Eye Movement Analysis in Biometrics

Galdi, Chiara; Nappi, Michele

doi:10.1007/978-981-13-1144-4_8

Cited by 3 publications

(2 citation statements)

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“…Low-level tasks can include, for example, reading text displayed on a computer screen or watching a point moving on a screen [29]. Most commonly, the mentioned "jumping points" are used as stimuli.…”

Section: B Visual Stimulimentioning

confidence: 99%

“…The user's task is to focus their gaze on successive points. In this way, it is possible to collect both parameters related to fixations (focusing the gaze on a point) and saccades (when the user moves the eye from point to point) [29]. The "jumping points" approach was adopted by many authors, usually proving to be effective [26], [27], [31].…”

Section: B Visual Stimulimentioning

confidence: 99%

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User Authentication by Eye Movement Features Employing SVM and XGBoost Classifiers

Odya,

Górski,

Czyżewski

2023

IEEE Access

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Devices capable of tracking the user's gaze have become significantly more affordable over the past few years, thus broadening their application, including in-home and office computers and various customer service equipment. Although such devices have comparatively low operating frequencies and limited resolution, they are sufficient to supplement or replace classic input interfaces, such as the keyboard and mouse. The biometric application we researched verifies a user's identity based on parameters acquired with a low-cost eye tracker. The use of the eye-tracking device in bank booths has many advantages, including the fact that eye trackers are contactless devices, which, especially in the light of the Covid pandemic, has increasing importance, in addition to providing a solution for confirming the liveness of the user. This paper describes an experiment in which 20 features extracted from eye movement data related mainly to saccades and fixations are used as a complementary biometric modality to authenticate clients at banking kiosks. Data were collected from 39 subjects while operating a banking system using engineered biometric kiosk prototypes. Authentication performance employing eye-movement tracking and parameterizing was compared for two classifiers: Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost). The results showed that the XGBoost-based classifier outperformed the SVM-based one regarding equal error rates (6.76% to 8% vs. 16.21 to 18.78%). Similar differences were obtained for true acceptance rates at different false acceptance rates (0.1 and 0.01), where the SVM-based classifier achieved a maximum of 81.08% and the XGBoost-based achieved 98.65%. Finally, prospects for the broader application of eye movement tracking as a biometric modality are discussed.INDEX TERMS Biometrics, gaze tracking, machine learning, support vector machines, XGBoost.

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Section: B Visual Stimulimentioning

confidence: 99%

Section: B Visual Stimulimentioning

confidence: 99%

User Authentication by Eye Movement Features Employing SVM and XGBoost Classifiers

Odya,

Górski,

Czyżewski

2023

IEEE Access

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Biometric as Secure Authentication for Virtual Reality Environment: A Systematic Literature Review

Heruatmadja

Meyliana²,

Hidayanto

et al. 2023

2023 International Conference for Advancement in Technology (ICONAT)

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EyeQoE

Zhu

Wang

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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As virtual reality (VR) offers an unprecedented experience than any existing multimedia technologies, VR videos, or called 360-degree videos, have attracted considerable attention from academia and industry. How to quantify and model end users' perceived quality in watching 360-degree videos, or called QoE, resides the center for high-quality provisioning of these multimedia services. In this work, we present EyeQoE, a novel QoE assessment model for 360-degree videos using ocular behaviors. Unlike prior approaches, which mostly rely on objective factors, EyeQoE leverages the new ocular sensing modality to comprehensively capture both subjective and objective impact factors for QoE modeling. We propose a novel method that models eye-based cues into graphs and develop a GCN-based classifier to produce QoE assessment by extracting intrinsic features from graph-structured data. We further exploit the Siamese network to eliminate the impact from subjects and visual stimuli heterogeneity. A domain adaptation scheme named MADA is also devised to generalize our model to a vast range of unseen 360-degree videos. Extensive tests are carried out with our collected dataset. Results show that EyeQoE achieves the best prediction accuracy at 92.9%, which outperforms state-of-the-art approaches. As another contribution of this work, we have publicized our dataset on https://github.com/MobiSec-CSE-UTA/EyeQoE_Dataset.git.

show abstract

Eye Movement Analysis in Biometrics

Cited by 3 publications

References 30 publications

User Authentication by Eye Movement Features Employing SVM and XGBoost Classifiers

User Authentication by Eye Movement Features Employing SVM and XGBoost Classifiers

Biometric as Secure Authentication for Virtual Reality Environment: A Systematic Literature Review

EyeQoE

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