Webcam-Based Visual Gaze Estimation

Valenti, Roberto; Staiano, Jacopo; Sebe, Nicu; Gevers, Theo

doi:10.1007/978-3-642-04146-4_71

Cited by 35 publications

(35 citation statements)

References 11 publications

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“…In general, most methods avoid this problem by assuming that the head does not move at all and assume that the eyes do not rotate in the ocular cavities but just shift on the horizontal and vertical planes [45]. In this way, the problem of eye displacement is simply solved by a 2-D mapping of the location of the pupil (with respect to an arbitrary anchor point) and known locations on the screen.…”

Section: B Pose-retargeted Gaze Estimationmentioning

confidence: 99%

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Combining Head Pose and Eye Location Information for Gaze Estimation

Valenti

Sebe

Gevers

2012

IEEE Trans. on Image Process.

301

136

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Abstract-Head pose and eye location for gaze estimation have been separately studied in numerous works in the literature. Previous research shows that satisfactory accuracy in head pose and eye location estimation can be achieved in constrained settings. However, in the presence of nonfrontal faces, eye locators are not adequate to accurately locate the center of the eyes. On the other hand, head pose estimation techniques are able to deal with these conditions; hence, they may be suited to enhance the accuracy of eye localization. Therefore, in this paper, a hybrid scheme is proposed to combine head pose and eye location information to obtain enhanced gaze estimation. To this end, the transformation matrix obtained from the head pose is used to normalize the eye regions, and in turn, the transformation matrix generated by the found eye location is used to correct the pose estimation procedure. The scheme is designed to enhance the accuracy of eye location estimations, particularly in low-resolution videos, to extend the operative range of the eye locators, and to improve the accuracy of the head pose tracker. These enhanced estimations are then combined to obtain a novel visual gaze estimation system, which uses both eye location and head information to refine the gaze estimates. From the experimental results, it can be derived that the proposed unified scheme improves the accuracy of eye estimations by 16% to 23%. Furthermore, it considerably extends its operating range by more than 15 by overcoming the problems introduced by extreme head poses. Moreover, the accuracy of the head pose tracker is improved by 12% to 24%. Finally, the experimentation on the proposed combined gaze estimation system shows that it is accurate (with a mean error between 2 and 5 ) and that it can be used in cases where classic approaches would fail without imposing restraints on the position of the head.Index Terms-Eye center location, gaze estimation, head pose estimation. I. MOTIVATION AND RELATED WORK IMAGE-BASED gaze estimation is important in many applications, spanning from human-computer interaction (HCI) to human behavior analysis. In applications where human activity is under observation from a static camera, the There is an abundance of literature concerning these two topics separately; recent surveys on eye center location and head pose estimation can be found in [17] and [31]. The eye location algorithms found in commercially available eye trackers share the problem of sensitivity to head pose variations and require the user to be either equipped with a head-mounted device or to use a high-resolution camera combined with a chin rest to limit the allowed head movement. Furthermore, daylight applications are precluded due to the use of active infrared (IR) illumination to obtain accurate eye location through corneal reflection. The appearance-based methods that make use of standard low-resolution cameras are considered to be less invasive and, thus, more desirable in a large range of applications. Within the appearance-based...

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Section: B Pose-retargeted Gaze Estimationmentioning

confidence: 99%

“…1) Eyes-only gaze estimator: This estimator uses the anchor-pupil vectors directly into the mapping as in the system proposed in [45]. Hence, when the user moves his head from the calibration position, the mapping is bound to fail.…”

Section: Visual Gaze Estimationmentioning

confidence: 99%

Combining Head Pose and Eye Location Information for Gaze Estimation

Valenti

Sebe

Gevers

2012

IEEE Trans. on Image Process.

301

136

View full text Add to dashboard Cite

show abstract

“…According to the approach used for relating the image features with gaze direction, feature-based methods can be further divided into geometric (or model based) and interpolation based (or regression based). Geometric methods [4,[17][18][19][20] compute directly the gaze direction from the image features based on a geometric model of the eye, while interpolation based methods [21][22][23][24][25][26] built a mapping function between them, using parametric (e.g. polynomial) or non-parametric forms (e.g.…”

Section: Gaze Estimation Methodsologiesmentioning

confidence: 99%

On visual gaze tracking based on a single low cost camera

Skodras

Kanas

Fakotakis

2015

Signal Processing: Image Communication

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“…Similar to older EEG headsets, eye gaze trackers consisted of mounting superfluous amounts of equipment on the head such as a camera, glasses, reflective plates and camera mounts [18,19]. Recently there have been improvements to eye gaze software which has made it possible to track eye gaze from video without calibration [18] and from standard low resolution web cameras [20,21]. Compared to more sophisticated systems involving special glasses or other hardware, later systems minimise eye distractions that may otherwise influence the test results.…”

Section: Eye Gaze Trackingmentioning

confidence: 99%

“…The IR light source causes only the cornea to reflect the light back at the camera. This creates a distinct pupil in the image which can then be segmented from the image using software algorithms that recognise the pupil and edges of the eye [20]. The work in this paper uses the ITU Gaze Tracker (GT) [24], which has been shown to provide accurate performance [21], to collect eye gaze data as a user views videos.…”

Section: Eye Gaze Trackingmentioning

confidence: 99%

Analysing the Quality of Experience of multisensory media from measurements of physiological responses

Donley

Ritz

Shujau

2014

2014 Sixth International Workshop on Quality of Multimedia Experience (QoMEX)

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This paper investigates the Quality of Experience (QoE) of multisensory media by analysing biosignals collected by electroencephalography (EEG) and eye gaze sensors and comparing with subjective ratings. Also investigated is the impact on QoE of various levels of synchronicity between the sensory effect and target video scene. Results confirm findings from previous research that show sensory effects added to videos increases the QoE rating. While there was no statistical difference observed for the QoE ratings for different levels of sensory effect synchronicity, an analysis of raw EEG data showed 25% more activity in the temporal lobe during asynchronous effects and 20-25% more activity in the occipital lobe during synchronous effects. The eye gaze data showed more deviation for a video with synchronous effects and the EEG showed correlating occipital lobe activity for this instance. These differences in physiological responses indicate sensory effect synchronicity may affect QoE despite subjective ratings appearing similar. ABSTRACTThis paper investigates the Quality of Experience (QoE) of multisensory media by analysing biosignals collected by electroencephalography (EEG) and eye gaze sensors and comparing with subjective ratings. Also investigated is the impact on QoE of various levels of synchronicity between the sensory effect and target video scene. Results confirm findings from previous research that show sensory effects added to videos increases the QoE rating. While there was no statistical difference observed for the QoE ratings for different levels of sensory effect synchronicity, an analysis of raw EEG data showed 25% more activity in the temporal lobe during asynchronous effects and 20-25% more activity in the occipital lobe during synchronous effects. The eye gaze data showed more deviation for a video with synchronous effects and the EEG showed correlating occipital lobe activity for this instance. These differences in physiological responses indicate sensory effect synchronicity may affect QoE despite subjective ratings appearing similar.

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Webcam-Based Visual Gaze Estimation

Cited by 35 publications

References 11 publications

Combining Head Pose and Eye Location Information for Gaze Estimation

Combining Head Pose and Eye Location Information for Gaze Estimation

On visual gaze tracking based on a single low cost camera

Analysing the Quality of Experience of multisensory media from measurements of physiological responses

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