Z. Xia, X. Feng, X. Hong and G. Zhao, "Spontaneous Facial Micro-expression Recognition via Deep Convolutional Network," 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA), Xi'an, 2018, pp. 1-6. doi: 10.1109/IPTA.2018.8608119
Spontaneous facial micro-expression recognition via deep convolutional network
|Author:||Xia, Zhaoqiang1; Feng, Xiaoyi1; Hong, Xiaopeng2;|
1School of Electronics and Information, Northwestern Polytechnical University
2Center for Machine Vision and Signal Analysis, University of Oulu
|Online Access:||PDF Full Text (PDF, 1.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019080523436
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-08-05
The automatic recognition of spontaneous facial micro-expressions becomes prevalent as it reveals the actual emotion of humans. However, handcrafted features employed for recognizing micro-expressions are designed for general applications and thus cannot well capture the subtle facial deformations of micro-expressions. To address this problem, we propose an end-to-end deep learning framework to suit the particular needs of micro-expression recognition (MER). In the deep model, re- current convolutional networks are utilized to learn the representation of subtle changes from image sequences. To guarantee the learning of deep model, we present a temporal jittering procedure to greatly enrich the training samples. Through performing the experiments on three spontaneous micro-expression datasets, i.e., SMIC, CASME, and CASME2, we verify the effectiveness of our proposed MER approach.
Proceedings. International Workshops on Image Processing Theory, Tools, and Applications
|Pages:||235 - 240|
2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)
International Conference on Image Processing Theory Tools and Applications
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
This work is partly supported by the National Nature Science Foundation of China (No.61702419), and the Natural Science Basic Research Plan in Shaanxi Province of China (No.2018JQ6090).
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