EyeLSD a Robust Approach for Eye Localization and State Detection
Abstract Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Eddine, Benrachou Djamel [verfasserIn] dos Santos, Filipe Neves [verfasserIn] Boulebtateche, Brahim [verfasserIn] Bensaoula, Salah [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of VLSI signal processing systems for signal, image and video technology - Springer Netherlands, 1989, 90(2017), 1 vom: 31. Jan., Seite 99-125 |
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| Übergeordnetes Werk: |
volume:90 ; year:2017 ; number:1 ; day:31 ; month:01 ; pages:99-125 |
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| DOI / URN: |
10.1007/s11265-016-1219-1 |
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| 520 | |a Abstract Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery will help to prevent accidents and deaths. This paper proposes a new framework called EyeLSD to localize the eyes and detect their states without face detection step. For EyeLSD aims, two novel descriptors are proposed: enhanced Pyramidal Local Binary Pattern Histogram (ePLBPH) and Multi-Three-Patch LBP histogram (Multi-TPLBP). The performance of EyeLSD with ePLBPH and Multi-TPLBP is evaluated and compared against other approaches. For this evaluation three independent and public datasets were used: BioID, CAS-PEAL-R1 and ZJU datasets. The set EyeLSD, ePLBPH and Multi-TPLBP have a greater performance when compared against the state-of-the-art algorithms. The proposed approach is very stable under large range of eye appearances caused by expression, rotation, lighting, head pose, and occlusion. | ||
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| 700 | 1 | |a Bensaoula, Salah |e verfasserin |4 aut | |
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10.1007/s11265-016-1219-1 doi (DE-627)SPR018331785 (SPR)s11265-016-1219-1-e DE-627 ger DE-627 rakwb eng Eddine, Benrachou Djamel verfasserin aut EyeLSD a Robust Approach for Eye Localization and State Detection 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery will help to prevent accidents and deaths. This paper proposes a new framework called EyeLSD to localize the eyes and detect their states without face detection step. For EyeLSD aims, two novel descriptors are proposed: enhanced Pyramidal Local Binary Pattern Histogram (ePLBPH) and Multi-Three-Patch LBP histogram (Multi-TPLBP). The performance of EyeLSD with ePLBPH and Multi-TPLBP is evaluated and compared against other approaches. For this evaluation three independent and public datasets were used: BioID, CAS-PEAL-R1 and ZJU datasets. The set EyeLSD, ePLBPH and Multi-TPLBP have a greater performance when compared against the state-of-the-art algorithms. The proposed approach is very stable under large range of eye appearances caused by expression, rotation, lighting, head pose, and occlusion. Eye localization (dpeaa)DE-He213 Eye state measurement (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 dos Santos, Filipe Neves verfasserin aut Boulebtateche, Brahim verfasserin aut Bensaoula, Salah verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 90(2017), 1 vom: 31. Jan., Seite 99-125 (DE-627)SPR018308090 nnns volume:90 year:2017 number:1 day:31 month:01 pages:99-125 https://dx.doi.org/10.1007/s11265-016-1219-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 90 2017 1 31 01 99-125 |
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10.1007/s11265-016-1219-1 doi (DE-627)SPR018331785 (SPR)s11265-016-1219-1-e DE-627 ger DE-627 rakwb eng Eddine, Benrachou Djamel verfasserin aut EyeLSD a Robust Approach for Eye Localization and State Detection 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery will help to prevent accidents and deaths. This paper proposes a new framework called EyeLSD to localize the eyes and detect their states without face detection step. For EyeLSD aims, two novel descriptors are proposed: enhanced Pyramidal Local Binary Pattern Histogram (ePLBPH) and Multi-Three-Patch LBP histogram (Multi-TPLBP). The performance of EyeLSD with ePLBPH and Multi-TPLBP is evaluated and compared against other approaches. For this evaluation three independent and public datasets were used: BioID, CAS-PEAL-R1 and ZJU datasets. The set EyeLSD, ePLBPH and Multi-TPLBP have a greater performance when compared against the state-of-the-art algorithms. The proposed approach is very stable under large range of eye appearances caused by expression, rotation, lighting, head pose, and occlusion. Eye localization (dpeaa)DE-He213 Eye state measurement (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 dos Santos, Filipe Neves verfasserin aut Boulebtateche, Brahim verfasserin aut Bensaoula, Salah verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 90(2017), 1 vom: 31. Jan., Seite 99-125 (DE-627)SPR018308090 nnns volume:90 year:2017 number:1 day:31 month:01 pages:99-125 https://dx.doi.org/10.1007/s11265-016-1219-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 90 2017 1 31 01 99-125 |
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10.1007/s11265-016-1219-1 doi (DE-627)SPR018331785 (SPR)s11265-016-1219-1-e DE-627 ger DE-627 rakwb eng Eddine, Benrachou Djamel verfasserin aut EyeLSD a Robust Approach for Eye Localization and State Detection 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery will help to prevent accidents and deaths. This paper proposes a new framework called EyeLSD to localize the eyes and detect their states without face detection step. For EyeLSD aims, two novel descriptors are proposed: enhanced Pyramidal Local Binary Pattern Histogram (ePLBPH) and Multi-Three-Patch LBP histogram (Multi-TPLBP). The performance of EyeLSD with ePLBPH and Multi-TPLBP is evaluated and compared against other approaches. For this evaluation three independent and public datasets were used: BioID, CAS-PEAL-R1 and ZJU datasets. The set EyeLSD, ePLBPH and Multi-TPLBP have a greater performance when compared against the state-of-the-art algorithms. The proposed approach is very stable under large range of eye appearances caused by expression, rotation, lighting, head pose, and occlusion. Eye localization (dpeaa)DE-He213 Eye state measurement (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 dos Santos, Filipe Neves verfasserin aut Boulebtateche, Brahim verfasserin aut Bensaoula, Salah verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 90(2017), 1 vom: 31. Jan., Seite 99-125 (DE-627)SPR018308090 nnns volume:90 year:2017 number:1 day:31 month:01 pages:99-125 https://dx.doi.org/10.1007/s11265-016-1219-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 90 2017 1 31 01 99-125 |
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Abstract Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery will help to prevent accidents and deaths. This paper proposes a new framework called EyeLSD to localize the eyes and detect their states without face detection step. For EyeLSD aims, two novel descriptors are proposed: enhanced Pyramidal Local Binary Pattern Histogram (ePLBPH) and Multi-Three-Patch LBP histogram (Multi-TPLBP). The performance of EyeLSD with ePLBPH and Multi-TPLBP is evaluated and compared against other approaches. For this evaluation three independent and public datasets were used: BioID, CAS-PEAL-R1 and ZJU datasets. The set EyeLSD, ePLBPH and Multi-TPLBP have a greater performance when compared against the state-of-the-art algorithms. The proposed approach is very stable under large range of eye appearances caused by expression, rotation, lighting, head pose, and occlusion. |
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Abstract Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery will help to prevent accidents and deaths. This paper proposes a new framework called EyeLSD to localize the eyes and detect their states without face detection step. For EyeLSD aims, two novel descriptors are proposed: enhanced Pyramidal Local Binary Pattern Histogram (ePLBPH) and Multi-Three-Patch LBP histogram (Multi-TPLBP). The performance of EyeLSD with ePLBPH and Multi-TPLBP is evaluated and compared against other approaches. For this evaluation three independent and public datasets were used: BioID, CAS-PEAL-R1 and ZJU datasets. The set EyeLSD, ePLBPH and Multi-TPLBP have a greater performance when compared against the state-of-the-art algorithms. The proposed approach is very stable under large range of eye appearances caused by expression, rotation, lighting, head pose, and occlusion. |
| abstract_unstemmed |
Abstract Improving the safety of public roads and industrial factories requires more reliable and robust computer vision-based approaches for monitoring the eye state (open or closed) of human operators. Getting this information in real time when humans are driving cars or using hazardous machinery will help to prevent accidents and deaths. This paper proposes a new framework called EyeLSD to localize the eyes and detect their states without face detection step. For EyeLSD aims, two novel descriptors are proposed: enhanced Pyramidal Local Binary Pattern Histogram (ePLBPH) and Multi-Three-Patch LBP histogram (Multi-TPLBP). The performance of EyeLSD with ePLBPH and Multi-TPLBP is evaluated and compared against other approaches. For this evaluation three independent and public datasets were used: BioID, CAS-PEAL-R1 and ZJU datasets. The set EyeLSD, ePLBPH and Multi-TPLBP have a greater performance when compared against the state-of-the-art algorithms. The proposed approach is very stable under large range of eye appearances caused by expression, rotation, lighting, head pose, and occlusion. |
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EyeLSD a Robust Approach for Eye Localization and State Detection |
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| author2 |
dos Santos, Filipe Neves Boulebtateche, Brahim Bensaoula, Salah |
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2024-07-03T18:56:54.644Z |
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