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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Ekspresi mikro muka ialah tindak balas muka yang seketika dan halus, dan masih mencabar untuk mengenali ekspresi mikro muka secara automatik dengan ketepatan yang tinggi dalam aplikasi praktikal. Mengekstrak ciri spatiotemporal daripada jujukan imej muka adalah penting untuk pengecaman ekspresi mikro muka. Dalam makalah ini, kami menggunakan Rangkaian Neural Konvolusi 3D (3D-CNN) untuk pengekstrakan ciri pembelajaran kendiri untuk mewakili ekspresi mikro muka dengan berkesan, kerana 3D-CNN boleh mengekstrak ciri spatiotemporal daripada jujukan imej muka. Selain itu, pembelajaran pemindahan telah digunakan untuk menangani masalah sampel yang tidak mencukupi dalam pangkalan data ekspresi mikro muka. Kami terutamanya melatih 3D-CNN pada pangkalan data ekspresi muka biasa Oulu-CASIA dengan pembelajaran diselia, kemudian model pra-latihan telah dipindahkan dengan berkesan ke domain sasaran, iaitu tugas pengecaman ekspresi mikro muka. Kaedah yang dicadangkan telah dinilai pada dua set data ekspresi mikro muka yang tersedia, iaitu CASME II dan SMIC-HS. Kami memperoleh ketepatan keseluruhan sebanyak 97.6% pada CASME II, dan 97.4% pada SMIC, masing-masing 3.4% dan 1.6% lebih tinggi daripada model 3D-CNN tanpa pembelajaran pemindahan. Dan keputusan eksperimen menunjukkan bahawa kaedah kami mencapai prestasi unggul berbanding kaedah terkini.
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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Salinan
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
Salinan
@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},}
Salinan
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 -