Current research in eye movement biometrics: An analysis based on BioEye 2015 competition

Rigas, Ioannis; Komogortsev, Oleg V.

doi:10.1016/j.imavis.2016.03.014

Cited by 31 publications

(6 citation statements)

References 63 publications

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“…However, even the earliest studies of fixation behaviour noted considerable individual differences 19,20 , and basic occulomotor traits vary reliably between observers [21][22][23][24][25][26] . Recent twinstudies revealed that social attention and gaze traces across complex scenes are highly heritable 27,28 .…”

Section: Introductionmentioning

confidence: 99%

Individual differences in visual salience vary along semantic dimensions

Haas

Iakovidis

Schwarzkopf

et al. 2018

Preprint

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150 words; Main text: 2615 words (excl. Methods, fig. and table captions); Methods: 17 1526 words; 5 figures:1 table; 1 supplementary figure 18 19 20 21 22 23 features and task demands govern fixation behaviour, while differences between observers 24 are 'noise'. Here, we investigated the fixations of > 100 human adults freely viewing a large 25 set of complex scenes. We found systematic individual differences in fixation frequencies 26 along six semantic stimulus dimensions. These differences were large (> twofold) and 27 highly stable across images and time. Surprisingly, they also held for first fixations directed 28 towards each image, commonly interpreted as 'bottom-up' visual salience. Their 29 perceptual relevance was documented by a correlation between individual face salience and 30 recognition skills. The dimensions of individual salience and their covariance pattern 31 replicated across samples from three different countries, suggesting they reflect 32 fundamental biological mechanisms of attention. Our findings show stable individual 33 salience differences along semantic dimensions, with meaningful perceptual implications. 34 Salience reflects features of the observer as well as the image. 35 36 image discontinuities of low level attributes, such as luminance, colour and orientation 13 . These 45 low-level models are inspired by 'early' visual neurons and their output correlates with neural 46 responses in subcortical 14 and cortical 15 areas thought to represent neural 'salience maps'. 47However, while these models work relatively well for impoverished stimuli, human gaze 48 behaviour towards richer scenes can be predicted at least as well by the locations of objects 16 and 49 perceived meaning 9 . When sematic object properties are taken into account, their weight for gaze 50 prediction far exceeds that of low-level attributes 8,17 . A common thread of low-and high-level 51 salience models is that they interpret salience as a property of the image and treat inter-individual 52 differences as unpredictable 7,18 , often using them as a 'noise ceiling' for model evaluations 18 . 53 However, even the earliest studies of fixation behaviour noted considerable individual 54 differences 19,20 and basic occulomotor traits, like mean saccadic amplitude and velocity, reliably 55 vary between observers 21-27 . Moreover, recent twin-studies revealed that social attention and 56 gaze traces across complex scenes are highly heritable 28,29 . This suggests individual differences 57 in fixation behaviour are not random, but systematic. However, it is largely unclear, how 58 individuals differ in their fixation behaviour and what may explain these differences. Can 59 individual fixation behaviour be captured along a limited set of dimensions? 60Here, we tested the hypothesis that individual gaze reflects individual salience differences 61 along a limited number of semantic dimensions. We investigated the fixation behaviour of > 100 62 human adults freely viewing 700 complex scenes, containing thousands of semantica...

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Section: Introductionmentioning

confidence: 99%

Individual differences in visual salience vary along semantic dimensions

Haas

Iakovidis

Schwarzkopf

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…However, even the earliest studies of fixation behavior noted considerable individual differences (19, 20), which recently gained wide-spread interest, ranging from behavioral genetics to computer science. Basic occulomotor traits, like mean saccadic amplitude and velocity, reliably vary between observers (21–28). Gaze predictions based on artificial neural networks can improve when being trained on individual data (28, 29), or taking observer properties like age into account (30).…”

mentioning

confidence: 99%

Individual differences in visual salience vary along semantic dimensions

Haas

Iakovidis

Schwarzkopf

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

107

104

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What determines where we look? Theories of attentional guidance hold that image features and task demands govern fixation behavior, while differences between observers are interpreted as a “noise-ceiling” that strictly limits predictability of fixations. However, recent twin studies suggest a genetic basis of gaze-trace similarity for a given stimulus. This leads to the question of how individuals differ in their gaze behavior and what may explain these differences. Here, we investigated the fixations of >100 human adults freely viewing a large set of complex scenes containing thousands of semantically annotated objects. We found systematic individual differences in fixation frequencies along six semantic stimulus dimensions. These differences were large (>twofold) and highly stable across images and time. Surprisingly, they also held for first fixations directed toward each image, commonly interpreted as “bottom-up” visual salience. Their perceptual relevance was documented by a correlation between individual face salience and face recognition skills. The set of reliable individual salience dimensions and their covariance pattern replicated across samples from three different countries, suggesting they reflect fundamental biological mechanisms of attention. Our findings show stable individual differences in salience along a set of fundamental semantic dimensions and that these differences have meaningful perceptual implications. Visual salience reflects features of the observer as well as the image.

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“…A collective review of related work published prior to 2015 may be found in [18]. Moreover, comparative results for studies analyzing common datasets are provided in [19], which summarizes the results of the most recent BioEye competition. As noted within these reviews, the majority of prior work uses a common processing pipeline, with the recordings initially partitioned into specific eye movement events using a classification algorithm, followed by the formation of the biometric template as a vector of discrete features from each event.…”

Section: Prior Workmentioning

confidence: 99%

Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Dataset

Lohr¹,

Griffith²,

Komogortsev³

2021

Preprint

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While numerous studies have explored eye movement biometrics since the modality's inception in 2004, the permanence of eye movements remains largely unexplored as most studies utilize datasets collected within a short time frame. This paper presents a convolutional neural network for authenticating users using their eye movements. The network is trained with an established metric learning loss function, multi-similarity loss, which seeks to form a well-clustered embedding space and directly enables the enrollment and authentication of out-of-sample users. Performance measures are computed on GazeBase, a task-diverse and publicly-available dataset collected over a 37-month period. This study includes an exhaustive analysis of the effects of training on various tasks and downsampling from 1000 Hz to several lower sampling rates. Our results reveal that reasonable authentication accuracy may be achieved even during a low-cognitive-load task or at low sampling rates. Moreover, we find that eye movements are quite resilient against template aging after 3 years.

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Current research in eye movement biometrics: An analysis based on BioEye 2015 competition

Cited by 31 publications

References 63 publications

Individual differences in visual salience vary along semantic dimensions

Individual differences in visual salience vary along semantic dimensions

Individual differences in visual salience vary along semantic dimensions

Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Dataset

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