Computational Analysis of Smile Weight Distribution across the Face for Accurate Distinction between Genuine and Posed Smiles

Ahmad Al-dahoud, H. Ugail
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Abstract

In this paper, we report the results of our recent research into the understanding of the exact distribution of a smile across the face, especially the distinction in the weight distribution of a smile between a genuine and a posed smile. To do this, we have developed a computational framework for the analysis of the dynamic motion of various parts of the face during a facial expression, in particular, for the smile expression. The heart of our dynamic smile analysis framework is the use of optical flow intensity variation across the face during a smile. This can be utilised to efficiently map the dynamic motion of individual regions of the face such as the mouth, cheeks and areas around the eyes. Thus, through our computational framework, we infer the exact distribution of weights of the smile across the face. Further, through the utilisation of two publicly available datasets, namely the CK+ dataset with 83 subjects expressing posed smiles and the MUG dataset with 35 subjects expressing genuine smiles, we show there is a far greater activity or weight distribution around the regions of the eyes in the case of a genuine smile.
面部微笑权重分布的计算分析,用于准确区分真实微笑和做作微笑
在本文中,我们报告了我们最近对微笑在面部的确切分布的理解的研究结果,特别是微笑在真实微笑和做作微笑之间的重量分布的区别。为了做到这一点,我们开发了一个计算框架,用于分析面部表情期间面部各个部分的动态运动,特别是微笑表情。我们的动态微笑分析框架的核心是在微笑期间使用光流强度变化。这可以用来有效地绘制面部各个区域的动态运动,如嘴巴、脸颊和眼睛周围的区域。因此,通过我们的计算框架,我们推断出微笑在面部的确切权重分布。此外,通过利用两个公开可用的数据集,即CK+数据集,其中有83名受试者表达了假笑,而MUG数据集,其中有35名受试者表达了真实的微笑,我们发现在真实微笑的情况下,眼睛周围区域的活动或重量分布要大得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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