Periocular Recognition from Low-Quality Iris Images

  • Josh Klontz
  • Mark J. Burge
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


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.


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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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.The MITRE CorporationMcLeanUSA

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