Combining 3D Convolutional Neural Networks with Transfer Learning by Supervised Pre-Training for Facial Micro-Expression Recognition

Ruicong ZHI; Hairui XU; Ming WAN; Tingting LI

doi:10.1587/transinf.2018EDP7153

IEICE TRANSACTIONS on Information

Combining 3D Convolutional Neural Networks with Transfer Learning by Supervised Pre-Training for Facial Micro-Expression Recognition

Ruicong ZHI, Hairui XU, Ming WAN, Tingting LI

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Facial micro-expression is momentary and subtle facial reactions, and it is still challenging to automatically recognize facial micro-expression with high accuracy in practical applications. Extracting spatiotemporal features from facial image sequences is essential for facial micro-expression recognition. In this paper, we employed 3D Convolutional Neural Networks (3D-CNNs) for self-learning feature extraction to represent facial micro-expression effectively, since the 3D-CNNs could well extract the spatiotemporal features from facial image sequences. Moreover, transfer learning was utilized to deal with the problem of insufficient samples in the facial micro-expression database. We primarily pre-trained the 3D-CNNs on normal facial expression database Oulu-CASIA by supervised learning, then the pre-trained model was effectively transferred to the target domain, which was the facial micro-expression recognition task. The proposed method was evaluated on two available facial micro-expression datasets, i.e. CASME II and SMIC-HS. We obtained the overall accuracy of 97.6% on CASME II, and 97.4% on SMIC, which were 3.4% and 1.6% higher than the 3D-CNNs model without transfer learning, respectively. And the experimental results demonstrated that our method achieved superior performance compared to state-of-the-art methods.

Publication: IEICE TRANSACTIONS on Information Vol.E102-D No.5 pp.1054-1064

Publication Date: 2019/05/01

Publicized: 2019/01/29

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2018EDP7153

Type of Manuscript: PAPER

Category: Pattern Recognition

Authors

Ruicong ZHI
  University of Science and Technology Beijing,Beijing Key Laboratory of Knowledge Engineering for Materials Science
Hairui XU
  University of Science and Technology Beijing,Beijing Key Laboratory of Knowledge Engineering for Materials Science
Ming WAN
  University of Science and Technology Beijing,Beijing Key Laboratory of Knowledge Engineering for Materials Science
Tingting LI
  University of Science and Technology Beijing,Beijing Key Laboratory of Knowledge Engineering for Materials Science

Keyword

facial micro-expression, 3D convolutional neural networks, transfer learning, spatiotemporal features

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Ruicong ZHI, Hairui XU, Ming WAN, Tingting LI, "Combining 3D Convolutional Neural Networks with Transfer Learning by Supervised Pre-Training for Facial Micro-Expression Recognition" in IEICE TRANSACTIONS on Information, vol. E102-D, no. 5, pp. 1054-1064, May 2019, doi: 10.1587/transinf.2018EDP7153.
Abstract: Facial micro-expression is momentary and subtle facial reactions, and it is still challenging to automatically recognize facial micro-expression with high accuracy in practical applications. Extracting spatiotemporal features from facial image sequences is essential for facial micro-expression recognition. In this paper, we employed 3D Convolutional Neural Networks (3D-CNNs) for self-learning feature extraction to represent facial micro-expression effectively, since the 3D-CNNs could well extract the spatiotemporal features from facial image sequences. Moreover, transfer learning was utilized to deal with the problem of insufficient samples in the facial micro-expression database. We primarily pre-trained the 3D-CNNs on normal facial expression database Oulu-CASIA by supervised learning, then the pre-trained model was effectively transferred to the target domain, which was the facial micro-expression recognition task. The proposed method was evaluated on two available facial micro-expression datasets, i.e. CASME II and SMIC-HS. We obtained the overall accuracy of 97.6% on CASME II, and 97.4% on SMIC, which were 3.4% and 1.6% higher than the 3D-CNNs model without transfer learning, respectively. And the experimental results demonstrated that our method achieved superior performance compared to state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7153/_p

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@ARTICLE{e102-d_5_1054,
author={Ruicong ZHI, Hairui XU, Ming WAN, Tingting LI, },
journal={IEICE TRANSACTIONS on Information},
title={Combining 3D Convolutional Neural Networks with Transfer Learning by Supervised Pre-Training for Facial Micro-Expression Recognition},
year={2019},
volume={E102-D},
number={5},
pages={1054-1064},
abstract={Facial micro-expression is momentary and subtle facial reactions, and it is still challenging to automatically recognize facial micro-expression with high accuracy in practical applications. Extracting spatiotemporal features from facial image sequences is essential for facial micro-expression recognition. In this paper, we employed 3D Convolutional Neural Networks (3D-CNNs) for self-learning feature extraction to represent facial micro-expression effectively, since the 3D-CNNs could well extract the spatiotemporal features from facial image sequences. Moreover, transfer learning was utilized to deal with the problem of insufficient samples in the facial micro-expression database. We primarily pre-trained the 3D-CNNs on normal facial expression database Oulu-CASIA by supervised learning, then the pre-trained model was effectively transferred to the target domain, which was the facial micro-expression recognition task. The proposed method was evaluated on two available facial micro-expression datasets, i.e. CASME II and SMIC-HS. We obtained the overall accuracy of 97.6% on CASME II, and 97.4% on SMIC, which were 3.4% and 1.6% higher than the 3D-CNNs model without transfer learning, respectively. And the experimental results demonstrated that our method achieved superior performance compared to state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.2018EDP7153},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - Combining 3D Convolutional Neural Networks with Transfer Learning by Supervised Pre-Training for Facial Micro-Expression Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1054
EP - 1064
AU - Ruicong ZHI
AU - Hairui XU
AU - Ming WAN
AU - Tingting LI
PY - 2019
DO - 10.1587/transinf.2018EDP7153
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2019
AB - Facial micro-expression is momentary and subtle facial reactions, and it is still challenging to automatically recognize facial micro-expression with high accuracy in practical applications. Extracting spatiotemporal features from facial image sequences is essential for facial micro-expression recognition. In this paper, we employed 3D Convolutional Neural Networks (3D-CNNs) for self-learning feature extraction to represent facial micro-expression effectively, since the 3D-CNNs could well extract the spatiotemporal features from facial image sequences. Moreover, transfer learning was utilized to deal with the problem of insufficient samples in the facial micro-expression database. We primarily pre-trained the 3D-CNNs on normal facial expression database Oulu-CASIA by supervised learning, then the pre-trained model was effectively transferred to the target domain, which was the facial micro-expression recognition task. The proposed method was evaluated on two available facial micro-expression datasets, i.e. CASME II and SMIC-HS. We obtained the overall accuracy of 97.6% on CASME II, and 97.4% on SMIC, which were 3.4% and 1.6% higher than the 3D-CNNs model without transfer learning, respectively. And the experimental results demonstrated that our method achieved superior performance compared to state-of-the-art methods.
ER -

IEICE TRANSACTIONS on Information