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2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) (2017)
Washington, DC, DC, USA
May 30, 2017 to June 3, 2017
ISBN: 978-1-5090-4023-0
pp: 742-747
To predict and assess visual attention, saliencybased visual attention modeling is a popular approach. However, state-of-the-art models are developed for adults, in which children are not considered. Additionally, these models consider neither social cues like face, nor attention learning cues. The face is a vital part of visual attention. Psychological studies reveal that sub-facial areas are different in visual attention. Some models highlight faces in social scenes, but sub-facial areas are not taken into account. Attention learning reveals internal processing of visual attention. By learning how the cognitive system deals with visual stimuli, it is possible to predict visual attention behavior. In this paper, we propose a multilevel visual attention model to predict fixations of children when watching a talking face. Based on traditional saliency maps, the proposed model includes both separate facial areas and attention shift cue. An eye-tracking experiment is conducted to evaluate the model. Results show that the proposed model significantly outperforms conventional models in talking face scenes.

S. Li, W. Cui, J. Cui, L. Wang, M. Li and H. Zha, "Improving Children's Gaze Prediction via Separate Facial Areas and Attention Shift Cue," 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)(FG), Washington, DC, DC, USA, 2017, pp. 742-747.
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