A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives
A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives
复制标题
DOI：
10.5555/2503308.2343694
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Aleix M. Martinez;Shichuan Du
中科院分区：
文献类型：
--
作者：
Aleix M. Martinez;Shichuan Du
在认知科学和神经科学中，描述人类如何感知和分类面部表情的主要模型有两种：连续模型和分类模型。连续模型将每个面部情感表情定义为面部空间中的特征向量。例如，这个模型解释了如何在不同的强度下看到情绪的表达。相比之下，分类模型由C分类器组成，每个分类器都调整到特定的情感类别。这个模型解释了为什么在快乐和惊讶的脸之间的变形序列中的图像被认为是快乐或惊讶，而不是介于两者之间的东西。虽然连续模型更难证明后一个发现，但分类模型在解释如何在不同强度或模式下识别表情时并不那么好。最重要的是，这两种模型都无法解释人们如何识别情绪类别的组合，例如高兴地惊讶、愤怒地惊讶和惊讶。为了解决这些问题，在过去的几年里，我们一直在研究一个修正的模型，该模型证明了认知科学和神经科学文献中报道的结果。这个模型由C个不同的连续空间组成。多个（复合）情感类别可以通过线性组合这些C面空间来识别。这些空间的尺寸被示出为主要是拱形的。根据该模型，面部表情的情感分类的主要任务是精确的，详细的面部标志检测，而不是识别。我们提供了证明该模型的文献的概述，展示了如何使用所得到的模型来构建用于识别面部表情的算法，并提出了机器学习和计算机视觉研究人员的研究方向，以继续推动这些领域的最新技术。我们还讨论了该模型如何有助于人类感知，社会互动和疾病的研究。
In cognitive science and neuroscience, there have been two leading models describing how humans perceive and classify facial expressions of emotion-the continuous and the categorical model. The continuous model defines each facial expression of emotion as a feature vector in a face space. This model explains, for example, how expressions of emotion can be seen at different intensities. In contrast, the categorical model consists of C classifiers, each tuned to a specific emotion category. This model explains, among other findings, why the images in a morphing sequence between a happy and a surprise face are perceived as either happy or surprise but not something in between. While the continuous model has a more difficult time justifying this latter finding, the categorical model is not as good when it comes to explaining how expressions are recognized at different intensities or modes. Most importantly, both models have problems explaining how one can recognize combinations of emotion categories such as happily surprised versus angrily surprised versus surprise. To resolve these issues, in the past several years, we have worked on a revised model that justifies the results reported in the cognitive science and neuroscience literature. This model consists of C distinct continuous spaces. Multiple (compound) emotion categories can be recognized by linearly combining these C face spaces. The dimensions of these spaces are shown to be mostly configural. According to this model, the major task for the classification of facial expressions of emotion is precise, detailed detection of facial landmarks rather than recognition. We provide an overview of the literature justifying the model, show how the resulting model can be employed to build algorithms for the recognition of facial expression of emotion, and propose research directions in machine learning and computer vision researchers to keep pushing the state of the art in these areas. We also discuss how the model can aid in studies of human perception, social interactions and disorders.