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The resolution of facial expressions of emotion

机译:情绪面部表情的解析

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摘要

Much is known on how facial expressions of emotion are produced, including which individual muscles are most active in each expression. Yet, little is known on how this information is interpreted by the human visual system. This paper presents a systematic study of the image dimensionality of facial expressions of emotion. In particular, we investigate how recognition degrades when the resolution of the image (i.e., number of pixels when seen as a 5.3 by 8 degree stimulus) is reduced. We show that recognition is only impaired in practice when the image resolution goes below 20 × 30 pixels. A study of the confusion tables demonstrates that each expression of emotion is consistently confused by a small set of alternatives and that the confusion is not symmetric, i.e., misclassifying emotion a as b does not imply we will mistake b for a. This asymmetric pattern is consistent over the different image resolutions and cannot be explained by the similarity of muscle activation. Furthermore, although women are generally better at recognizing expressions of emotion at all resolutions, the asymmetry patterns are the same. We discuss the implications of these results for current models of face perception.
机译:关于如何产生面部表情的情绪,包括在每种表情中哪些肌肉最活跃,是众所周知的。然而,关于人类视觉系统如何解释此信息知之甚少。本文对情绪面部表情的图像维数进行了系统的研究。特别是,我们研究了当图像的分辨率(即5.3 x 8度刺激下的像素数)降低时,识别效果如何降低。我们表明,只有在图像分辨率低于20×30像素时,识别功能才会在实践中受到损害。对混淆表的研究表明,情绪的每种表达总是被一小组替代方案所混淆,并且混淆不是对称的,即将情绪a误分类为b并不意味着我们会将b误认为a。这种不对称模式在不同的图像分辨率上是一致的,无法用肌肉激活的相似性来解释。此外,尽管女性通常在所有分辨率下都更擅长识别情感表达,但不对称模式是相同的。我们讨论了这些结果对当前面部感知模型的影响。

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