Evaluating real-life performance of the state-of-the-art in facial expression recognition using a novel YouTube-based datasets
Abstract Facial expression recognition (FER) is one of the most active areas of research in computer science, due to its importance in a large number of application domains. Over the years, a great number of FER systems have been implemented, each surpassing the other in terms of classification accu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Siddiqi, Muhammad Hameed [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 77(2017), 1 vom: 06. Jan., Seite 917-937 |
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| Übergeordnetes Werk: |
volume:77 ; year:2017 ; number:1 ; day:06 ; month:01 ; pages:917-937 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-016-4321-2 |
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OLC2035041724 |
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| 520 | |a Abstract Facial expression recognition (FER) is one of the most active areas of research in computer science, due to its importance in a large number of application domains. Over the years, a great number of FER systems have been implemented, each surpassing the other in terms of classification accuracy. However, one major weakness found in the previous studies is that they have all used standard datasets for their evaluations and comparisons. Though this serves well given the needs of a fair comparison with existing systems, it is argued that this does not go in hand with the fact that these systems are built with a hope of eventually being used in the real-world. It is because these datasets assume a predefined camera setup, consist of mostly posed expressions collected in a controlled setting, using fixed background and static ambient settings, and having low variations in the face size and camera angles, which is not the case in a dynamic real-world. The contributions of this work are two-fold: firstly, using numerous online resources and also our own setup, we have collected a rich FER dataset keeping in mind the above mentioned problems. Secondly, we have chosen eleven state-of-the-art FER systems, implemented them and performed a rigorous evaluation of these systems using our dataset. The results confirm our hypothesis that even the most accurate existing FER systems are not ready to face the challenges of a dynamic real-world. We hope that our dataset would become a benchmark to assess the real-life performance of future FER systems. | ||
| 650 | 4 | |a Facial expressions | |
| 650 | 4 | |a Classification | |
| 650 | 4 | |a YouTube | |
| 650 | 4 | |a Real-life scenarios | |
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| 700 | 1 | |a Banos, Oresti |4 aut | |
| 700 | 1 | |a Khan, Adil Mehmood |4 aut | |
| 700 | 1 | |a Lee, Sungyoung |4 aut | |
| 700 | 1 | |a Choo, Hyunseung |4 aut | |
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10.1007/s11042-016-4321-2 doi (DE-627)OLC2035041724 (DE-He213)s11042-016-4321-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Siddiqi, Muhammad Hameed verfasserin aut Evaluating real-life performance of the state-of-the-art in facial expression recognition using a novel YouTube-based datasets 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract Facial expression recognition (FER) is one of the most active areas of research in computer science, due to its importance in a large number of application domains. Over the years, a great number of FER systems have been implemented, each surpassing the other in terms of classification accuracy. However, one major weakness found in the previous studies is that they have all used standard datasets for their evaluations and comparisons. Though this serves well given the needs of a fair comparison with existing systems, it is argued that this does not go in hand with the fact that these systems are built with a hope of eventually being used in the real-world. It is because these datasets assume a predefined camera setup, consist of mostly posed expressions collected in a controlled setting, using fixed background and static ambient settings, and having low variations in the face size and camera angles, which is not the case in a dynamic real-world. The contributions of this work are two-fold: firstly, using numerous online resources and also our own setup, we have collected a rich FER dataset keeping in mind the above mentioned problems. Secondly, we have chosen eleven state-of-the-art FER systems, implemented them and performed a rigorous evaluation of these systems using our dataset. The results confirm our hypothesis that even the most accurate existing FER systems are not ready to face the challenges of a dynamic real-world. We hope that our dataset would become a benchmark to assess the real-life performance of future FER systems. Facial expressions Classification YouTube Real-life scenarios Ali, Maqbool aut Abdelrahman Eldib, Mohamed Elsayed aut Khan, Asfandyar aut Banos, Oresti aut Khan, Adil Mehmood aut Lee, Sungyoung aut Choo, Hyunseung aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2017), 1 vom: 06. Jan., Seite 917-937 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2017 number:1 day:06 month:01 pages:917-937 https://doi.org/10.1007/s11042-016-4321-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2017 1 06 01 917-937 |
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10.1007/s11042-016-4321-2 doi (DE-627)OLC2035041724 (DE-He213)s11042-016-4321-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Siddiqi, Muhammad Hameed verfasserin aut Evaluating real-life performance of the state-of-the-art in facial expression recognition using a novel YouTube-based datasets 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract Facial expression recognition (FER) is one of the most active areas of research in computer science, due to its importance in a large number of application domains. Over the years, a great number of FER systems have been implemented, each surpassing the other in terms of classification accuracy. However, one major weakness found in the previous studies is that they have all used standard datasets for their evaluations and comparisons. Though this serves well given the needs of a fair comparison with existing systems, it is argued that this does not go in hand with the fact that these systems are built with a hope of eventually being used in the real-world. It is because these datasets assume a predefined camera setup, consist of mostly posed expressions collected in a controlled setting, using fixed background and static ambient settings, and having low variations in the face size and camera angles, which is not the case in a dynamic real-world. The contributions of this work are two-fold: firstly, using numerous online resources and also our own setup, we have collected a rich FER dataset keeping in mind the above mentioned problems. Secondly, we have chosen eleven state-of-the-art FER systems, implemented them and performed a rigorous evaluation of these systems using our dataset. The results confirm our hypothesis that even the most accurate existing FER systems are not ready to face the challenges of a dynamic real-world. We hope that our dataset would become a benchmark to assess the real-life performance of future FER systems. Facial expressions Classification YouTube Real-life scenarios Ali, Maqbool aut Abdelrahman Eldib, Mohamed Elsayed aut Khan, Asfandyar aut Banos, Oresti aut Khan, Adil Mehmood aut Lee, Sungyoung aut Choo, Hyunseung aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2017), 1 vom: 06. Jan., Seite 917-937 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2017 number:1 day:06 month:01 pages:917-937 https://doi.org/10.1007/s11042-016-4321-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2017 1 06 01 917-937 |
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10.1007/s11042-016-4321-2 doi (DE-627)OLC2035041724 (DE-He213)s11042-016-4321-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Siddiqi, Muhammad Hameed verfasserin aut Evaluating real-life performance of the state-of-the-art in facial expression recognition using a novel YouTube-based datasets 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract Facial expression recognition (FER) is one of the most active areas of research in computer science, due to its importance in a large number of application domains. Over the years, a great number of FER systems have been implemented, each surpassing the other in terms of classification accuracy. However, one major weakness found in the previous studies is that they have all used standard datasets for their evaluations and comparisons. Though this serves well given the needs of a fair comparison with existing systems, it is argued that this does not go in hand with the fact that these systems are built with a hope of eventually being used in the real-world. It is because these datasets assume a predefined camera setup, consist of mostly posed expressions collected in a controlled setting, using fixed background and static ambient settings, and having low variations in the face size and camera angles, which is not the case in a dynamic real-world. The contributions of this work are two-fold: firstly, using numerous online resources and also our own setup, we have collected a rich FER dataset keeping in mind the above mentioned problems. Secondly, we have chosen eleven state-of-the-art FER systems, implemented them and performed a rigorous evaluation of these systems using our dataset. The results confirm our hypothesis that even the most accurate existing FER systems are not ready to face the challenges of a dynamic real-world. We hope that our dataset would become a benchmark to assess the real-life performance of future FER systems. Facial expressions Classification YouTube Real-life scenarios Ali, Maqbool aut Abdelrahman Eldib, Mohamed Elsayed aut Khan, Asfandyar aut Banos, Oresti aut Khan, Adil Mehmood aut Lee, Sungyoung aut Choo, Hyunseung aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2017), 1 vom: 06. Jan., Seite 917-937 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2017 number:1 day:06 month:01 pages:917-937 https://doi.org/10.1007/s11042-016-4321-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2017 1 06 01 917-937 |
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10.1007/s11042-016-4321-2 doi (DE-627)OLC2035041724 (DE-He213)s11042-016-4321-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Siddiqi, Muhammad Hameed verfasserin aut Evaluating real-life performance of the state-of-the-art in facial expression recognition using a novel YouTube-based datasets 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract Facial expression recognition (FER) is one of the most active areas of research in computer science, due to its importance in a large number of application domains. Over the years, a great number of FER systems have been implemented, each surpassing the other in terms of classification accuracy. However, one major weakness found in the previous studies is that they have all used standard datasets for their evaluations and comparisons. Though this serves well given the needs of a fair comparison with existing systems, it is argued that this does not go in hand with the fact that these systems are built with a hope of eventually being used in the real-world. It is because these datasets assume a predefined camera setup, consist of mostly posed expressions collected in a controlled setting, using fixed background and static ambient settings, and having low variations in the face size and camera angles, which is not the case in a dynamic real-world. The contributions of this work are two-fold: firstly, using numerous online resources and also our own setup, we have collected a rich FER dataset keeping in mind the above mentioned problems. Secondly, we have chosen eleven state-of-the-art FER systems, implemented them and performed a rigorous evaluation of these systems using our dataset. The results confirm our hypothesis that even the most accurate existing FER systems are not ready to face the challenges of a dynamic real-world. We hope that our dataset would become a benchmark to assess the real-life performance of future FER systems. Facial expressions Classification YouTube Real-life scenarios Ali, Maqbool aut Abdelrahman Eldib, Mohamed Elsayed aut Khan, Asfandyar aut Banos, Oresti aut Khan, Adil Mehmood aut Lee, Sungyoung aut Choo, Hyunseung aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2017), 1 vom: 06. Jan., Seite 917-937 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2017 number:1 day:06 month:01 pages:917-937 https://doi.org/10.1007/s11042-016-4321-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2017 1 06 01 917-937 |
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Evaluating real-life performance of the state-of-the-art in facial expression recognition using a novel YouTube-based datasets |
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Evaluating real-life performance of the state-of-the-art in facial expression recognition using a novel YouTube-based datasets |
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Siddiqi, Muhammad Hameed |
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Siddiqi, Muhammad Hameed Ali, Maqbool Abdelrahman Eldib, Mohamed Elsayed Khan, Asfandyar Banos, Oresti Khan, Adil Mehmood Lee, Sungyoung Choo, Hyunseung |
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10.1007/s11042-016-4321-2 |
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evaluating real-life performance of the state-of-the-art in facial expression recognition using a novel youtube-based datasets |
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Evaluating real-life performance of the state-of-the-art in facial expression recognition using a novel YouTube-based datasets |
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Abstract Facial expression recognition (FER) is one of the most active areas of research in computer science, due to its importance in a large number of application domains. Over the years, a great number of FER systems have been implemented, each surpassing the other in terms of classification accuracy. However, one major weakness found in the previous studies is that they have all used standard datasets for their evaluations and comparisons. Though this serves well given the needs of a fair comparison with existing systems, it is argued that this does not go in hand with the fact that these systems are built with a hope of eventually being used in the real-world. It is because these datasets assume a predefined camera setup, consist of mostly posed expressions collected in a controlled setting, using fixed background and static ambient settings, and having low variations in the face size and camera angles, which is not the case in a dynamic real-world. The contributions of this work are two-fold: firstly, using numerous online resources and also our own setup, we have collected a rich FER dataset keeping in mind the above mentioned problems. Secondly, we have chosen eleven state-of-the-art FER systems, implemented them and performed a rigorous evaluation of these systems using our dataset. The results confirm our hypothesis that even the most accurate existing FER systems are not ready to face the challenges of a dynamic real-world. We hope that our dataset would become a benchmark to assess the real-life performance of future FER systems. © Springer Science+Business Media New York 2017 |
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Abstract Facial expression recognition (FER) is one of the most active areas of research in computer science, due to its importance in a large number of application domains. Over the years, a great number of FER systems have been implemented, each surpassing the other in terms of classification accuracy. However, one major weakness found in the previous studies is that they have all used standard datasets for their evaluations and comparisons. Though this serves well given the needs of a fair comparison with existing systems, it is argued that this does not go in hand with the fact that these systems are built with a hope of eventually being used in the real-world. It is because these datasets assume a predefined camera setup, consist of mostly posed expressions collected in a controlled setting, using fixed background and static ambient settings, and having low variations in the face size and camera angles, which is not the case in a dynamic real-world. The contributions of this work are two-fold: firstly, using numerous online resources and also our own setup, we have collected a rich FER dataset keeping in mind the above mentioned problems. Secondly, we have chosen eleven state-of-the-art FER systems, implemented them and performed a rigorous evaluation of these systems using our dataset. The results confirm our hypothesis that even the most accurate existing FER systems are not ready to face the challenges of a dynamic real-world. We hope that our dataset would become a benchmark to assess the real-life performance of future FER systems. © Springer Science+Business Media New York 2017 |
| abstract_unstemmed |
Abstract Facial expression recognition (FER) is one of the most active areas of research in computer science, due to its importance in a large number of application domains. Over the years, a great number of FER systems have been implemented, each surpassing the other in terms of classification accuracy. However, one major weakness found in the previous studies is that they have all used standard datasets for their evaluations and comparisons. Though this serves well given the needs of a fair comparison with existing systems, it is argued that this does not go in hand with the fact that these systems are built with a hope of eventually being used in the real-world. It is because these datasets assume a predefined camera setup, consist of mostly posed expressions collected in a controlled setting, using fixed background and static ambient settings, and having low variations in the face size and camera angles, which is not the case in a dynamic real-world. The contributions of this work are two-fold: firstly, using numerous online resources and also our own setup, we have collected a rich FER dataset keeping in mind the above mentioned problems. Secondly, we have chosen eleven state-of-the-art FER systems, implemented them and performed a rigorous evaluation of these systems using our dataset. The results confirm our hypothesis that even the most accurate existing FER systems are not ready to face the challenges of a dynamic real-world. We hope that our dataset would become a benchmark to assess the real-life performance of future FER systems. © Springer Science+Business Media New York 2017 |
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