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Auflistung nach Schlagwort "iris recognition"

1 - 3 von 3
  • Textdokument
    Improvement of Iris Recognition based on Iris-Code Bit-Error Pattern Analysis
    (BIOSIG 2017, 2017) Rathgeb,Christian; Busch,Christoph
    In this paper an advanced iris-biometric comparator is presented. In the proposed scheme an analysis of bit-error patterns produced by Hamming distance-based iris-code comparisons is performed. The lengths of sequences of horizontal consecutive mis-matching bits are measured and a frequency distribution is estimated. The difference of the extracted frequency distribution to that of an average genuine one obtained from a training set is used as a second comparison score. This score is then used together with the fractional Hamming distance in order to improve the recognition accuracy of an iris recognition system. In experimental evaluations relative improvements of approximately 45% and 10% in terms of false non-match rate at a false match rate of 0.01% are achieved on the CASIAv4-Interval and the BioSecure iris databases, respectively.
  • Konferenzbeitrag
    Iris Recognition in Postmortem Enucleated Eyes
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Saripalle, Sashi K.; McLaughlin, Adam; Derakhshani, Reza
    This paper presents a comprehensive multispectral study of iris recognition on postmortem enucleated eyes over a period of three days. An off the shelf iris recognition methodology is employed to analyze the biometric capability of iris in the post mortem setting.We observed that iris patterns of enucleated eyes can provide biometric matches with no false accepts for up to 164 hours after death, albeit with high false rejection rates. We also present our observations on the effects of the environment and other confounding factors that may affect the performance of postmortem iris recognition, with recommendations for rehydration of specimen to regain postmortem biometric utility.
  • Konferenzbeitrag
    Visible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networks
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Osorio-Roig, Dailé; Rathgeb, Christian; Gomez-Barrero, Marta; Morales-González, Annette; Garea-Llano, Eduardo; Busch, Christoph
    Iris segmentation under visible wavelengths (VWs) is a vital processing step for iris recognition systems operating at-a-distance or in non-cooperative environments. In these scenarios the presence of various artefacts, e.g. occlusions or specular reflections, as well as out-of-focus blur represents a significant challenge. The vast majority of proposed iris segmentation algorithms under VW aim at discriminating the iris and non-iris regions without taking into account the variability that is present in the non-iris region. In this paper, we introduce the idea of segmenting the iris region using a multi-class approach which differentiates additional classes, e.g. pupil or sclera, as opposed to commonly employed bi-class approaches (iris and non-iris). Experimental results conducted on two publicly available databases show that the use of the proposed multi-class approach improves the iris segmentation accuracy. Simultaneously, it also allows for the segmentation of different non-iris regions, e.g. glasses, which could be employed in further application scenarios.