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Analyses of the Differences between Posed and Spontaneous Facial Expressions

机译:构成和自发面部表情的差异分析

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This paper presents comprehensive analyses of the differences between posed and spontaneous expressions from visible images. First, geometric and appearance features are extracted from the difference images between apex and onset facial images. Secondly, the differences between the posed and spontaneous facial expressions are analyzed through hypothetical testing methods from three aspects: on overall samples, on samples with different genders, and on samples with different expressions. Thirdly, Bayesian networks (BNs) are used to classify posed versus spontaneous expressions from the same three aspects. Statistical analyses on the NVIE database demonstrate the importance of the geometric and appearance features for discriminating posed and spontaneous expressions. Gender effect exists on the differences between posed and spontaneous expressions. It is easier to distinguish posed happiness from spontaneous happiness than other expressions. Recognition experimental results confirm the observations of statistical analyses in most cases.
机译:本文旨在综合分析可见图像的构成和自发表达的差异。首先,从顶点和开始面部图像之间的差异图像中提取几何和外观特征。其次,通过假设的测试方法从三个方面的假设检测方法分析了构成和自发性面部表情之间的差异:在整个样本上,在具有不同性别的样品上,以及不同表达的样品。第三,贝叶斯网络(BNS)用于分类与同一三个方面的构成与自发表达式。 NVIE数据库上的统计分析表明了几何和外观特征的重要性,以辨别构成和自发表达。存在于构成和自发表达的差异存在性别效果。从自发的幸福区分,比其他表情更容易。识别实验结果证实了大多数情况下的统计分析的观察。

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