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Fusing Iris and Periocular Region for User Verification in Head Mounted Displays

Boutros, Fadi and Damer, Naser and Raja, Kiran and Ramachandra, Raghavendra and Kirchbuchner, Florian and Kuijper, Arjan (2020):
Fusing Iris and Periocular Region for User Verification in Head Mounted Displays.
pp. 1-8, IEEE, 23rd International Conference on Information Fusion (FUSION 2020), virtual Conference, 06.-09.07., ISBN 978-1-7281-6830-2,
DOI: 10.23919/FUSION45008.2020.9190282,
[Conference or Workshop Item]

Abstract

The growing popularity of Virtual Reality and Augmented Reality (VR/AR) devices in many applications also demands authentication of users. As the devices inherently capture the eye image while capturing the user interaction, the authentication can be devised using the iris and periocular recognition. While both iris and periocular data being non-ideal unlike the data captured from standard biometric sensors, the authentication performance is expected to be lower. In this work, we present and evaluate a fusion framework for improving the biometric authentication performance. Specifically, we employ score-level fusion for two independent biometric systems of iris and periocular region to avoid expensive feature-level fusion. With a detailed evaluation of three different score-level fusion after the score normalization on a dataset of 12579 images, we report the performance gain in authentication using score-level fusion for iris and periocular recognition.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Boutros, Fadi and Damer, Naser and Raja, Kiran and Ramachandra, Raghavendra and Kirchbuchner, Florian and Kuijper, Arjan
Title: Fusing Iris and Periocular Region for User Verification in Head Mounted Displays
Language: English
Abstract:

The growing popularity of Virtual Reality and Augmented Reality (VR/AR) devices in many applications also demands authentication of users. As the devices inherently capture the eye image while capturing the user interaction, the authentication can be devised using the iris and periocular recognition. While both iris and periocular data being non-ideal unlike the data captured from standard biometric sensors, the authentication performance is expected to be lower. In this work, we present and evaluate a fusion framework for improving the biometric authentication performance. Specifically, we employ score-level fusion for two independent biometric systems of iris and periocular region to avoid expensive feature-level fusion. With a detailed evaluation of three different score-level fusion after the score normalization on a dataset of 12579 images, we report the performance gain in authentication using score-level fusion for iris and periocular recognition.

Publisher: IEEE
ISBN: 978-1-7281-6830-2
Uncontrolled Keywords: Biometrics, Information fusion, Deep learning, Head mounted displays, Iris recognition
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 23rd International Conference on Information Fusion (FUSION 2020)
Event Location: virtual Conference
Event Dates: 06.-09.07.
Date Deposited: 22 Sep 2020 14:13
DOI: 10.23919/FUSION45008.2020.9190282
Official URL: https://ieeexplore.ieee.org/document/9190282
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