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  4. Iris and Periocular Biometrics for Head Mounted Displays. Segmentation, Recognition, and Synthetic Data Generation
 
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2020
Journal Article
Titel

Iris and Periocular Biometrics for Head Mounted Displays. Segmentation, Recognition, and Synthetic Data Generation

Abstract
Augmented and virtual reality deployment is finding increasing use in novel applications. Some of these emerging and foreseen applications allow the users to access sensitive information and functionalities. Head Mounted Displays (HMD) are used to enable such applications and they typically include eye facing cameras to facilitate advanced user interaction. Such integrated cameras capture iris and partial periocular region during the interaction. This work investigates the possibility of using the captured ocular images from integrated cameras from HMD devices for biometric verification, taking into account the expected limited computational power of such devices. Such an approach can allow user to be verified in a manner that does not require any special and explicit user action. In addition to our comprehensive analyses, we present a light weight, yet accurate, segmentation solution for the ocular region captured from HMD devices. Further, we benchmark a number of well-established iris and periocular verification methods along with an in-depth analysis on the impact of iris sample selection and its effect on iris recognition performance for HMD devices. To the end, we also propose and validate an identity-preserving synthetic ocular image generation mechanism that can be used for large scale data generation for training purposes or attack generation purposes. We establish the realistic image quality of generated images with high fidelity and identity preserving capabilities through benchmarking them for iris and periocular verification.
Author(s)
Boutros, Fadi
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Damer, Naser
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Raja, Kiran
NTNU, Gjovik, Norway
Ramachandra, Raghavendra
NTNU, Gjovik, Norway
Kirchbuchner, Florian
TU Darmstadt GRIS
Kuijper, Arjan
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Zeitschrift
Image and Vision Computing
Project(s)
ATHENE
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Thumbnail Image
DOI
10.1016/j.imavis.2020.104007
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • Lead Topic: Smart Cit...

  • Lead Topic: Visual Co...

  • Research Line: Comput...

  • Research Line: Human ...

  • Biometrics

  • Head mounted displays...

  • Iris recognition

  • Image generation

  • ATHENE

  • CRISP

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