches over the compared algorithms. © EURASIP 2017.

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