Robustness comparison between the capsule network and the convolutional network for facial expression recognition
As an important part of human-computer interactions, facial expression recognition has become a popular research topic in computer vision, pattern recognition, artificial intelligence and other fields. With the development of deep learning and convolutional neural networks, research on facial expres...
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| Published in: | Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 4; pp. 2269 - 2278 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01.04.2021
Springer Nature B.V |
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| ISSN: | 0924-669X, 1573-7497 |
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| Abstract | As an important part of human-computer interactions, facial expression recognition has become a popular research topic in computer vision, pattern recognition, artificial intelligence and other fields. With the development of deep learning and convolutional neural networks, research on facial expression recognition has also made considerable progress. Because facial expressions vary in real environments, such as rotation, shifting, brightness changes, partial occlusion and noise with different intensities, research on the robustness of facial expression recognition is very important. A capsule network consists of capsules, which are groups of neurons, and these capsules can learn posture information through the dynamic routing mechanism. The length of a capsule represents the existence probability, and each neuron in a capsule represents posture information (e.g., position, size, orientation or a combination of these properties). Therefore, in this study, the robustness of the emerging capsule network (CapsNet) is comprehensively compares with that of the traditional convolutional neural network (CNN) and fully convolutional network (FCN) in facial expression recognition tasks. The simulation results based on the Cohn-Kanade (CK+) databases show that the capsule network is more robust than the other networks. Therefore, the capsule network has significant advantages over the other networks in facial expression recognition task in complex real-world environments. |
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| AbstractList | As an important part of human-computer interactions, facial expression recognition has become a popular research topic in computer vision, pattern recognition, artificial intelligence and other fields. With the development of deep learning and convolutional neural networks, research on facial expression recognition has also made considerable progress. Because facial expressions vary in real environments, such as rotation, shifting, brightness changes, partial occlusion and noise with different intensities, research on the robustness of facial expression recognition is very important. A capsule network consists of capsules, which are groups of neurons, and these capsules can learn posture information through the dynamic routing mechanism. The length of a capsule represents the existence probability, and each neuron in a capsule represents posture information (e.g., position, size, orientation or a combination of these properties). Therefore, in this study, the robustness of the emerging capsule network (CapsNet) is comprehensively compares with that of the traditional convolutional neural network (CNN) and fully convolutional network (FCN) in facial expression recognition tasks. The simulation results based on the Cohn-Kanade (CK+) databases show that the capsule network is more robust than the other networks. Therefore, the capsule network has significant advantages over the other networks in facial expression recognition task in complex real-world environments. |
| Author | Yuan, Guangjie Zhao, Xingcong Liu, Ying Li, Donghui Liu, Guangyuan |
| Author_xml | – sequence: 1 givenname: Donghui surname: Li fullname: Li, Donghui organization: College of Electronic and Information Engineering, Southwest University, Institute of Affective Computing and Information Processing, Southwest University, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University – sequence: 2 givenname: Xingcong surname: Zhao fullname: Zhao, Xingcong organization: College of Electronic and Information Engineering, Southwest University, Institute of Affective Computing and Information Processing, Southwest University, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University – sequence: 3 givenname: Guangjie surname: Yuan fullname: Yuan, Guangjie organization: College of Electronic and Information Engineering, Southwest University, Institute of Affective Computing and Information Processing, Southwest University, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University – sequence: 4 givenname: Ying surname: Liu fullname: Liu, Ying organization: School of Mathematics and Statistics, Southwest University – sequence: 5 givenname: Guangyuan surname: Liu fullname: Liu, Guangyuan email: liugy@swu.edu.cn organization: College of Electronic and Information Engineering, Southwest University, Institute of Affective Computing and Information Processing, Southwest University, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University |
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| Cites_doi | 10.1007/s10489-018-1388-7 10.1109/TIP.2012.2235848 10.1007/s11263-017-1055-1 10.1007/s10489-019-01491-8 10.21629/JSEE.2017.04.18 10.1007/s10489-017-1121-y 10.1016/j.patcog.2015.04.025 10.1007/s12559-019-09654-y 10.1016/j.patcog.2011.05.006 10.1109/TIP.2018.2886767 10.1016/j.patrec.2019.12.013 10.1016/j.eswa.2012.07.074 10.1016/j.jpdc.2019.04.017 10.1109/CVPR.2018.00231 10.1109/CVPR.2017.243 10.1109/CVPRW.2010.5543262 10.1109/FG.2015.7163082 10.1109/CVPR.2016.90 10.1109/WACV.2016.7477450 10.1109/ChiCC.2016.7553957 10.1145/3175603.3175615 10.1109/CVPR.2015.7298965 |
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| Keywords | Deep learning Facial expression recognition Robustness Capsule network |
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| Title | Robustness comparison between the capsule network and the convolutional network for facial expression recognition |
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