A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation
Abstract Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Foytik, Jacob [verfasserIn] Asari, Vijayan K. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International journal of computer vision - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 101(2012), 2 vom: 19. Sept., Seite 270-287 |
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| Übergeordnetes Werk: |
volume:101 ; year:2012 ; number:2 ; day:19 ; month:09 ; pages:270-287 |
| Links: |
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| DOI / URN: |
10.1007/s11263-012-0567-y |
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| Katalog-ID: |
SPR018806694 |
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| 520 | |a Abstract Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing’04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods. | ||
| 650 | 4 | |a Head pose estimation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Piecewise linear manifold |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Coarse to fine |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Phase congruency |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Gabor filter |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Asari, Vijayan K. |e verfasserin |4 aut | |
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10.1007/s11263-012-0567-y doi (DE-627)SPR018806694 (SPR)s11263-012-0567-y-e DE-627 ger DE-627 rakwb eng 004 ASE 54.74 bkl Foytik, Jacob verfasserin aut A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing’04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods. Head pose estimation (dpeaa)DE-He213 Piecewise linear manifold (dpeaa)DE-He213 Coarse to fine (dpeaa)DE-He213 Phase congruency (dpeaa)DE-He213 Gabor filter (dpeaa)DE-He213 Asari, Vijayan K. verfasserin aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 101(2012), 2 vom: 19. Sept., Seite 270-287 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:101 year:2012 number:2 day:19 month:09 pages:270-287 https://dx.doi.org/10.1007/s11263-012-0567-y 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_2070 GBV_ILN_2086 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_2116 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 ASE AR 101 2012 2 19 09 270-287 |
| spelling |
10.1007/s11263-012-0567-y doi (DE-627)SPR018806694 (SPR)s11263-012-0567-y-e DE-627 ger DE-627 rakwb eng 004 ASE 54.74 bkl Foytik, Jacob verfasserin aut A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing’04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods. Head pose estimation (dpeaa)DE-He213 Piecewise linear manifold (dpeaa)DE-He213 Coarse to fine (dpeaa)DE-He213 Phase congruency (dpeaa)DE-He213 Gabor filter (dpeaa)DE-He213 Asari, Vijayan K. verfasserin aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 101(2012), 2 vom: 19. Sept., Seite 270-287 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:101 year:2012 number:2 day:19 month:09 pages:270-287 https://dx.doi.org/10.1007/s11263-012-0567-y 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_2070 GBV_ILN_2086 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_2116 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 ASE AR 101 2012 2 19 09 270-287 |
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10.1007/s11263-012-0567-y doi (DE-627)SPR018806694 (SPR)s11263-012-0567-y-e DE-627 ger DE-627 rakwb eng 004 ASE 54.74 bkl Foytik, Jacob verfasserin aut A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing’04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods. Head pose estimation (dpeaa)DE-He213 Piecewise linear manifold (dpeaa)DE-He213 Coarse to fine (dpeaa)DE-He213 Phase congruency (dpeaa)DE-He213 Gabor filter (dpeaa)DE-He213 Asari, Vijayan K. verfasserin aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 101(2012), 2 vom: 19. Sept., Seite 270-287 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:101 year:2012 number:2 day:19 month:09 pages:270-287 https://dx.doi.org/10.1007/s11263-012-0567-y 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_2070 GBV_ILN_2086 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_2116 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 ASE AR 101 2012 2 19 09 270-287 |
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10.1007/s11263-012-0567-y doi (DE-627)SPR018806694 (SPR)s11263-012-0567-y-e DE-627 ger DE-627 rakwb eng 004 ASE 54.74 bkl Foytik, Jacob verfasserin aut A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing’04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods. Head pose estimation (dpeaa)DE-He213 Piecewise linear manifold (dpeaa)DE-He213 Coarse to fine (dpeaa)DE-He213 Phase congruency (dpeaa)DE-He213 Gabor filter (dpeaa)DE-He213 Asari, Vijayan K. verfasserin aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 101(2012), 2 vom: 19. Sept., Seite 270-287 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:101 year:2012 number:2 day:19 month:09 pages:270-287 https://dx.doi.org/10.1007/s11263-012-0567-y 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_2070 GBV_ILN_2086 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_2116 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 ASE AR 101 2012 2 19 09 270-287 |
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10.1007/s11263-012-0567-y doi (DE-627)SPR018806694 (SPR)s11263-012-0567-y-e DE-627 ger DE-627 rakwb eng 004 ASE 54.74 bkl Foytik, Jacob verfasserin aut A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing’04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods. Head pose estimation (dpeaa)DE-He213 Piecewise linear manifold (dpeaa)DE-He213 Coarse to fine (dpeaa)DE-He213 Phase congruency (dpeaa)DE-He213 Gabor filter (dpeaa)DE-He213 Asari, Vijayan K. verfasserin aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 101(2012), 2 vom: 19. Sept., Seite 270-287 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:101 year:2012 number:2 day:19 month:09 pages:270-287 https://dx.doi.org/10.1007/s11263-012-0567-y 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_2070 GBV_ILN_2086 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_2116 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 ASE AR 101 2012 2 19 09 270-287 |
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Enthalten in International journal of computer vision 101(2012), 2 vom: 19. Sept., Seite 270-287 volume:101 year:2012 number:2 day:19 month:09 pages:270-287 |
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Foytik, Jacob |
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Foytik, Jacob ddc 004 bkl 54.74 misc Head pose estimation misc Piecewise linear manifold misc Coarse to fine misc Phase congruency misc Gabor filter A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation |
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004 ASE 54.74 bkl A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation Head pose estimation (dpeaa)DE-He213 Piecewise linear manifold (dpeaa)DE-He213 Coarse to fine (dpeaa)DE-He213 Phase congruency (dpeaa)DE-He213 Gabor filter (dpeaa)DE-He213 |
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ddc 004 bkl 54.74 misc Head pose estimation misc Piecewise linear manifold misc Coarse to fine misc Phase congruency misc Gabor filter |
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A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation |
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two-layer framework for piecewise linear manifold-based head pose estimation |
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A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation |
| abstract |
Abstract Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing’04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods. |
| abstractGer |
Abstract Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing’04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods. |
| abstract_unstemmed |
Abstract Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing’04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods. |
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| title_short |
A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation |
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https://dx.doi.org/10.1007/s11263-012-0567-y |
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Asari, Vijayan K. |
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Asari, Vijayan K. |
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| doi_str |
10.1007/s11263-012-0567-y |
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2024-07-03T22:20:51.715Z |
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| score |
7.3975716 |