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Periocular Recognition from Low-Quality Iris Images

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Handbook of Iris Recognition

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

Abstract

Definitions of the periocular region vary, but typically encompass the skin covering the orbit of the eye. Especially in cases where the iris has not been acquired with sufficient quality to reliably compute an IrisCode, the periocular region can provide additional discriminative information for biometric identification. The NIR periocular images which form NIST’s Face and Ocular Challenge Series (FOCS) are characterized by large variations in illumination, eye-lid and eye-lash occlusion, de-focus blur, motion blur and low resolution. We investigate periocular recognition on the FOCS dataset using three distinct classes of features: photometric, keypoint, and frequency-based. We examine the performance of these features alone, in combination, and when fused with classic IrisCodes.

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Notes

  1. 1.

    As can be expected for one of the earlier standard test sets like CASIA [18], the commercial algorithm was able to perfectly separate match and non-match pairs.

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Authors and Affiliations

  1. The MITRE Corporation, McLean, VA, USA

    Josh Klontz & Mark J. Burge

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  1. Josh Klontz
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  2. Mark J. Burge
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Correspondence to Josh Klontz .

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Editors and Affiliations

  1. MITRE Corporation, Colshire Drive 7515, McLean, 22102-7539, Virginia, USA

    Mark J. Burge

  2. , Department of Computer Science and Engin, University of Notre Dame, Fitzpatrick Hall 384, Notre Dame, 46556-5637, Indiana, USA

    Kevin W. Bowyer

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Klontz, J., Burge, M.J. (2013). Periocular Recognition from Low-Quality Iris Images. In: Burge, M., Bowyer, K. (eds) Handbook of Iris Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4402-1_15

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  • DOI: https://doi.org/10.1007/978-1-4471-4402-1_15

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4401-4

  • Online ISBN: 978-1-4471-4402-1

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