University of Oulu

Y. Li, X. Huang and G. Zhao, "Can Micro-Expression be Recognized Based on Single Apex Frame?," 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, 2018, pp. 3094-3098. doi: 10.1109/ICIP.2018.8451376

Can micro-expression be recognized based on single apex frame?

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Author: Li, Yante1; Huang, Xiaohua1; Zhao, Guoying1
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2019-02-22


Micro-expressions are rapid and subtle facial movements such that they are difficult to detect and recognize. Most of recent works have attempted to recognize micro-expression by using the spatial and dynamic information from the video clip. Physiological studies have demonstrated that the apex frame can convey the most emotion expressed in facial expression. It may be reasonable to use apex frame for improving micro-expression recognition. However, it is wonder how much apex frames contribute to micro-expression recognition. In this paper, we primarily focus on resolving the contribution-level by using apex frame for micro-expression recognition. Firstly, we propose a new method to detect the apex frame in frequency domain, as it is found that apex frame has very correlated relationship with the amplitude change in frequency domain. Secondly, we propose to use deep convolutional neural network (DCNN) on apex frame to recognize micro-expression. Intensive experimental results on CASME II database shows that our method has achieved considerably improvement compared with the state-of-the-art methods in micro-expression recognition. These results also demonstrate that apex frame can express the major emotion in micro-expression.

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Series: IEEE International Conference on Image Processing
ISSN: 1522-4880
ISSN-E: 2381-8549
ISSN-L: 1522-4880
ISBN: 978-1-4799-7061-2
ISBN Print: 978-1-4799-7062-9
Pages: 3094 - 3098
DOI: 10.1109/ICIP.2018.8451376
Host publication: 2018 25th IEEE International Conference on Image Processing (ICIP)
Conference: IEEE International Conference on Image Processing
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work is supported by Academy of Finland, Tekes Fidipro program (Grant No.1849/31/2015), Business Finland project (Grant No.3116/31/2017), Infotech Oulu and the National Natural Science Foundation of China (Grants No.61772419), Jorma Ollila Grant of Nokia Foundation, Central Fund of Finnish Cultural Foundation, Apurahoja tekoalytutkimukseen of Kaute Foundation, NVIDIA GPU Grant Program. The authors wish to acknowledge CSC - IT Center for Science, Finland, for computational resources. Thanks for the help of Henglin Shi.
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