Joint Attention by Gaze Interpolation and Saliency

Joint attention, which is the ability of coordination of a common point of reference with the communicating party, emerges as a key factor in various interaction scenarios. This paper presents an image-based method for establishing joint attention between an experimenter and a robot. The precise ana...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2013-06, Vol.43 (3), p.829-842
Main Authors: Yucel, Z., Salah, A. A., Mericli, C., Mericli, T., Valenti, R., Gevers, T.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Attention - physiology
Biomimetics - methods
Communication
Cybernetics
Developmental robotics
Estimation
Face
Fixation, Ocular - physiology
Gaussian
gaze following
head pose estimation
Humans
Illumination
Interpolation
joint visual attention
Joints
Man-Machine Systems
Neural networks
Pattern Recognition, Automated - methods
Regression
Regression analysis
Robot kinematics
Robotics - methods
Robots
saliency
selective attention
Studies
Vectors
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Summary:Joint attention, which is the ability of coordination of a common point of reference with the communicating party, emerges as a key factor in various interaction scenarios. This paper presents an image-based method for establishing joint attention between an experimenter and a robot. The precise analysis of the experimenter's eye region requires stability and high-resolution image acquisition, which is not always available. We investigate regression-based interpolation of the gaze direction from the head pose of the experimenter, which is easier to track. Gaussian process regression and neural networks are contrasted to interpolate the gaze direction. Then, we combine gaze interpolation with image-based saliency to improve the target point estimates and test three different saliency schemes. We demonstrate the proposed method on a human-robot interaction scenario. Cross-subject evaluations, as well as experiments under adverse conditions (such as dimmed or artificial illumination or motion blur), show that our method generalizes well and achieves rapid gaze estimation for establishing joint attention.
ISSN:2168-2267
2168-2275
DOI:10.1109/TSMCB.2012.2216979