Robust facial expression recognition using RGB-D images and multichannel features
Abstract Traditional and classical methods of facial expression recognition are mainly based on intensity image and are prone to be disturbed by illumination, poses, and disguise. This research aims to develop a robust facial expression recognition method using RGB-D images and multichannel features...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cai, Linqin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 78(2018), 20 vom: 02. Mai, Seite 28591-28607 |
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| Übergeordnetes Werk: |
volume:78 ; year:2018 ; number:20 ; day:02 ; month:05 ; pages:28591-28607 |
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10.1007/s11042-018-5981-x |
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| 520 | |a Abstract Traditional and classical methods of facial expression recognition are mainly based on intensity image and are prone to be disturbed by illumination, poses, and disguise. This research aims to develop a robust facial expression recognition method using RGB-D images and multichannel features. Based on image entropy and visual saliency, facial texture features are firstly extracted from RGB images and depth images to construct the Histogram of Oriented Gradient (HOG) descriptors. And then, we extract geometric features of RGB images using Active Appearance Model (AAM). Combining the HOG texture features with the AAM geometric feature, we build a robust multichannel feature vector for facial expression recognition. On this basis, an improved Support Vector Machine (SVM) algorithm, namely GS-SVM, is used to classify facial expression recognition. The proposed GS-SVM algorithm applies Grid Search method to optimize the best parameters for SVM classifier and estimate the accuracy of each parameter combination in specified range. Finally, the proposed methods are tested and evaluated on the merged RGB-D database. Experimental results show that the proposed algorithm not only achieves a higher average recognition rate but also is robust to uncontrolled environments. | ||
| 650 | 4 | |a Facial expression recognition | |
| 650 | 4 | |a Saliency and entropy | |
| 650 | 4 | |a Active appearance model | |
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| 700 | 1 | |a Xu, Hongbo |4 aut | |
| 700 | 1 | |a Yang, Yang |4 aut | |
| 700 | 1 | |a Yu, Jimin |4 aut | |
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10.1007/s11042-018-5981-x doi (DE-627)OLC2035069696 (DE-He213)s11042-018-5981-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Cai, Linqin verfasserin aut Robust facial expression recognition using RGB-D images and multichannel features 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Traditional and classical methods of facial expression recognition are mainly based on intensity image and are prone to be disturbed by illumination, poses, and disguise. This research aims to develop a robust facial expression recognition method using RGB-D images and multichannel features. Based on image entropy and visual saliency, facial texture features are firstly extracted from RGB images and depth images to construct the Histogram of Oriented Gradient (HOG) descriptors. And then, we extract geometric features of RGB images using Active Appearance Model (AAM). Combining the HOG texture features with the AAM geometric feature, we build a robust multichannel feature vector for facial expression recognition. On this basis, an improved Support Vector Machine (SVM) algorithm, namely GS-SVM, is used to classify facial expression recognition. The proposed GS-SVM algorithm applies Grid Search method to optimize the best parameters for SVM classifier and estimate the accuracy of each parameter combination in specified range. Finally, the proposed methods are tested and evaluated on the merged RGB-D database. Experimental results show that the proposed algorithm not only achieves a higher average recognition rate but also is robust to uncontrolled environments. Facial expression recognition Saliency and entropy Active appearance model Support vector machine Xu, Hongbo aut Yang, Yang aut Yu, Jimin aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2018), 20 vom: 02. Mai, Seite 28591-28607 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2018 number:20 day:02 month:05 pages:28591-28607 https://doi.org/10.1007/s11042-018-5981-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2018 20 02 05 28591-28607 |
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10.1007/s11042-018-5981-x doi (DE-627)OLC2035069696 (DE-He213)s11042-018-5981-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Cai, Linqin verfasserin aut Robust facial expression recognition using RGB-D images and multichannel features 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Traditional and classical methods of facial expression recognition are mainly based on intensity image and are prone to be disturbed by illumination, poses, and disguise. This research aims to develop a robust facial expression recognition method using RGB-D images and multichannel features. Based on image entropy and visual saliency, facial texture features are firstly extracted from RGB images and depth images to construct the Histogram of Oriented Gradient (HOG) descriptors. And then, we extract geometric features of RGB images using Active Appearance Model (AAM). Combining the HOG texture features with the AAM geometric feature, we build a robust multichannel feature vector for facial expression recognition. On this basis, an improved Support Vector Machine (SVM) algorithm, namely GS-SVM, is used to classify facial expression recognition. The proposed GS-SVM algorithm applies Grid Search method to optimize the best parameters for SVM classifier and estimate the accuracy of each parameter combination in specified range. Finally, the proposed methods are tested and evaluated on the merged RGB-D database. Experimental results show that the proposed algorithm not only achieves a higher average recognition rate but also is robust to uncontrolled environments. Facial expression recognition Saliency and entropy Active appearance model Support vector machine Xu, Hongbo aut Yang, Yang aut Yu, Jimin aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2018), 20 vom: 02. Mai, Seite 28591-28607 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2018 number:20 day:02 month:05 pages:28591-28607 https://doi.org/10.1007/s11042-018-5981-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2018 20 02 05 28591-28607 |
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10.1007/s11042-018-5981-x doi (DE-627)OLC2035069696 (DE-He213)s11042-018-5981-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Cai, Linqin verfasserin aut Robust facial expression recognition using RGB-D images and multichannel features 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Traditional and classical methods of facial expression recognition are mainly based on intensity image and are prone to be disturbed by illumination, poses, and disguise. This research aims to develop a robust facial expression recognition method using RGB-D images and multichannel features. Based on image entropy and visual saliency, facial texture features are firstly extracted from RGB images and depth images to construct the Histogram of Oriented Gradient (HOG) descriptors. And then, we extract geometric features of RGB images using Active Appearance Model (AAM). Combining the HOG texture features with the AAM geometric feature, we build a robust multichannel feature vector for facial expression recognition. On this basis, an improved Support Vector Machine (SVM) algorithm, namely GS-SVM, is used to classify facial expression recognition. The proposed GS-SVM algorithm applies Grid Search method to optimize the best parameters for SVM classifier and estimate the accuracy of each parameter combination in specified range. Finally, the proposed methods are tested and evaluated on the merged RGB-D database. Experimental results show that the proposed algorithm not only achieves a higher average recognition rate but also is robust to uncontrolled environments. Facial expression recognition Saliency and entropy Active appearance model Support vector machine Xu, Hongbo aut Yang, Yang aut Yu, Jimin aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2018), 20 vom: 02. Mai, Seite 28591-28607 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2018 number:20 day:02 month:05 pages:28591-28607 https://doi.org/10.1007/s11042-018-5981-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2018 20 02 05 28591-28607 |
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10.1007/s11042-018-5981-x doi (DE-627)OLC2035069696 (DE-He213)s11042-018-5981-x-p DE-627 ger DE-627 rakwb eng 070 004 VZ Cai, Linqin verfasserin aut Robust facial expression recognition using RGB-D images and multichannel features 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Traditional and classical methods of facial expression recognition are mainly based on intensity image and are prone to be disturbed by illumination, poses, and disguise. This research aims to develop a robust facial expression recognition method using RGB-D images and multichannel features. Based on image entropy and visual saliency, facial texture features are firstly extracted from RGB images and depth images to construct the Histogram of Oriented Gradient (HOG) descriptors. And then, we extract geometric features of RGB images using Active Appearance Model (AAM). Combining the HOG texture features with the AAM geometric feature, we build a robust multichannel feature vector for facial expression recognition. On this basis, an improved Support Vector Machine (SVM) algorithm, namely GS-SVM, is used to classify facial expression recognition. The proposed GS-SVM algorithm applies Grid Search method to optimize the best parameters for SVM classifier and estimate the accuracy of each parameter combination in specified range. Finally, the proposed methods are tested and evaluated on the merged RGB-D database. Experimental results show that the proposed algorithm not only achieves a higher average recognition rate but also is robust to uncontrolled environments. Facial expression recognition Saliency and entropy Active appearance model Support vector machine Xu, Hongbo aut Yang, Yang aut Yu, Jimin aut Enthalten in Multimedia tools and applications Springer US, 1995 78(2018), 20 vom: 02. Mai, Seite 28591-28607 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:78 year:2018 number:20 day:02 month:05 pages:28591-28607 https://doi.org/10.1007/s11042-018-5981-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 78 2018 20 02 05 28591-28607 |
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Abstract Traditional and classical methods of facial expression recognition are mainly based on intensity image and are prone to be disturbed by illumination, poses, and disguise. This research aims to develop a robust facial expression recognition method using RGB-D images and multichannel features. Based on image entropy and visual saliency, facial texture features are firstly extracted from RGB images and depth images to construct the Histogram of Oriented Gradient (HOG) descriptors. And then, we extract geometric features of RGB images using Active Appearance Model (AAM). Combining the HOG texture features with the AAM geometric feature, we build a robust multichannel feature vector for facial expression recognition. On this basis, an improved Support Vector Machine (SVM) algorithm, namely GS-SVM, is used to classify facial expression recognition. The proposed GS-SVM algorithm applies Grid Search method to optimize the best parameters for SVM classifier and estimate the accuracy of each parameter combination in specified range. Finally, the proposed methods are tested and evaluated on the merged RGB-D database. Experimental results show that the proposed algorithm not only achieves a higher average recognition rate but also is robust to uncontrolled environments. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract Traditional and classical methods of facial expression recognition are mainly based on intensity image and are prone to be disturbed by illumination, poses, and disguise. This research aims to develop a robust facial expression recognition method using RGB-D images and multichannel features. Based on image entropy and visual saliency, facial texture features are firstly extracted from RGB images and depth images to construct the Histogram of Oriented Gradient (HOG) descriptors. And then, we extract geometric features of RGB images using Active Appearance Model (AAM). Combining the HOG texture features with the AAM geometric feature, we build a robust multichannel feature vector for facial expression recognition. On this basis, an improved Support Vector Machine (SVM) algorithm, namely GS-SVM, is used to classify facial expression recognition. The proposed GS-SVM algorithm applies Grid Search method to optimize the best parameters for SVM classifier and estimate the accuracy of each parameter combination in specified range. Finally, the proposed methods are tested and evaluated on the merged RGB-D database. Experimental results show that the proposed algorithm not only achieves a higher average recognition rate but also is robust to uncontrolled environments. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Traditional and classical methods of facial expression recognition are mainly based on intensity image and are prone to be disturbed by illumination, poses, and disguise. This research aims to develop a robust facial expression recognition method using RGB-D images and multichannel features. Based on image entropy and visual saliency, facial texture features are firstly extracted from RGB images and depth images to construct the Histogram of Oriented Gradient (HOG) descriptors. And then, we extract geometric features of RGB images using Active Appearance Model (AAM). Combining the HOG texture features with the AAM geometric feature, we build a robust multichannel feature vector for facial expression recognition. On this basis, an improved Support Vector Machine (SVM) algorithm, namely GS-SVM, is used to classify facial expression recognition. The proposed GS-SVM algorithm applies Grid Search method to optimize the best parameters for SVM classifier and estimate the accuracy of each parameter combination in specified range. Finally, the proposed methods are tested and evaluated on the merged RGB-D database. Experimental results show that the proposed algorithm not only achieves a higher average recognition rate but also is robust to uncontrolled environments. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Robust facial expression recognition using RGB-D images and multichannel features |
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Xu, Hongbo Yang, Yang Yu, Jimin |
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