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Periocular Recognition from Low-Quality Iris Images

Part of the Advances in Computer Vision and Pattern Recognition book series (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.

Keywords

  • Local Binary Pattern
  • Scale Invariant Feature Transformation
  • Gabor Filter
  • Gabor Wavelet
  • Motion Blur

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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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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