The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Les micro-expressions faciales sont des réactions faciales momentanées et subtiles, et il est toujours difficile de reconnaître automatiquement les micro-expressions faciales avec une grande précision dans les applications pratiques. L'extraction de caractéristiques spatio-temporelles à partir de séquences d'images faciales est essentielle pour la reconnaissance des micro-expressions faciales. Dans cet article, nous avons utilisé des réseaux de neurones convolutifs 3D (3D-CNN) pour l'extraction de caractéristiques d'auto-apprentissage afin de représenter efficacement la micro-expression faciale, car les 3D-CNN pourraient très bien extraire les caractéristiques spatio-temporelles des séquences d'images faciales. De plus, l’apprentissage par transfert a été utilisé pour résoudre le problème de l’insuffisance d’échantillons dans la base de données de micro-expressions faciales. Nous avons principalement pré-entraîné les 3D-CNN sur la base de données d'expressions faciales normales Oulu-CASIA par apprentissage supervisé, puis le modèle pré-entraîné a été efficacement transféré au domaine cible, qui était la tâche de reconnaissance des micro-expressions faciales. La méthode proposée a été évaluée sur deux ensembles de données de micro-expression faciale disponibles, à savoir CASME II et SMIC-HS. Nous avons obtenu une précision globale de 97.6 % sur CASME II et de 97.4 % sur SMIC, soit 3.4 % et 1.6 % de plus que le modèle 3D-CNN sans apprentissage par transfert, respectivement. Et les résultats expérimentaux ont démontré que notre méthode atteignait des performances supérieures à celles des méthodes de pointe.
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
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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},}
Copier
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 -