Abstract
We address the problem of 3D gaze estimation within a 3D environment from remote sensors, which is highly valuable for applications in human–human and human–robot interactions. To the contrary of most previous works, which are limited to screen gazing applications, we propose to leverage the depth data of RGB-D cameras to perform an accurate head pose tracking, acquire head pose invariance through a 3D rectification process that renders head pose dependent eye images into a canonical viewpoint, and computes the line-of-sight in the 3D space. To address the low resolution issue of the eye image resulting from the use of remote sensors, we rely on the appearance based gaze estimation paradigm, which has demonstrated robustness against this factor. In this context, we do a comparative study of recent appearance based strategies within our framework, study the generalization of these methods to unseen individual, and propose a cross-user eye image alignment technique relying on the direct registration of gaze-synchronized eye images. We demonstrate the validity of our approach through extensive gaze estimation experiments on a public dataset as well as a gaze coding task applied to natural job interviews.
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Except when combined with local binary patterns, although the gain in accuracy was negligible: 0.02\(^{\circ }\).
This is an educated estimation. Location errors for the ball target or the screen dot center is considered as 0, but we needed to add the depth uncertainties or calibration errors. For the eyeball center, we evaluated the error by comparing in a few frames the manual annotation of the eyeball center with the projection of \({\mathbf o}^{{\mathbf {\scriptscriptstyle WCS}}}\).
In a given application, training data would need to be collected appropriately.
For the SVR methods we limited the training set to 1200 samples as using the full set was prohibitively slow.
With the slight difference that the evaluation is conducted on all samples of the test subject’s session, instead of only the second half in the person-specific case.
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Communicated by M. Hebert.
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Funes-Mora, K.A., Odobez, JM. Gaze Estimation in the 3D Space Using RGB-D Sensors. Int J Comput Vis 118, 194–216 (2016). https://doi.org/10.1007/s11263-015-0863-4
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DOI: https://doi.org/10.1007/s11263-015-0863-4