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Dynamic Emotional Faces Generalise Better to a New Expression but not to a New View

机译:动态情感面孔可以更好地推广到新的表情,而不是新的观点

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摘要

Prior research based on static images has found limited improvement for recognising previously learnt faces in a new expression after several different facial expressions of these faces had been shown during the learning session. We investigated whether non-rigid motion of facial expression facilitates the learning process. In Experiment 1, participants remembered faces that were either presented in short video clips or still images. To assess the effect of exposure to expression variation, each face was either learnt through a single expression or three different expressions. Experiment 2 examined whether learning faces in video clips could generalise more effectively to a new view. The results show that faces learnt from video clips generalised effectively to a new expression with exposure to a single expression, whereas faces learnt from stills showed poorer generalisation with exposure to either single or three expressions. However, although superior recognition performance was demonstrated for faces learnt through video clips, dynamic facial expression did not create better transfer of learning to faces tested in a new view. The data thus fail to support the hypothesis that non-rigid motion enhances viewpoint invariance. These findings reveal both benefits and limitations of exposures to moving expressions for expression-invariant face recognition.
机译:基于静态图像的先前研究发现,在学习过程中显示出这些面孔的几种不同面部表情后,在识别这些新面孔中以新表情识别先前学习过的面孔方面的改进有限。我们调查了面部表情的非刚性运动是否有助于学习过程。在实验1中,参与者记得在短视频剪辑或静止图像中呈现的脸部。为了评估暴露于表情变化的影响,可以通过单个表情或三个不同表情来学习每张脸。实验2检验了视频剪辑中的学习面孔是否可以更有效地推广到新视图。结果表明,从视频剪辑中学到的脸部在暴露于单个表情的情况下可以有效地泛化为新的表情,而从静止图像中学到的人脸在暴露于单个或三个表情下的泛化性较差。但是,尽管通过视频剪辑学习到的脸部表现出了出色的识别性能,但是动态脸部表情并不能更好地将学习转移到以新视图测试的脸部。因此,数据无法支持非刚性运动会增加视点不变性的假设。这些发现揭示了暴露于移动表情以实现表情不变的面部识别的好处和局限性。

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