Dataset Information

A Validation of Automatically-Generated Areas-of-Interest in Videos of a Face for Eye-Tracking Research.

ABSTRACT: When mapping eye-movement behavior to the visual information presented to an observer, Areas of Interest (AOIs) are commonly employed. For static stimuli (screen without moving elements), this requires that one AOI set is constructed for each stimulus, a possibility in most eye-tracker manufacturers' software. For moving stimuli (screens with moving elements), however, it is often a time-consuming process, as AOIs have to be constructed for each video frame. A popular use-case for such moving AOIs is to study gaze behavior to moving faces. Although it is technically possible to construct AOIs automatically, the standard in this field is still manual AOI construction. This is likely due to the fact that automatic AOI-construction methods are (1) technically complex, or (2) not effective enough for empirical research. To aid researchers in this field, we present and validate a method that automatically achieves AOI construction for videos containing a face. The fully-automatic method uses an open-source toolbox for facial landmark detection, and a Voronoi-based AOI-construction method. We compared the position of AOIs obtained using our new method, and the eye-tracking measures derived from it, to a recently published semi-automatic method. The differences between the two methods were negligible. The presented method is therefore both effective (as effective as previous methods), and efficient; no researcher time is needed for AOI construction. The software is freely available from https://osf.io/zgmch/.

SUBMITTER: Hessels RS

PROVIDER: S-EPMC6085555 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A Validation of Automatically-Generated Areas-of-Interest in Videos of a Face for Eye-Tracking Research.

Hessels Roy S RS Benjamins Jeroen S JS Cornelissen Tim H W THW Hooge Ignace T C ITC

Frontiers in psychology 20180803

When mapping eye-movement behavior to the visual information presented to an observer, Areas of Interest (AOIs) are commonly employed. For static stimuli (screen without moving elements), this requires that one AOI set is constructed for each stimulus, a possibility in most eye-tracker manufacturers' software. For moving stimuli (screens with moving elements), however, it is often a time-consuming process, as AOIs have to be constructed for each video frame. A popular use-case for such moving AO ...[more]

PMID: 30123168

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Project description:BackgroundCriminal associates such as terrorist members are likely to deny knowing members of their network when questioned by police. Eye tracking research suggests that lies about familiar faces can be detected by distinct markers of recognition (e.g. fewer fixations and longer fixation durations) across multiple eye fixation parameters. However, the effect of explicit eye movement strategies to concealed recognition on such markers has not been examined. Our aim was to assess the impact of fixed-sequence eye movement strategies (across the forehead, ears, eyes, nose, mouth and chin) on markers of familiar face recognition. Participants were assigned to one of two groups: a standard guilty group who were simply instructed to conceal knowledge but with no specific instructions on how to do so; and a countermeasures group who were instructed to look at every familiar and unfamiliar face in the same way by executing a consistent sequence of fixations.ResultsIn the standard guilty group, lies about recognition of familiar faces showed longer average fixation durations, a lower proportion of fixations to the inner face regions, and proportionately more viewing of the eyes than honest responses to genuinely unknown faces. In the countermeasures condition, familiar face recognition was detected by longer fixations durations, fewer fixations to the inner regions of the face, and fewer interest areas of the face viewed. Longer fixation durations were a consistent marker of recognition across both conditions for most participants; differences were detectable from the first fixation.ConclusionThe results suggest that individuals can exert a degree of executive control over fixation patterns but that: the eyes are particularly attention-grabbing for familiar faces; the more viewers look around the face, the more they give themselves away; and attempts to deploy the same fixation patterns to familiar and unfamiliar faces were unsuccessful. The results suggest that the best strategy for concealing recognition might be to keep the eyes fixated in the centre of the screen but, even then, recognition is apparent in longer fixation durations. We discuss potential optimal conditions for detecting concealed knowledge of faces.

Dataset Information

A Validation of Automatically-Generated Areas-of-Interest in Videos of a Face for Eye-Tracking Research.

Publications

A Validation of Automatically-Generated Areas-of-Interest in Videos of a Face for Eye-Tracking Research.

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