Pose-invariant face recognition with multitask cascade networks
Abstract In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Elharrouss, Omar [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 34(2022), 8 vom: 09. Jan., Seite 6039-6052 |
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| Übergeordnetes Werk: |
volume:34 ; year:2022 ; number:8 ; day:09 ; month:01 ; pages:6039-6052 |
| Links: |
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| DOI / URN: |
10.1007/s00521-021-06690-4 |
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OLC2078267201 |
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| 520 | |a Abstract In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the left side, frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g., background content), we propose a skin-based face segmentation method using structure–texture decomposition and the color-invariant descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) and is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness. | ||
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| 700 | 1 | |a Al-Maadeed, Somaya |4 aut | |
| 700 | 1 | |a Khelifi, Fouad |4 aut | |
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10.1007/s00521-021-06690-4 doi (DE-627)OLC2078267201 (DE-He213)s00521-021-06690-4-p DE-627 ger DE-627 rakwb eng 004 VZ Elharrouss, Omar verfasserin (orcid)0000-0002-5341-5440 aut Pose-invariant face recognition with multitask cascade networks 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the left side, frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g., background content), we propose a skin-based face segmentation method using structure–texture decomposition and the color-invariant descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) and is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness. Face recognition Pose estimation Pose-invariant Skin segmentation Convolutional Neural Networks Almaadeed, Noor aut Al-Maadeed, Somaya aut Khelifi, Fouad aut Enthalten in Neural computing & applications Springer London, 1993 34(2022), 8 vom: 09. Jan., Seite 6039-6052 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2022 number:8 day:09 month:01 pages:6039-6052 https://doi.org/10.1007/s00521-021-06690-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2022 8 09 01 6039-6052 |
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10.1007/s00521-021-06690-4 doi (DE-627)OLC2078267201 (DE-He213)s00521-021-06690-4-p DE-627 ger DE-627 rakwb eng 004 VZ Elharrouss, Omar verfasserin (orcid)0000-0002-5341-5440 aut Pose-invariant face recognition with multitask cascade networks 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the left side, frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g., background content), we propose a skin-based face segmentation method using structure–texture decomposition and the color-invariant descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) and is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness. Face recognition Pose estimation Pose-invariant Skin segmentation Convolutional Neural Networks Almaadeed, Noor aut Al-Maadeed, Somaya aut Khelifi, Fouad aut Enthalten in Neural computing & applications Springer London, 1993 34(2022), 8 vom: 09. Jan., Seite 6039-6052 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2022 number:8 day:09 month:01 pages:6039-6052 https://doi.org/10.1007/s00521-021-06690-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2022 8 09 01 6039-6052 |
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10.1007/s00521-021-06690-4 doi (DE-627)OLC2078267201 (DE-He213)s00521-021-06690-4-p DE-627 ger DE-627 rakwb eng 004 VZ Elharrouss, Omar verfasserin (orcid)0000-0002-5341-5440 aut Pose-invariant face recognition with multitask cascade networks 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the left side, frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g., background content), we propose a skin-based face segmentation method using structure–texture decomposition and the color-invariant descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) and is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness. Face recognition Pose estimation Pose-invariant Skin segmentation Convolutional Neural Networks Almaadeed, Noor aut Al-Maadeed, Somaya aut Khelifi, Fouad aut Enthalten in Neural computing & applications Springer London, 1993 34(2022), 8 vom: 09. Jan., Seite 6039-6052 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2022 number:8 day:09 month:01 pages:6039-6052 https://doi.org/10.1007/s00521-021-06690-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2022 8 09 01 6039-6052 |
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10.1007/s00521-021-06690-4 doi (DE-627)OLC2078267201 (DE-He213)s00521-021-06690-4-p DE-627 ger DE-627 rakwb eng 004 VZ Elharrouss, Omar verfasserin (orcid)0000-0002-5341-5440 aut Pose-invariant face recognition with multitask cascade networks 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the left side, frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g., background content), we propose a skin-based face segmentation method using structure–texture decomposition and the color-invariant descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) and is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness. Face recognition Pose estimation Pose-invariant Skin segmentation Convolutional Neural Networks Almaadeed, Noor aut Al-Maadeed, Somaya aut Khelifi, Fouad aut Enthalten in Neural computing & applications Springer London, 1993 34(2022), 8 vom: 09. Jan., Seite 6039-6052 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2022 number:8 day:09 month:01 pages:6039-6052 https://doi.org/10.1007/s00521-021-06690-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2022 8 09 01 6039-6052 |
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10.1007/s00521-021-06690-4 doi (DE-627)OLC2078267201 (DE-He213)s00521-021-06690-4-p DE-627 ger DE-627 rakwb eng 004 VZ Elharrouss, Omar verfasserin (orcid)0000-0002-5341-5440 aut Pose-invariant face recognition with multitask cascade networks 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the left side, frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g., background content), we propose a skin-based face segmentation method using structure–texture decomposition and the color-invariant descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) and is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness. Face recognition Pose estimation Pose-invariant Skin segmentation Convolutional Neural Networks Almaadeed, Noor aut Al-Maadeed, Somaya aut Khelifi, Fouad aut Enthalten in Neural computing & applications Springer London, 1993 34(2022), 8 vom: 09. Jan., Seite 6039-6052 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2022 number:8 day:09 month:01 pages:6039-6052 https://doi.org/10.1007/s00521-021-06690-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2022 8 09 01 6039-6052 |
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Elharrouss, Omar |
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10.1007/s00521-021-06690-4 |
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pose-invariant face recognition with multitask cascade networks |
| title_auth |
Pose-invariant face recognition with multitask cascade networks |
| abstract |
Abstract In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the left side, frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g., background content), we propose a skin-based face segmentation method using structure–texture decomposition and the color-invariant descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) and is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
| abstractGer |
Abstract In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the left side, frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g., background content), we propose a skin-based face segmentation method using structure–texture decomposition and the color-invariant descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) and is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the left side, frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g., background content), we propose a skin-based face segmentation method using structure–texture decomposition and the color-invariant descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) and is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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Pose-invariant face recognition with multitask cascade networks |
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Almaadeed, Noor Al-Maadeed, Somaya Khelifi, Fouad |
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Almaadeed, Noor Al-Maadeed, Somaya Khelifi, Fouad |
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2024-07-03T19:37:18.864Z |
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