Frontalization and adaptive exponential ensemble rule for deep-learning-based facial expression recognition system
Automatic facial expression recognition (FER) is an important technique in human–computer interfaces and surveillance systems. It classifies the input facial image into one of the basic expressions (anger, sadness, surprise, happiness, disgust, fear, and neutral). There are two types of FER algorith...
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| Vydané v: | Signal processing. Image communication Ročník 96; s. 116321 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Amsterdam
Elsevier B.V
01.08.2021
Elsevier BV |
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| ISSN: | 0923-5965, 1879-2677 |
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| Abstract | Automatic facial expression recognition (FER) is an important technique in human–computer interfaces and surveillance systems. It classifies the input facial image into one of the basic expressions (anger, sadness, surprise, happiness, disgust, fear, and neutral). There are two types of FER algorithms: feature-based and convolutional neural network (CNN)-based algorithms. The CNN is a powerful classifier, however, without proper auxiliary techniques, its performance may be limited. In this study, we improve the CNN-based FER system by utilizing face frontalization and the hierarchical architecture. The frontalization algorithm aligns the face by in-plane or out-of-plane, rotation, landmark point matching, and removing background noise. The proposed adaptive exponentially weighted average ensemble rule can determine the optimal weight according to the accuracy of classifiers to improve robustness. Experiments on several popular databases are performed and the results show that the proposed system has a very high accuracy and outperforms state-of-the-art FER systems.
[Display omitted]
•An advanced CNN based facial expression recognition (FER) method is proposed.•Its accuracy is higher than that of other stated-of-the-art CNN-based methods.•Advanced frontalization method is used to make the input of the CNN more meaningful.•A hierarchical AEWEA system is applied to integrate the advantages of each model.•The shortcut CNN, which considers block relations and is easier to train, is adopted. |
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| AbstractList | Automatic facial expression recognition (FER) is an important technique in human–computer interfaces and surveillance systems. It classifies the input facial image into one of the basic expressions (anger, sadness, surprise, happiness, disgust, fear, and neutral). There are two types of FER algorithms: feature-based and convolutional neural network (CNN)-based algorithms. The CNN is a powerful classifier, however, without proper auxiliary techniques, its performance may be limited. In this study, we improve the CNN-based FER system by utilizing face frontalization and the hierarchical architecture. The frontalization algorithm aligns the face by in-plane or out-of-plane, rotation, landmark point matching, and removing background noise. The proposed adaptive exponentially weighted average ensemble rule can determine the optimal weight according to the accuracy of classifiers to improve robustness. Experiments on several popular databases are performed and the results show that the proposed system has a very high accuracy and outperforms state-of-the-art FER systems. Automatic facial expression recognition (FER) is an important technique in human–computer interfaces and surveillance systems. It classifies the input facial image into one of the basic expressions (anger, sadness, surprise, happiness, disgust, fear, and neutral). There are two types of FER algorithms: feature-based and convolutional neural network (CNN)-based algorithms. The CNN is a powerful classifier, however, without proper auxiliary techniques, its performance may be limited. In this study, we improve the CNN-based FER system by utilizing face frontalization and the hierarchical architecture. The frontalization algorithm aligns the face by in-plane or out-of-plane, rotation, landmark point matching, and removing background noise. The proposed adaptive exponentially weighted average ensemble rule can determine the optimal weight according to the accuracy of classifiers to improve robustness. Experiments on several popular databases are performed and the results show that the proposed system has a very high accuracy and outperforms state-of-the-art FER systems. [Display omitted] •An advanced CNN based facial expression recognition (FER) method is proposed.•Its accuracy is higher than that of other stated-of-the-art CNN-based methods.•Advanced frontalization method is used to make the input of the CNN more meaningful.•A hierarchical AEWEA system is applied to integrate the advantages of each model.•The shortcut CNN, which considers block relations and is easier to train, is adopted. |
| ArticleNumber | 116321 |
| Author | Ding, Jian-Jiun Tsai, Yi-Wei Chang, Ronald Y. Tsai, Kai-Yuan Lee, Yih-Cherng |
| Author_xml | – sequence: 1 givenname: Kai-Yuan surname: Tsai fullname: Tsai, Kai-Yuan email: r05942109@ntu.edu.tw organization: Graduate Inst. Communication Engineering, National Taiwan University, Taipei, Taiwan – sequence: 2 givenname: Yi-Wei surname: Tsai fullname: Tsai, Yi-Wei email: r08942044@ntu.edu.tw organization: Graduate Inst. Communication Engineering, National Taiwan University, Taipei, Taiwan – sequence: 3 givenname: Yih-Cherng orcidid: 0000-0002-5905-1967 surname: Lee fullname: Lee, Yih-Cherng email: d04942009@ntu.edu.tw organization: Graduate Inst. Communication Engineering, National Taiwan University, Taipei, Taiwan – sequence: 4 givenname: Jian-Jiun orcidid: 0000-0003-4510-2273 surname: Ding fullname: Ding, Jian-Jiun email: jjding@ntu.edu.tw organization: Graduate Inst. Communication Engineering, National Taiwan University, Taipei, Taiwan – sequence: 5 givenname: Ronald Y. surname: Chang fullname: Chang, Ronald Y. email: rchang@citi.sinica.edu.tw organization: Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan |
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| Keywords | Computer vision Face frontalization Face recognition Convolutional neural network Facial expression Hierarchical structure |
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