Appearance-Based Gaze Estimation Using Visual Saliency

Sugano, Yusuke; Matsushita, Yasuyuki; Sato, Yasuomi D.

doi:10.1109/tpami.2012.101

Cited by 170 publications

(104 citation statements)

References 25 publications

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“…An object of interest is salient if it is rare or novel to the surroundings. A variety of applications can be benefited from saliency modeling, e.g., object detection [2][3], image segmentation [4] [5], image retargeting [6] [7], image/video compression [8] [9], visual tracking [10][11], gaze estimation [12], robot navigation [13], image/video quality assessment [14] [15], and advertising design [16].…”

Section: Introductionmentioning

confidence: 99%

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Mahdi

Schlesinger

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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Abstract-Various saliency models have been developed over the years. The performance of saliency models is typically evaluated based on databases of experimentally recorded adult eye fixations. Although studies on infant gaze patterns have attracted much attention recently, saliency based models have not been widely applied for prediction of infant gaze patterns. In this study, we conduct a comprehensive comparison study of eight state-ofthe-art saliency models on predictions of experimentally captured fixations from infants and adults. Seven evaluation metrics are used to evaluate and compare the performance of saliency models. The results demonstrate a consistent performance of saliency models predicting adult fixations over infant fixations in terms of overlap, center fitting, intersection, information loss of approximation, and spatial distance between the distributions of saliency map and fixation map. In saliency and baselines models performance ranking, the results show that GBVS and Itti models are among the top three contenders, infants and adults have bias toward the centers of images, and all models and the center baseline model outperformed the chance baseline model.

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Section: Introductionmentioning

confidence: 99%

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Mahdi

Schlesinger

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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“…For each of the ten stimuli, the first corresponding web camera frame is taken as an input by the landmarker (Zhu and Ramanan 2012) to detect the eye corners. In Sugano et al (2013), the eye corners are detected using the OMRON OKAO vision library. To detect the eye corners for the subsequent frames, we apply template matching using the eye corners of the first frame (for each stimulus) as templates.…”

Section: Resultsmentioning

confidence: 99%

“…Clearly, the comparison with this method is not feasible as the authors use different equipment to reconstruct an accurate 3D eye model. Sugano et al (2013) adopt an appearance-based gaze estimator and use visual saliency for auto-calibration. The authors reported an error of 3.5 • .…”

Section: Comparison To the State-of-the-artmentioning

confidence: 99%

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Auto-Calibrated Gaze Estimation Using Human Gaze Patterns

et al. 2017

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We present a novel method to auto-calibrate gaze estimators based on gaze patterns obtained from other viewers. Our method is based on the observation that the gaze patterns of humans are indicative of where a new viewer will look at. When a new viewer is looking at a stimulus, we first estimate a topology of gaze points (initial gaze points). Next, these points are transformed so that they match the gaze patterns of other humans to find the correct gaze points. In a flexible uncalibrated setup with a web camera and no chin rest, the proposed method is tested on ten subjects and ten images. The method estimates the gaze points after looking at a stimulus for a few seconds with an average error below 4.5 • . Although the reported performance is lower than what could be achieved with dedicated hardware or calibrated setup, the proposed method still provides sufficient accuracy to trace the viewer attention. This is promising considering the fact that auto-calibration is done in a flexible setup , without the use of a chin rest, and based only on a few seconds of gaze initialization data. To the best of our knowledge, this is the first work to use human gaze patterns in order to auto-calibrate gaze estimators.

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“…Secondly, while user-specific models are usually better, user adaptation often rely on a manual calibration requiring user cooperation or manual data processing [5]. Unsupervised methods based on visual saliency have been considered, but they are mainly restricted to screen gazing application [24].…”

Section: Introductionmentioning

confidence: 99%

Towards the use of social interaction conventions as prior for gaze model adaptation

Siegfried

Odobez

2017

Proceedings of the 19th ACM International Conference on Multimodal Interaction

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Gaze is an important non-verbal cue involved in many facets of social interactions like communication, attentiveness or attitudes. Nevertheless, extracting gaze directions visually and remotely usually suffers large errors because of low resolution images, inaccurate eye cropping, or large eye shape variations across the population, amongst others. This paper hypothesizes that these challenges can be addressed by exploiting multimodal social cues for gaze model adaptation on top of an head-pose independent 3D gaze estimation framework. First, a robust eye cropping refinement is achieved by combining a semantic face model with eye landmark detections. Investigations on whether temporal smoothing can overcome instantaneous refinement limitations is conducted. Secondly, to study whether social interaction convention could be used as priors for adaptation, we exploited the speaking status and head pose constraints to derive soft gaze labels and infer person-specific gaze bias using robust statistics. Experimental results on gaze coding in natural interactions from two different settings demonstrate that the two steps of our gaze adaptation method contribute to reduce gaze errors by a large margin over the baseline and can be generalized to several identities in challenging scenarios.

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Appearance-Based Gaze Estimation Using Visual Saliency

Cited by 170 publications

References 25 publications

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Auto-Calibrated Gaze Estimation Using Human Gaze Patterns

Towards the use of social interaction conventions as prior for gaze model adaptation

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