Deciphering the Face.
Deciphering the Face.
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DOI：
10.1109/cvprw.2011.5981690
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Martinez AM
中科院分区：
文献类型：
--
作者：
Martinez AM
我们认为，为了制作出色的计算机视觉算法以进行面部分析和识别，这些算法应基于配置和形状特征。在此模型中，计算机视觉研究人员要解决的最重要的任务是准确检测面部特征，而不是识别。我们基于认知科学和神经科学的最新结果。特别是，我们表明，情感的不同面部表情在人类的行为/认知中具有多种用途，并且面部表达可能与多种情感类别有关。这两个结果与认知科学的连续模型矛盾，神经科学的边缘假设以及计算机视觉中通常采用的多维方法。因此，我们提出了一种替代的混合连续分类方法来感知面部表情，并表明配置和形状特征对于人类对情感构建的识别最为重要。我们说明如何通过计算机视觉算法成功利用这些图像提示。在整个论文中，我们讨论了这些结果在面部识别和人类计算机相互作用中的应用中的含义。
We argue that to make robust computer vision algorithms for face analysis and recognition, these should be based on configural and shape features. In this model, the most important task to be solved by computer vision researchers is that of accurate detection of facial features, rather than recognition. We base our arguments on recent results in cognitive science and neuroscience. In particular, we show that different facial expressions of emotion have diverse uses in human behavior/cognition and that a facial expression may be associated to multiple emotional categories. These two results are in contradiction with the continuous models in cognitive science, the limbic assumption in neuroscience and the multidimensional approaches typically employed in computer vision. Thus, we propose an alternative hybrid continuous-categorical approach to the perception of facial expressions and show that configural and shape features are most important for the recognition of emotional constructs by humans. We illustrate how these image cues can be successfully exploited by computer vision algorithms. Throughout the paper, we discuss the implications of these results in applications in face recognition and human-computer interaction.