CLC number: TP37
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-06-03
Cited: 0
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Ping-ping Wu, Hong Liu, Xue-wu Zhang, Yuan Gao. Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(7): 955-967.
@article{title="Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics",
author="Ping-ping Wu, Hong Liu, Xue-wu Zhang, Yuan Gao",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="7",
pages="955-967",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1600041"
}
%0 Journal Article
%T Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics
%A Ping-ping Wu
%A Hong Liu
%A Xue-wu Zhang
%A Yuan Gao
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 7
%P 955-967
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1600041
TY - JOUR
T1 - Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics
A1 - Ping-ping Wu
A1 - Hong Liu
A1 - Xue-wu Zhang
A1 - Yuan Gao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 7
SP - 955
EP - 967
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1600041
Abstract: As a typical biometric cue with great diversities, smile is a fairly influential signal in social interaction, which reveals the emotional feeling and inner state of a person. Spontaneous and posed smiles initiated by different brain systems have differences in both morphology and dynamics. Distinguishing the two types of smiles remains challenging as discriminative subtle changes need to be captured, which are also uneasily observed by human eyes. Most previous related works about spontaneous versus posed smile recognition concentrate on extracting geometric features while appearance features are not fully used, leading to the loss of texture information. In this paper, we propose a region-specific texture descriptor to represent local pattern changes of different facial regions and compensate for limitations of geometric features. The temporal phase of each facial region is divided by calculating the intensity of the corresponding facial region rather than the intensity of only the mouth region. A mid-level fusion strategy of support vector machine is employed to combine the two feature types. Experimental results show that both our proposed appearance representation and its combination with geometry-based facial dynamics achieve favorable performances on four baseline databases: BBC, SPOS, MMI, and UvA-NEMO.
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