Methods of Recognizing True and Fake Smiles by Using AU6 and AU12 in a Holistic Way

Wu, Pingping; Wang, Wenmin; Liu, Hong

doi:10.1007/978-3-642-38466-0_67

Cited by 3 publications

(1 citation statement)

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“…On the other hand, a non-Duchenne smile appears to be formulated by the upward movement of mouth corners (AU12). To this end, Wu et al, [15] have proposed a computational system to identify the genuineness of a smile based on the detection of AUs 6 and 12. Based on the 2D principal component analysis combined with Gabor filters and support vector based machine learning they report an 85.9% accuracy in smile classification.…”

Section: Related Workmentioning

confidence: 99%

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

Aldahoud

Ugail

2018

2018 International Conference on Cyberworlds (CW)

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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.

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Section: Related Workmentioning

confidence: 99%