W. Peng, X. Hong, Y. Xu and G. Zhao, "A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework," 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France, 2019, pp. 1-5. doi: 10.1109/FG.2019.8756541
A boost in revealing subtle facial expressions : a consolidated Eulerian framework
|Author:||Peng, Wei1; Hong, Xiaopeng2,1; Xu, Yingyue1;|
1Center for Machine Vision and Signal Processing, University of Oulu, Finland
2Xian Jiaotong University, Xian, P. R. China
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019120245225
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-12-02
Facial Micro-expression Recognition (MER) distinguishes the underlying emotional states of spontaneous subtle facial expressions. Automatic MER is challenging because that the intensity of subtle facial muscle movement is extremely low and the duration of ME is transient. Recent works adopt motion magnification or temporal interpolation to resolve these issues. Nevertheless, existing works divide them into two separate modules due to their non-linearity. Though such operation eases the difficulty in implementation, it ignores their underlying connections and thus results in inevitable losses in both accuracy and speed. Instead, in this paper, we propose a consolidated Eulerian framework to reveal the subtle facial movements. It expands the temporal duration and amplifies the muscle movements in micro-expressions simultaneously. Compared to existing approaches, the proposed method can not only process ME clips more efficiently but also make subtle ME movements more distinguishable. Experiments on two public MER databases indicate that our model outperforms the state-of-the-art in both speed and accuracy.
14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019, 14-18 May 2019, Lille, France
IEEE International Conference on Automatic Face and Gesture Recognition
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This work was supported by the Academy of Finland, Tekes Fidipro program (Grant No. 1849/31/2015), Business Finland project (Grant No. 3116/31/2017), and Infotech Oulu. It was also supported, in part, by the National Natural Science Foundation of China (No. 61772419 & 61572205). Moreover, the authors wish to acknowledge CSC-IT Center for Science, Finland, for computational resources. Furthermore, we express deep gratitude to the support of NVIDIA Corporation with the donation of GPUs for this research.
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