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Periocular Biometrics in Head-Mounted Displays: A Sample Selection Approach for Better Recognition

Boutros, Fadi and Damer, Naser and Raja, Kiran and Ramachandra, Raghavendra and Kirchbuchner, Florian and Kuijper, Arjan (2020):
Periocular Biometrics in Head-Mounted Displays: A Sample Selection Approach for Better Recognition.
In: 2020 8th International Workshop on Biometrics and Forensics (IWBF), pp. 1-6,
Los Alamitos, Calif., Porto, Portugal, 29-30 April 2020, DOI: 10.1109/IWBF49977.2020.9107939,
[Conference or Workshop Item]

Abstract

Virtual and augmented reality technologies are increasingly used in a wide range of applications. Such technologies employ a Head Mounted Display (HMD) that typicallyincludes an eye-facing camera and is used for eye tracking.As some of these applications require accessing or transmittinghighly sensitive private information, a trusted verification ofthe operator’s identity is needed. We investigate the use ofHMD-setup to perform verification of operator using periocularregion captured from inbuilt camera. However, the uncontrollednature of the periocular capture within the HMD results inimages with a high variation in relative eye location and eyeopening due to varied interactions. Therefore, we propose a newnormalization scheme to align the ocular images and then, a newreference sample selection protocol to achieve higher verificationaccuracy. The applicability of our proposed scheme is exemplifiedusing two handcrafted feature extraction methods and two deeplearning strategies.We conclude by stating the feasibility of sucha verification approach despite the uncontrolled nature of thecaptured ocular images, especially when proper alignment andsample selection strategy is employed.

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: Periocular Biometrics in Head-Mounted Displays: A Sample Selection Approach for Better Recognition
Language: English
Abstract:

Virtual and augmented reality technologies are increasingly used in a wide range of applications. Such technologies employ a Head Mounted Display (HMD) that typicallyincludes an eye-facing camera and is used for eye tracking.As some of these applications require accessing or transmittinghighly sensitive private information, a trusted verification ofthe operator’s identity is needed. We investigate the use ofHMD-setup to perform verification of operator using periocularregion captured from inbuilt camera. However, the uncontrollednature of the periocular capture within the HMD results inimages with a high variation in relative eye location and eyeopening due to varied interactions. Therefore, we propose a newnormalization scheme to align the ocular images and then, a newreference sample selection protocol to achieve higher verificationaccuracy. The applicability of our proposed scheme is exemplifiedusing two handcrafted feature extraction methods and two deeplearning strategies.We conclude by stating the feasibility of sucha verification approach despite the uncontrolled nature of thecaptured ocular images, especially when proper alignment andsample selection strategy is employed.

Title of Book: 2020 8th International Workshop on Biometrics and Forensics (IWBF)
Place of Publication: Los Alamitos, Calif.
Uncontrolled Keywords: Biometrics, Head mounted displays
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 Location: Porto, Portugal
Event Dates: 29-30 April 2020
Date Deposited: 08 Jun 2020 10:16
DOI: 10.1109/IWBF49977.2020.9107939
Official URL: https://doi.org/10.1109/IWBF49977.2020.9107939
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