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Biometric Recognition via Eye Movements: Saccadic Vigor and Acceleration Cues

Authors:

Oleg Komogortsev,

Reza ShadmehrAuthors Info & Claims

ACM Transactions on Applied Perception (TAP), Volume 13, Issue 2

Article No.: 6, Pages 1 - 21

https://doi.org/10.1145/2842614

Published: 29 January 2016 Publication History

Abstract

Previous research shows that human eye movements can serve as a valuable source of information about the structural elements of the oculomotor system and they also can open a window to the neural functions and cognitive mechanisms related to visual attention and perception. The research field of eye movement-driven biometrics explores the extraction of individual-specific characteristics from eye movements and their employment for recognition purposes. In this work, we present a study for the incorporation of dynamic saccadic features into a model of eye movement-driven biometrics. We show that when these features are added to our previous biometric framework and tested on a large database of 322 subjects, the biometric accuracy presents a relative improvement in the range of 31.6--33.5% for the verification scenario, and in range of 22.3--53.1% for the identification scenario. More importantly, this improvement is demonstrated for different types of visual stimulus (random dot, text, video), indicating the enhanced robustness offered by the incorporation of saccadic vigor and acceleration cues.

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Index Terms

Biometric Recognition via Eye Movements: Saccadic Vigor and Acceleration Cues
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Transactions on Applied Perception

ACM Transactions on Applied Perception Volume 13, Issue 2

March 2016

90 pages

ISSN:1544-3558

EISSN:1544-3965

DOI:10.1145/2888406

Editors:
Victoria Interrante
University of Minnesota, USA
,
Diego Gutierrez
Universidad de Zaragoza, Spain

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Copyright © 2016 ACM.

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Publication History

Published: 29 January 2016

Accepted: 01 October 2015

Revised: 01 October 2015

Received: 01 June 2015

Published in TAP Volume 13, Issue 2

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Garcia De La Santa AMuguerza Rivero JLopez DElordi UUnzueta LVillanueva A(2024)Unsupervised data labeling and incremental cross-domain training for enhanced hybrid eye gaze estimationProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3656367(1-7)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3649902.3656367
Borovska Pde Haas B(2023)Faces in scenes attract rapid saccadesJournal of Vision10.1167/jov.23.8.1123:8(11)Online publication date: 8-Aug-2023
https://doi.org/10.1167/jov.23.8.11
Abdrabou YMecke LRivu RPrange SNguyen QVoigt VAlt FPfeuffer K(2023)How Unique do we Move? Understanding the Human Body and Context Factors for User IdentificationProceedings of Mensch und Computer 202310.1145/3603555.3603574(127-137)Online publication date: 3-Sep-2023
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Garcia De La Santa Ramos ACabeza RVillanueva A(2023)Calibration free eye tracking solution for mobile and embedded devicesProceedings of the 2023 Symposium on Eye Tracking Research and Applications10.1145/3588015.3589539(1-3)Online publication date: 30-May-2023
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