A Survey of Automatic Person Recognition Using Eye Movements

Saeed, Usman

doi:10.1142/s0218001414560151

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…A number of surveys served as a starting point for our review [41,47,111,117,138,145,174]. Additionally, we used the following search terms and all their combinations to obtain the papers that formed the basis of our literature review:…”

Section: Methodsmentioning

confidence: 99%

The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

Katsini¹,

Abdrabou

Raptis³

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

106

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For the past 20 years, researchers have investigated the use of eye tracking in security applications. We present a holistic view on gaze-based security applications. In particular, we canvassed the literature and classify the utility of gaze in security applications into a) authentication, b) privacy protection, and c) gaze monitoring during security critical tasks. This allows us to chart several research directions, most importantly 1) conducting field studies of implicit and explicit gaze-based authentication due to recent advances in eye tracking, 2) research on gaze-based privacy protection and gaze monitoring in security critical tasks which are under-investigated yet very promising areas, and 3) understanding the privacy implications of pervasive eye tracking. We discuss the most promising opportunities and most pressing challenges of eye tracking for security that will shape research in gaze-based security applications for the next decade.

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Section: Methodsmentioning

confidence: 99%

The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

Katsini¹,

Abdrabou

Raptis³

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

106

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show abstract

“…It was successfully used in many classification problems (pattern recognition, bioinformatics), in these, in field of eye movement data processing as well [2,19].…”

Section: Feature Extraction With Principal Component Analysismentioning

confidence: 99%

“…In the research described in the paper the problem of dimensionality reduction has been applied into analysis of eye movement data for the purpose of biometric classification [19]. Two methods have been considered: PCA [6] method combined with SVM classifier and random-forest based procedure [5].…”

Section: Introductionmentioning

confidence: 99%

Application of Dimensionality Reduction Methods for Eye Movement Data Classification

Gruca

Harężlak

Kasprowski

2015

Advances in Intelligent Systems and Computing

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In this paper we apply two data dimensionality reduction methods to eye movement dataset and analyse how the feature reduction method improves classification accuracy. Due to the specificity of the recording process, eye movement datasets are characterized by both big size and high-dimensionality that make them difficult to analyse and classify using standard classification approaches. Here, we analyse eye movement data from BioEye 2015 competition and to deal with the problem of high dimensionality we apply SVM combined with PCA feature extraction and random forests wrapper variable selection. Our results show that the reduction of the number of variables improves classification results. We also show that some of classes (participants) can be classified (recognised) with high accuracy while others are very difficult to be correctly identified.

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“…A comprehensive survey on the use of eye movements for biometric identification has been compiled by me in Saeed [24]. During the survey I was unable to find any study utilizing eye movements extracted from scene understanding based task for biometric identification.…”

Section: Introductionmentioning

confidence: 99%