Colour face recognition using fuzzy quaternion-based discriminant analysis
Abstract Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bao, Shuzhe [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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| Anmerkung: |
© Springer-Verlag GmbH Germany 2017 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of machine learning and cybernetics - Heidelberg : Springer, 2010, 10(2017), 2 vom: 31. Aug., Seite 385-395 |
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| Übergeordnetes Werk: |
volume:10 ; year:2017 ; number:2 ; day:31 ; month:08 ; pages:385-395 |
| Links: |
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| DOI / URN: |
10.1007/s13042-017-0722-4 |
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SPR02960673X |
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| 520 | |a Abstract Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method. | ||
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| 650 | 4 | |a Fuzzy parameterized discriminant analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Colour image recognition |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Song, Xiaoning |4 aut | |
| 700 | 1 | |a Hu, Guosheng |4 aut | |
| 700 | 1 | |a Yang, Xibei |4 aut | |
| 700 | 1 | |a Wang, Chunli |4 aut | |
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10.1007/s13042-017-0722-4 doi (DE-627)SPR02960673X (SPR)s13042-017-0722-4-e DE-627 ger DE-627 rakwb eng Bao, Shuzhe verfasserin aut Colour face recognition using fuzzy quaternion-based discriminant analysis 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method. Quaternion-based vector (dpeaa)DE-He213 Fuzzy parameterized discriminant analysis (dpeaa)DE-He213 Colour image recognition (dpeaa)DE-He213 Song, Xiaoning aut Hu, Guosheng aut Yang, Xibei aut Wang, Chunli aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 10(2017), 2 vom: 31. Aug., Seite 385-395 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:10 year:2017 number:2 day:31 month:08 pages:385-395 https://dx.doi.org/10.1007/s13042-017-0722-4 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_65 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 10 2017 2 31 08 385-395 |
| spelling |
10.1007/s13042-017-0722-4 doi (DE-627)SPR02960673X (SPR)s13042-017-0722-4-e DE-627 ger DE-627 rakwb eng Bao, Shuzhe verfasserin aut Colour face recognition using fuzzy quaternion-based discriminant analysis 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method. Quaternion-based vector (dpeaa)DE-He213 Fuzzy parameterized discriminant analysis (dpeaa)DE-He213 Colour image recognition (dpeaa)DE-He213 Song, Xiaoning aut Hu, Guosheng aut Yang, Xibei aut Wang, Chunli aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 10(2017), 2 vom: 31. Aug., Seite 385-395 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:10 year:2017 number:2 day:31 month:08 pages:385-395 https://dx.doi.org/10.1007/s13042-017-0722-4 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_65 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 10 2017 2 31 08 385-395 |
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10.1007/s13042-017-0722-4 doi (DE-627)SPR02960673X (SPR)s13042-017-0722-4-e DE-627 ger DE-627 rakwb eng Bao, Shuzhe verfasserin aut Colour face recognition using fuzzy quaternion-based discriminant analysis 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method. Quaternion-based vector (dpeaa)DE-He213 Fuzzy parameterized discriminant analysis (dpeaa)DE-He213 Colour image recognition (dpeaa)DE-He213 Song, Xiaoning aut Hu, Guosheng aut Yang, Xibei aut Wang, Chunli aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 10(2017), 2 vom: 31. Aug., Seite 385-395 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:10 year:2017 number:2 day:31 month:08 pages:385-395 https://dx.doi.org/10.1007/s13042-017-0722-4 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_65 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 10 2017 2 31 08 385-395 |
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10.1007/s13042-017-0722-4 doi (DE-627)SPR02960673X (SPR)s13042-017-0722-4-e DE-627 ger DE-627 rakwb eng Bao, Shuzhe verfasserin aut Colour face recognition using fuzzy quaternion-based discriminant analysis 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method. Quaternion-based vector (dpeaa)DE-He213 Fuzzy parameterized discriminant analysis (dpeaa)DE-He213 Colour image recognition (dpeaa)DE-He213 Song, Xiaoning aut Hu, Guosheng aut Yang, Xibei aut Wang, Chunli aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 10(2017), 2 vom: 31. Aug., Seite 385-395 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:10 year:2017 number:2 day:31 month:08 pages:385-395 https://dx.doi.org/10.1007/s13042-017-0722-4 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_65 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 10 2017 2 31 08 385-395 |
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10.1007/s13042-017-0722-4 doi (DE-627)SPR02960673X (SPR)s13042-017-0722-4-e DE-627 ger DE-627 rakwb eng Bao, Shuzhe verfasserin aut Colour face recognition using fuzzy quaternion-based discriminant analysis 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method. Quaternion-based vector (dpeaa)DE-He213 Fuzzy parameterized discriminant analysis (dpeaa)DE-He213 Colour image recognition (dpeaa)DE-He213 Song, Xiaoning aut Hu, Guosheng aut Yang, Xibei aut Wang, Chunli aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 10(2017), 2 vom: 31. Aug., Seite 385-395 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:10 year:2017 number:2 day:31 month:08 pages:385-395 https://dx.doi.org/10.1007/s13042-017-0722-4 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_65 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 10 2017 2 31 08 385-395 |
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Bao, Shuzhe @@aut@@ Song, Xiaoning @@aut@@ Hu, Guosheng @@aut@@ Yang, Xibei @@aut@@ Wang, Chunli @@aut@@ |
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Bao, Shuzhe |
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Bao, Shuzhe misc Quaternion-based vector misc Fuzzy parameterized discriminant analysis misc Colour image recognition Colour face recognition using fuzzy quaternion-based discriminant analysis |
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Colour face recognition using fuzzy quaternion-based discriminant analysis Quaternion-based vector (dpeaa)DE-He213 Fuzzy parameterized discriminant analysis (dpeaa)DE-He213 Colour image recognition (dpeaa)DE-He213 |
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misc Quaternion-based vector misc Fuzzy parameterized discriminant analysis misc Colour image recognition |
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Colour face recognition using fuzzy quaternion-based discriminant analysis |
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Colour face recognition using fuzzy quaternion-based discriminant analysis |
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International journal of machine learning and cybernetics |
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Bao, Shuzhe Song, Xiaoning Hu, Guosheng Yang, Xibei Wang, Chunli |
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10.1007/s13042-017-0722-4 |
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colour face recognition using fuzzy quaternion-based discriminant analysis |
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Colour face recognition using fuzzy quaternion-based discriminant analysis |
| abstract |
Abstract Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method. © Springer-Verlag GmbH Germany 2017 |
| abstractGer |
Abstract Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method. © Springer-Verlag GmbH Germany 2017 |
| abstract_unstemmed |
Abstract Colour information has been shown to be effective in improving object recognition performance. In this paper, we propose a novel quaternion-based colour model with enhanced fuzzy parameterized discriminant analysis to perform face recognition. The proposed method represents and classifies colour images by using an improved fuzzy quaternion-based discriminant (FQD) model, which is effective for colour image feature representation, extraction and classification. More specifically, each pixel in a colour image is first assigned a quaternion number, and a quaternion-based vector is then generated to represent this colour image. Second, an enhanced fuzzy parameterized discriminant analysis is used to transform the original quaternion-based vector into an optimized discriminant quaternion space. Third, colour face recognition is conducted by interpreting the colour feature model as fuzzy weight measurement in a quaternion discriminant analysis. The main contribution of this paper is that it provides a novel fuzzy supervised learning approach to reconstruct the quaternion-based discriminant vector space, thus showing the importance of the FQD characteristic from colour spaces for colour-image-based face recognition. Experimental results on the AR and Georgia Tech colour datasets demonstrate the effectiveness of the proposed method. © Springer-Verlag GmbH Germany 2017 |
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Colour face recognition using fuzzy quaternion-based discriminant analysis |
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https://dx.doi.org/10.1007/s13042-017-0722-4 |
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Song, Xiaoning Hu, Guosheng Yang, Xibei Wang, Chunli |
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Song, Xiaoning Hu, Guosheng Yang, Xibei Wang, Chunli |
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| doi_str |
10.1007/s13042-017-0722-4 |
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2024-07-04T01:40:31.351Z |
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