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Auflistung nach Schlagwort "Iris Recognition"

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  • Textdokument
    Exploring Texture Transfer Learning via Convolutional Neural Networks for Iris Super Resolution
    (BIOSIG 2017, 2017) Ribeiro,Eduardo; Uhl,Andreas
    Increasingly, iris recognition towards more relaxed conditions has issued a new superresolution field direction. In this work we evaluate the use of deep learning and transfer learning for single image super resolution applied to iris recognition. For this purpose, we explore if the nature of the images as well as if the pattern from the iris can influence the CNN transfer learning and, consequently, the results in the recognition process. The good results obtained by the texture transfer learning using a deep architecture suggest that features learned by Convolutional Neural Networks used for image super-resolution can be highly relevant to increase iris recognition rate.
  • Textdokument
    SIC-Gen: A Synthetic Iris-Code Generator
    (BIOSIG 2017, 2017) Drozdowski,Pawel; Rathgeb,Christian; Busch,Christoph
    Nowadays large-scale identity management systems enrol more than one billion data subjects. In order to limit transaction times, biometric indexing is a suitable method to reduce the search space in biometric identifications. Effective testing of such biometric identification systems and biometric indexing approaches requires large datasets of biometric data. Currently, the size of the publicly available iris datasets is insufficient, especially for system scalability assessments. Synthetic data generation offers a potential solution to this issue; however, it is challenging to generate data hat is both statistically sound and visually realistic - for the iris, the currently available approaches prove unsatisfactory. In this paper, we present a method for generation of synthetic binary iris-based templates, i.e. Iris-Codes, which are the de facto standard used throughout major biometric deployments around the world. We validate the statistical properties of the synthetic templates and show that they closely resemble ones produced from real ocular images. With the proposed approach, large databases of synthetic Iris-Codes with flexibly adjustable properties can be generated.