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首页> 外文会议>International Conference on Automatic Face and Gesture Recognition >Improving Children's Gaze Prediction via Separate Facial Areas and Attention Shift Cue
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Improving Children's Gaze Prediction via Separate Facial Areas and Attention Shift Cue

机译：通过单独的面部区域和注意力转变提高儿童凝视预测

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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.
机译：为了预测和评估视觉关注，显着的可视注意建模是一种流行的方法。然而，为成年人开发了最先进的模型，其中没有考虑孩子。此外，这些模型既认为脸部也不是面孔，也不是注重学习线索。脸部是视觉关注的重要组成部分。心理学研究表明，视觉关注的亚面部面积不同。有些型号在社交场景中亮起面部，但不考虑子面部面积。注意学习揭示了视觉关注的内部处理。通过了解认知系统如何处理视觉刺激，可以预测视觉注意力。在本文中，我们提出了一种多级视觉注意模型，以预测观看谈话的脸部儿童的固定。基于传统的显着图，所提出的模型包括单独的面部区域和注意力换档提示。进行了一种眼新的实验以评估模型。结果表明，该模型在谈话脸部场景中显着优于传统模型。

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