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Micro-expression video clip synthesis method based on spatial-temporal statistical model and motion intensity evaluation function

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conference contribution
posted on 17.12.2020, 11:09 by Lei Wang, Jialu Hou, Xingbei Guo, Ziping Ma, Xiyao Liu, Hui FangHui Fang
Micro-expression (ME) recognition is an effective method to detect lies and other subtle human emotions. Machine learning-based and deep learning-based models have achieved remarkable results recently. However, these models are vulnerable to overfitting issue due to the scarcity of ME video clips. These videos are much harder to collect and annotate than normal expression video clips, thus limiting the recognition performance improvement. To address this issue, we propose a microexpression video clip synthesis method based on spatial-temporal statistical and motion intensity evaluation in this paper. In our proposed scheme, we establish a micro-expression spatial and temporal statistical model (MSTSM) by analyzing the dynamic characteristics of micro-expressions and deploy this model to provide the rules for micro-expressions video synthesis. In addition, we design a motion intensity evaluation function (MIEF) to ensure that the intensity of facial expression in the synthesized video clips is consistent with those in real -ME. Finally, facial video clips with MEs of new subjects can be generated by deploying the MIEF together with the widely-used 3D facial morphable model and the rules provided by the MSTSM. The experimental results have demonstrated that the accuracy of micro-expression recognition can be effectively improved by adding the synthesized video clips generated by our proposed method.

Funding

National Natural Science Foundation of China (61602527)

Hunan Provincial Natural Science Foundation of China (2017JJ3416, 2018JJ2548, 2020JJ4746)

History

School

  • Science

Department

  • Computer Science

Published in

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Pages

211-217

Source

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2020-12-14

Copyright date

2020

ISBN

9781728185262

ISSN

2577-1655

Language

en

Location

Toronto, Canada

Event dates

11th October 2020 - 14th October 2020

Depositor

Dr Hui Fang. Deposit date: 16 December 2020