Cross-Domain Facial Expression Recognition Based on Transductive Deep Transfer Learning
In this paper, we proposed a novel end-to-end transductive deep transfer learning network (TDTLN) to deal with the challenging cross-domain expression recognition problem, in which both the source and target databases are utilized to jointly learn optimal nonlinear discriminative features so as to i...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8786815/ |
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| _version_ | 1849725239781490688 |
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| author | Keyu Yan Wenming Zheng Tong Zhang Yuan Zong Chuangao Tang Cheng Lu Zhen Cui |
| author_facet | Keyu Yan Wenming Zheng Tong Zhang Yuan Zong Chuangao Tang Cheng Lu Zhen Cui |
| author_sort | Keyu Yan |
| collection | DOAJ |
| description | In this paper, we proposed a novel end-to-end transductive deep transfer learning network (TDTLN) to deal with the challenging cross-domain expression recognition problem, in which both the source and target databases are utilized to jointly learn optimal nonlinear discriminative features so as to improve the label prediction performance of the target data samples. As part of the network parameters, the labels of the target samples are also optimized when optimizing the parameters of TDTLN, such that the cross-entropy loss of source domain data and the regression loss of target domain data can be simultaneously calculated. Finally, to evaluate the recognition performance of the proposed TDTLN method, we conduct extensive cross-database experiments on four commonly used multi-view facial expression databases, namely the BU-3DEF, Multi-PIE, SFEW, and RAF database. The experimental results show that the proposed TDTLN method outperforms state-of-the-art methods. |
| format | Article |
| id | doaj-art-fd4f79b252a84dffae3d9499c0098889 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fd4f79b252a84dffae3d9499c00988892025-08-20T03:10:31ZengIEEEIEEE Access2169-35362019-01-01710890610891510.1109/ACCESS.2019.29303598786815Cross-Domain Facial Expression Recognition Based on Transductive Deep Transfer LearningKeyu Yan0https://orcid.org/0000-0001-6838-7203Wenming Zheng1https://orcid.org/0000-0002-7764-5179Tong Zhang2Yuan Zong3Chuangao Tang4Cheng Lu5Zhen Cui6Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaKey Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, ChinaKey Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaKey Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaKey Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, ChinaIn this paper, we proposed a novel end-to-end transductive deep transfer learning network (TDTLN) to deal with the challenging cross-domain expression recognition problem, in which both the source and target databases are utilized to jointly learn optimal nonlinear discriminative features so as to improve the label prediction performance of the target data samples. As part of the network parameters, the labels of the target samples are also optimized when optimizing the parameters of TDTLN, such that the cross-entropy loss of source domain data and the regression loss of target domain data can be simultaneously calculated. Finally, to evaluate the recognition performance of the proposed TDTLN method, we conduct extensive cross-database experiments on four commonly used multi-view facial expression databases, namely the BU-3DEF, Multi-PIE, SFEW, and RAF database. The experimental results show that the proposed TDTLN method outperforms state-of-the-art methods.https://ieeexplore.ieee.org/document/8786815/Cross-domain facial expression recognitiontransductive transfer learningVGGFace16-Net |
| spellingShingle | Keyu Yan Wenming Zheng Tong Zhang Yuan Zong Chuangao Tang Cheng Lu Zhen Cui Cross-Domain Facial Expression Recognition Based on Transductive Deep Transfer Learning IEEE Access Cross-domain facial expression recognition transductive transfer learning VGGFace16-Net |
| title | Cross-Domain Facial Expression Recognition Based on Transductive Deep Transfer Learning |
| title_full | Cross-Domain Facial Expression Recognition Based on Transductive Deep Transfer Learning |
| title_fullStr | Cross-Domain Facial Expression Recognition Based on Transductive Deep Transfer Learning |
| title_full_unstemmed | Cross-Domain Facial Expression Recognition Based on Transductive Deep Transfer Learning |
| title_short | Cross-Domain Facial Expression Recognition Based on Transductive Deep Transfer Learning |
| title_sort | cross domain facial expression recognition based on transductive deep transfer learning |
| topic | Cross-domain facial expression recognition transductive transfer learning VGGFace16-Net |
| url | https://ieeexplore.ieee.org/document/8786815/ |
| work_keys_str_mv | AT keyuyan crossdomainfacialexpressionrecognitionbasedontransductivedeeptransferlearning AT wenmingzheng crossdomainfacialexpressionrecognitionbasedontransductivedeeptransferlearning AT tongzhang crossdomainfacialexpressionrecognitionbasedontransductivedeeptransferlearning AT yuanzong crossdomainfacialexpressionrecognitionbasedontransductivedeeptransferlearning AT chuangaotang crossdomainfacialexpressionrecognitionbasedontransductivedeeptransferlearning AT chenglu crossdomainfacialexpressionrecognitionbasedontransductivedeeptransferlearning AT zhencui crossdomainfacialexpressionrecognitionbasedontransductivedeeptransferlearning |