TY - JOUR
T1 - Micro-expression recognition based on 3D flow convolutional neural network
AU - Li, Jing
AU - Wang, Yandan
AU - See, John
AU - Liu, Wenbin
N1 - Funding Information:
We gratefully acknowledge the support of NVIDIA Corporation for the donation of a Quadro K5200 GPU used in this work.
Publisher Copyright:
© 2018, Springer-Verlag London Ltd., part of Springer Nature.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
KW - 3D CNN
KW - CASME
KW - Facial micro-expressions
KW - Micro-expression recognition
KW - Optical flow
KW - SMIC
UR - http://www.scopus.com/inward/record.url?scp=85056305389&partnerID=8YFLogxK
U2 - 10.1007/s10044-018-0757-5
DO - 10.1007/s10044-018-0757-5
M3 - Article
AN - SCOPUS:85056305389
SN - 1433-7541
VL - 22
SP - 1331
EP - 1339
JO - Pattern Analysis and Applications
JF - Pattern Analysis and Applications
IS - 4
ER -