Citation

Yang, John See Su and Wang, Yandan and Li, Jing and Liu, Wenbin (2018) Micro-expression recognition based on 3D flow convolutional neural network. Pattern Analysis and Applications. pp. 13331-13339. ISSN 1433-7541

Text Li2019_Article_Micro-expressionRecognitionBas.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Micro-expression recognition (MER) is a growing field of research which is currently in its early stage of development. Unlike conventional macro-expressions, micro-expressions occur at a very short duration and are elicited in a spontaneous manner from emotional stimuli. While existing methods for solving MER are largely non-deep-learning-based methods, deep convolutional neural network (CNN) has shown to work very well on such as face recognition, facial expression recognition, and action recognition. In this article, we propose applying the 3D flow-based CNNs model for video-based micro-expression recognition, which extracts deeply learned features that are able to characterize fine motion flow arising from minute facial movements. Results from comprehensive experiments on three benchmark datasets—SMIC, CASME/CASME II, showed a marked improvement over state-of-the-art methods, hence proving the effectiveness of our fairly easy CNN model as the deep learning benchmark for facial MER.

Item Type:	Article
Uncontrolled Keywords:	Neural network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	28 Apr 2021 15:05
Last Modified:	28 Apr 2021 15:05
URII:	http://shdl.mmu.edu.my/id/eprint/7662

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