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Iris and periocular biometrics for head mounted displays: Segmentation, recognition, and synthetic data generation

Boutros, Fadi and Damer, Naser and Raja, Kiran and Ramachandra, Raghavendra and Kirchbuchner, Florian and Kuijper, Arjan (2020):
Iris and periocular biometrics for head mounted displays: Segmentation, recognition, and synthetic data generation.
In: Image and Vision Computing, 104, pp. 1-24. Elsevier ScienceDirect, ISSN 02628856,
DOI: 10.1016/j.imavis.2020.104007,
[Article]

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.

Item Type: Article
Erschienen: 2020
Creators: Boutros, Fadi and Damer, Naser and Raja, Kiran and Ramachandra, Raghavendra and Kirchbuchner, Florian and Kuijper, Arjan
Title: Iris and periocular biometrics for head mounted displays: Segmentation, recognition, and synthetic data generation
Language: English
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.

Journal or Publication Title: Image and Vision Computing
Journal volume: 104
Publisher: Elsevier ScienceDirect
Uncontrolled Keywords: Biometrics, Head mounted displays, Iris recognition, Image generation
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 22 Sep 2020 13:35
DOI: 10.1016/j.imavis.2020.104007
Additional Information:

Art.104007

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