Robust facial expression recognition with global-local joint representation learning

Abstract As an important part in computer vision, facial expression recognition (FER) has received extensive attention, but it still has lots of challenges in this area. One of the important difficulties is to remain the topological information in the feature extraction operation. In this paper, we...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Fan, Chunxiao [verfasserIn] Wang, Zhenxing Li, Jia Wang, Shanshan Sun, Xiao

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Facial expression recognition Dual channel neural network Graph convolutional network

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Multimedia systems - Berlin : Springer, 1993, 29(2022), 5 vom: 07. März, Seite 3069-3079
Übergeordnetes Werk:	volume:29 ; year:2022 ; number:5 ; day:07 ; month:03 ; pages:3069-3079

Links:	Volltext

DOI / URN:	10.1007/s00530-022-00907-9

Katalog-ID:	SPR053084519

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520			\|a Abstract As an important part in computer vision, facial expression recognition (FER) has received extensive attention, but it still has lots of challenges in this area. One of the important difficulties is to remain the topological information in the feature extraction operation. In this paper, we propose a novel facial expression recognition method with lite dual channel neural network based on graph convolutional networks (DCNN-GCN). In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features. (2) The local features of ROIs and global features are extracted with dual channel neural networks, which can improve the performance of features extraction and reduce the complexity of networks. The proposed method is evaluated on CK+, Oulu-CASIA and MMI data sets. Experiment results show that the proposed method can significantly improve the accuracy of facial expression recognition. In addition, the network is much lite and suitable for application.
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allfields	10.1007/s00530-022-00907-9 doi (DE-627)SPR053084519 (SPR)s00530-022-00907-9-e DE-627 ger DE-627 rakwb eng Fan, Chunxiao verfasserin (orcid)0000-0002-6157-8362 aut Robust facial expression recognition with global-local joint representation learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract As an important part in computer vision, facial expression recognition (FER) has received extensive attention, but it still has lots of challenges in this area. One of the important difficulties is to remain the topological information in the feature extraction operation. In this paper, we propose a novel facial expression recognition method with lite dual channel neural network based on graph convolutional networks (DCNN-GCN). In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features. (2) The local features of ROIs and global features are extracted with dual channel neural networks, which can improve the performance of features extraction and reduce the complexity of networks. The proposed method is evaluated on CK+, Oulu-CASIA and MMI data sets. Experiment results show that the proposed method can significantly improve the accuracy of facial expression recognition. In addition, the network is much lite and suitable for application. Facial expression recognition (dpeaa)DE-He213 Dual channel neural network (dpeaa)DE-He213 Graph convolutional network (dpeaa)DE-He213 Wang, Zhenxing aut Li, Jia aut Wang, Shanshan aut Sun, Xiao aut Enthalten in Multimedia systems Berlin : Springer, 1993 29(2022), 5 vom: 07. März, Seite 3069-3079 (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:29 year:2022 number:5 day:07 month:03 pages:3069-3079 https://dx.doi.org/10.1007/s00530-022-00907-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 5 07 03 3069-3079
spelling	10.1007/s00530-022-00907-9 doi (DE-627)SPR053084519 (SPR)s00530-022-00907-9-e DE-627 ger DE-627 rakwb eng Fan, Chunxiao verfasserin (orcid)0000-0002-6157-8362 aut Robust facial expression recognition with global-local joint representation learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract As an important part in computer vision, facial expression recognition (FER) has received extensive attention, but it still has lots of challenges in this area. One of the important difficulties is to remain the topological information in the feature extraction operation. In this paper, we propose a novel facial expression recognition method with lite dual channel neural network based on graph convolutional networks (DCNN-GCN). In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features. (2) The local features of ROIs and global features are extracted with dual channel neural networks, which can improve the performance of features extraction and reduce the complexity of networks. The proposed method is evaluated on CK+, Oulu-CASIA and MMI data sets. Experiment results show that the proposed method can significantly improve the accuracy of facial expression recognition. In addition, the network is much lite and suitable for application. Facial expression recognition (dpeaa)DE-He213 Dual channel neural network (dpeaa)DE-He213 Graph convolutional network (dpeaa)DE-He213 Wang, Zhenxing aut Li, Jia aut Wang, Shanshan aut Sun, Xiao aut Enthalten in Multimedia systems Berlin : Springer, 1993 29(2022), 5 vom: 07. März, Seite 3069-3079 (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:29 year:2022 number:5 day:07 month:03 pages:3069-3079 https://dx.doi.org/10.1007/s00530-022-00907-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 5 07 03 3069-3079
allfields_unstemmed	10.1007/s00530-022-00907-9 doi (DE-627)SPR053084519 (SPR)s00530-022-00907-9-e DE-627 ger DE-627 rakwb eng Fan, Chunxiao verfasserin (orcid)0000-0002-6157-8362 aut Robust facial expression recognition with global-local joint representation learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract As an important part in computer vision, facial expression recognition (FER) has received extensive attention, but it still has lots of challenges in this area. One of the important difficulties is to remain the topological information in the feature extraction operation. In this paper, we propose a novel facial expression recognition method with lite dual channel neural network based on graph convolutional networks (DCNN-GCN). In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features. (2) The local features of ROIs and global features are extracted with dual channel neural networks, which can improve the performance of features extraction and reduce the complexity of networks. The proposed method is evaluated on CK+, Oulu-CASIA and MMI data sets. Experiment results show that the proposed method can significantly improve the accuracy of facial expression recognition. In addition, the network is much lite and suitable for application. Facial expression recognition (dpeaa)DE-He213 Dual channel neural network (dpeaa)DE-He213 Graph convolutional network (dpeaa)DE-He213 Wang, Zhenxing aut Li, Jia aut Wang, Shanshan aut Sun, Xiao aut Enthalten in Multimedia systems Berlin : Springer, 1993 29(2022), 5 vom: 07. März, Seite 3069-3079 (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:29 year:2022 number:5 day:07 month:03 pages:3069-3079 https://dx.doi.org/10.1007/s00530-022-00907-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 5 07 03 3069-3079
allfieldsGer	10.1007/s00530-022-00907-9 doi (DE-627)SPR053084519 (SPR)s00530-022-00907-9-e DE-627 ger DE-627 rakwb eng Fan, Chunxiao verfasserin (orcid)0000-0002-6157-8362 aut Robust facial expression recognition with global-local joint representation learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract As an important part in computer vision, facial expression recognition (FER) has received extensive attention, but it still has lots of challenges in this area. One of the important difficulties is to remain the topological information in the feature extraction operation. In this paper, we propose a novel facial expression recognition method with lite dual channel neural network based on graph convolutional networks (DCNN-GCN). In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features. (2) The local features of ROIs and global features are extracted with dual channel neural networks, which can improve the performance of features extraction and reduce the complexity of networks. The proposed method is evaluated on CK+, Oulu-CASIA and MMI data sets. Experiment results show that the proposed method can significantly improve the accuracy of facial expression recognition. In addition, the network is much lite and suitable for application. Facial expression recognition (dpeaa)DE-He213 Dual channel neural network (dpeaa)DE-He213 Graph convolutional network (dpeaa)DE-He213 Wang, Zhenxing aut Li, Jia aut Wang, Shanshan aut Sun, Xiao aut Enthalten in Multimedia systems Berlin : Springer, 1993 29(2022), 5 vom: 07. März, Seite 3069-3079 (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:29 year:2022 number:5 day:07 month:03 pages:3069-3079 https://dx.doi.org/10.1007/s00530-022-00907-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 5 07 03 3069-3079
allfieldsSound	10.1007/s00530-022-00907-9 doi (DE-627)SPR053084519 (SPR)s00530-022-00907-9-e DE-627 ger DE-627 rakwb eng Fan, Chunxiao verfasserin (orcid)0000-0002-6157-8362 aut Robust facial expression recognition with global-local joint representation learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract As an important part in computer vision, facial expression recognition (FER) has received extensive attention, but it still has lots of challenges in this area. One of the important difficulties is to remain the topological information in the feature extraction operation. In this paper, we propose a novel facial expression recognition method with lite dual channel neural network based on graph convolutional networks (DCNN-GCN). In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features. (2) The local features of ROIs and global features are extracted with dual channel neural networks, which can improve the performance of features extraction and reduce the complexity of networks. The proposed method is evaluated on CK+, Oulu-CASIA and MMI data sets. Experiment results show that the proposed method can significantly improve the accuracy of facial expression recognition. In addition, the network is much lite and suitable for application. Facial expression recognition (dpeaa)DE-He213 Dual channel neural network (dpeaa)DE-He213 Graph convolutional network (dpeaa)DE-He213 Wang, Zhenxing aut Li, Jia aut Wang, Shanshan aut Sun, Xiao aut Enthalten in Multimedia systems Berlin : Springer, 1993 29(2022), 5 vom: 07. März, Seite 3069-3079 (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:29 year:2022 number:5 day:07 month:03 pages:3069-3079 https://dx.doi.org/10.1007/s00530-022-00907-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 29 2022 5 07 03 3069-3079
language	English
source	Enthalten in Multimedia systems 29(2022), 5 vom: 07. März, Seite 3069-3079 volume:29 year:2022 number:5 day:07 month:03 pages:3069-3079
sourceStr	Enthalten in Multimedia systems 29(2022), 5 vom: 07. März, Seite 3069-3079 volume:29 year:2022 number:5 day:07 month:03 pages:3069-3079
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Facial expression recognition Dual channel neural network Graph convolutional network
isfreeaccess_bool	false
container_title	Multimedia systems
authorswithroles_txt_mv	Fan, Chunxiao @@aut@@ Wang, Zhenxing @@aut@@ Li, Jia @@aut@@ Wang, Shanshan @@aut@@ Sun, Xiao @@aut@@
publishDateDaySort_date	2022-03-07T00:00:00Z
hierarchy_top_id	254638880
id	SPR053084519
language_de	englisch
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author	Fan, Chunxiao
spellingShingle	Fan, Chunxiao misc Facial expression recognition misc Dual channel neural network misc Graph convolutional network Robust facial expression recognition with global-local joint representation learning
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topic_title	Robust facial expression recognition with global-local joint representation learning Facial expression recognition (dpeaa)DE-He213 Dual channel neural network (dpeaa)DE-He213 Graph convolutional network (dpeaa)DE-He213
topic	misc Facial expression recognition misc Dual channel neural network misc Graph convolutional network
topic_unstemmed	misc Facial expression recognition misc Dual channel neural network misc Graph convolutional network
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title	Robust facial expression recognition with global-local joint representation learning
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title_full	Robust facial expression recognition with global-local joint representation learning
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title_sort	robust facial expression recognition with global-local joint representation learning
title_auth	Robust facial expression recognition with global-local joint representation learning
abstract	Abstract As an important part in computer vision, facial expression recognition (FER) has received extensive attention, but it still has lots of challenges in this area. One of the important difficulties is to remain the topological information in the feature extraction operation. In this paper, we propose a novel facial expression recognition method with lite dual channel neural network based on graph convolutional networks (DCNN-GCN). In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features. (2) The local features of ROIs and global features are extracted with dual channel neural networks, which can improve the performance of features extraction and reduce the complexity of networks. The proposed method is evaluated on CK+, Oulu-CASIA and MMI data sets. Experiment results show that the proposed method can significantly improve the accuracy of facial expression recognition. In addition, the network is much lite and suitable for application. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
abstractGer	Abstract As an important part in computer vision, facial expression recognition (FER) has received extensive attention, but it still has lots of challenges in this area. One of the important difficulties is to remain the topological information in the feature extraction operation. In this paper, we propose a novel facial expression recognition method with lite dual channel neural network based on graph convolutional networks (DCNN-GCN). In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features. (2) The local features of ROIs and global features are extracted with dual channel neural networks, which can improve the performance of features extraction and reduce the complexity of networks. The proposed method is evaluated on CK+, Oulu-CASIA and MMI data sets. Experiment results show that the proposed method can significantly improve the accuracy of facial expression recognition. In addition, the network is much lite and suitable for application. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
abstract_unstemmed	Abstract As an important part in computer vision, facial expression recognition (FER) has received extensive attention, but it still has lots of challenges in this area. One of the important difficulties is to remain the topological information in the feature extraction operation. In this paper, we propose a novel facial expression recognition method with lite dual channel neural network based on graph convolutional networks (DCNN-GCN). In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features. (2) The local features of ROIs and global features are extracted with dual channel neural networks, which can improve the performance of features extraction and reduce the complexity of networks. The proposed method is evaluated on CK+, Oulu-CASIA and MMI data sets. Experiment results show that the proposed method can significantly improve the accuracy of facial expression recognition. In addition, the network is much lite and suitable for application. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
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container_issue	5
title_short	Robust facial expression recognition with global-local joint representation learning
url	https://dx.doi.org/10.1007/s00530-022-00907-9
remote_bool	true
author2	Wang, Zhenxing Li, Jia Wang, Shanshan Sun, Xiao
author2Str	Wang, Zhenxing Li, Jia Wang, Shanshan Sun, Xiao
ppnlink	254638880
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isOA_txt	false
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doi_str	10.1007/s00530-022-00907-9
up_date	2024-07-03T16:56:59.612Z
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