Eye-based head gestures

Mardanbegi, Diako; Hansen, Dan Witzner; Pederson, Thomas

doi:10.1145/2168556.2168578

Cited by 58 publications

(30 citation statements)

References 12 publications

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“…In contrast to [Mardanbegi et al 2012] where authors used a head-mounted eye tracker, we concluded that no algorithms to filter out eye movements from EP CV are needed. Also, we found that horizontal and vertical head movements of small amplitude did not notably influence the measured point of gaze.…”

Section: Head Movementscontrasting

confidence: 60%

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Look and lean

Špakov

Isokoski

Majaranta

2014

Proceedings of the Symposium on Eye Tracking Research and Applications

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Compared to the mouse, eye pointing is inaccurate. As a consequence, small objects are difficult to point by gaze alone. We suggest using a combination of eye pointing and subtle head movements to achieve accurate hands-free pointing in a conventional desktop computing environment. For tracking the head movements, we exploited information of the eye position in the eye tracker's camera view. We conducted a series of three experiments to study the potential caveats and benefits of using head movements to adjust gaze cursor position. Results showed that head-assisted eye pointing significantly improves the pointing accuracy without a negative impact on the pointing time. In some cases participants were able to point almost 3 times closer to the target's center, compared to the eye pointing alone (7 vs. 19 pixels). We conclude that head assisted eye pointing is a comfortable and potentially very efficient alternative for other assisting methods in the eye pointing, such as zooming.

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Section: Head Movementscontrasting

confidence: 60%

“…To our knowledge, the only attempts to exploit this parameter are the recent studies [Mardanbegi et al 2012;Špakov and Majaranta 2012] where the eye position in the camera view was used to implement simple head gestures that could enhance gaze interaction. For example, a simple nod could be used to select the target pointed by gaze.…”

Section: Introductionmentioning

confidence: 99%

Look and lean

Špakov

Isokoski

Majaranta

2014

Proceedings of the Symposium on Eye Tracking Research and Applications

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“…Mardenbegi et al demonstrated the use of head gestures in combination with gaze to interact with applications on a public display [15]. This work followed the same principles as in the previously described work on pointing.…”

Section: Multi-modal Gaze Interaction With Public Displaysmentioning

confidence: 94%

Eye Pull, Eye Push: Moving Objects between Large Screens and Personal Devices with Gaze and Touch

Turner

Alexander

Bulling

et al. 2013

Human-Computer Interaction – INTERACT 2013

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“…Head-worn eyetracking has been used to map gaze to any planar digital display in a real-world environment [Mardanbegi and Hansen 2011]. Further work used nodding gestures combined with gaze to issue commands in remote applications [Mardanbegi et al 2012]. Interaction with eye-gaze on portable devices poses additional challenges.…”

Section: Gaze-supported Interactionmentioning

confidence: 99%

Cross-device gaze-supported point-to-point content transfer

Turner

Bulling

Alexander

et al. 2014

Proceedings of the Symposium on Eye Tracking Research and Applications

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Within a pervasive computing environment, we see content on shared displays that we wish to acquire and use in a specific way i.e., with an application on a personal device, transferring from point-to-point. The eyes as input can indicate intention to interact with a service, providing implicit pointing as a result. In this paper we investigate the use of gaze and manual input for the positioning of gaze-acquired content on personal devices. We evaluate two main techniques, (1) Gaze Positioning, transfer of content using gaze with manual input to confirm actions, (2) Manual Positioning, content is selected with gaze but final positioning is performed by manual input, involving a switch of modalities from gaze to manual input. A first user study compares these techniques applied to direct and indirect manual input configurations, a tablet with touch input and a laptop with mouse input. A second study evaluated our techniques in an application scenario involving distractor targets. Our overall results showed general acceptance and understanding of all conditions, although there were clear individual user preferences dependent on familiarity and preference toward gaze, touch, or mouse input.

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Eye-based head gestures

Cited by 58 publications

References 12 publications

Look and lean

Look and lean

Eye Pull, Eye Push: Moving Objects between Large Screens and Personal Devices with Gaze and Touch

Cross-device gaze-supported point-to-point content transfer

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