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- Publisher Website: 10.1007/978-3-030-01418-6_9
- Scopus: eid_2-s2.0-85054879043
- WOS: WOS:000463336400009
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Conference Paper: Multi-region ensemble convolutional neural network for facial expression recognition
Title | Multi-region ensemble convolutional neural network for facial expression recognition |
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Authors | |
Keywords | Expression recognition Deep learning Convolutional Neural Network Multi-region ensemble |
Issue Date | 2018 |
Publisher | Springer. The Proceedings' web site is located at https://link.springer.com/conference/icann |
Citation | European Neural Network Society 27th International Conference on Artificial Neural Networks (ICANN2018), Rhodes, Greece, 4-7 October 2018. In Artificial Neural Networks and Machine Learning – ICANN 2018, pt. 1, p. 84-94 How to Cite? |
Abstract | Facial expressions play an important role in conveying the emotional states of human beings. Recently, deep learning approaches have been applied to image recognition field due to the discriminative power of Convolutional Neural Network (CNN). In this paper, we first propose a novel Multi-Region Ensemble CNN (MRE-CNN) framework for facial expression recognition, which aims to enhance the learning power of CNN models by capturing both the global and the local features from multiple human face sub-regions. Second, the weighted prediction scores from each sub-network are aggregated to produce the final prediction of high accuracy. Third, we investigate the effects of different sub-regions of the whole face on facial expression recognition. Our proposed method is evaluated based on two well-known publicly available facial expression databases: AFEW 7.0 and RAF-DB, and has been shown to achieve the state-of-the-art recognition accuracy. |
Persistent Identifier | http://hdl.handle.net/10722/263545 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
ISI Accession Number ID | |
Series/Report no. | Lecture Notes in Computer Science ; v. 11139 |
DC Field | Value | Language |
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dc.contributor.author | Fan, Y | - |
dc.contributor.author | Lam, JCK | - |
dc.contributor.author | Li, VOK | - |
dc.date.accessioned | 2018-10-22T07:40:40Z | - |
dc.date.available | 2018-10-22T07:40:40Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | European Neural Network Society 27th International Conference on Artificial Neural Networks (ICANN2018), Rhodes, Greece, 4-7 October 2018. In Artificial Neural Networks and Machine Learning – ICANN 2018, pt. 1, p. 84-94 | - |
dc.identifier.isbn | 9783030014179 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/263545 | - |
dc.description.abstract | Facial expressions play an important role in conveying the emotional states of human beings. Recently, deep learning approaches have been applied to image recognition field due to the discriminative power of Convolutional Neural Network (CNN). In this paper, we first propose a novel Multi-Region Ensemble CNN (MRE-CNN) framework for facial expression recognition, which aims to enhance the learning power of CNN models by capturing both the global and the local features from multiple human face sub-regions. Second, the weighted prediction scores from each sub-network are aggregated to produce the final prediction of high accuracy. Third, we investigate the effects of different sub-regions of the whole face on facial expression recognition. Our proposed method is evaluated based on two well-known publicly available facial expression databases: AFEW 7.0 and RAF-DB, and has been shown to achieve the state-of-the-art recognition accuracy. | - |
dc.language | eng | - |
dc.publisher | Springer. The Proceedings' web site is located at https://link.springer.com/conference/icann | - |
dc.relation.ispartof | International Conference on Artificial Neural Networks (ICANN2018): Artificial Neural Networks and Machine Learning | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; v. 11139 | - |
dc.subject | Expression recognition | - |
dc.subject | Deep learning | - |
dc.subject | Convolutional Neural Network | - |
dc.subject | Multi-region ensemble | - |
dc.title | Multi-region ensemble convolutional neural network for facial expression recognition | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Fan, Y: yrfan@HKUCC-COM.hku.hk | - |
dc.identifier.email | Lam, JCK: h9992013@hkucc.hku.hk | - |
dc.identifier.email | Li, VOK: vli@eee.hku.hk | - |
dc.identifier.authority | Lam, JCK=rp00864 | - |
dc.identifier.authority | Li, VOK=rp00150 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-030-01418-6_9 | - |
dc.identifier.scopus | eid_2-s2.0-85054879043 | - |
dc.identifier.hkuros | 294326 | - |
dc.identifier.hkuros | 306540 | - |
dc.identifier.volume | 1 | - |
dc.identifier.spage | 84 | - |
dc.identifier.epage | 94 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.identifier.isi | WOS:000463336400009 | - |
dc.publisher.place | Cham | - |
dc.identifier.issnl | 0302-9743 | - |