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Methods for Iris Segmentation

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Handbook of Iris Recognition

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Under ideal image acquisition conditions, the iris biometric has been observed to provide high recognition performance compared to other biometric traits. Such a performance is possible by accurately segmenting the iris region from the given ocular image. This chapter discusses the challenges associated with the segmentation process, along with some of the prominent iris segmentation techniques proposed in the literature. The methods are presented according to their suitability for segmenting iris images acquired under different wavelengths of illumination. Furthermore, methods to refine and evaluate the output of the iris segmentation routine are presented. The goal of this chapter is to provide a brief overview of the progress made in iris segmentation.

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Notes

  1. 1.

    The term “iris boundaries” is used in this chapter to collectively refer to both the pupillary and limbus boundaries.

  2. 2.

    This is true for images obtained in the near-infrared spectrum.

  3. 3.

    Distance between the user and the sensor.

  4. 4.

    The process of generating a noise mask, and the subsequent schemes for iris normalization and matching are very similar in a majority of iris recognition algorithms. However, as the chapter focuses only on iris segmentation, these details are not discussed. The reader is directed to the original publication by Daugman [9] for further information.

  5. 5.

    The subscript t denotes the iteration number.

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Author information

Authors and Affiliations

  1. Digital Signal Corporation, Chantilly, VA, USA

    Raghavender Jillela

  2. Integrated Pattern Recognition and Biometrics Lab (i-PRoBe), Michigan State University, East Lansing, MI, USA

    Arun A. Ross

Authors
  1. Raghavender Jillela
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  2. Arun A. Ross
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Corresponding author

Correspondence to Arun A. Ross .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, USA

    Kevin W. Bowyer

  2. Noblis, Inc., Washington, District of Columbia, USA

    Mark J. Burge

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Jillela, R., Ross, A.A. (2016). Methods for Iris Segmentation. In: Bowyer, K., Burge, M. (eds) Handbook of Iris Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6784-6_7

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  • DOI: https://doi.org/10.1007/978-1-4471-6784-6_7

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  • Print ISBN: 978-1-4471-6782-2

  • Online ISBN: 978-1-4471-6784-6

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