Subspace cross representation measure for robust face recognition with few samples

Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the...
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Autor*in:	Zhang, Jian [verfasserIn] Qin, Xin [verfasserIn] Xiao, Yuchen [verfasserIn] Fei, Rong [verfasserIn] Zang, Qiyan [verfasserIn] Xu, Shuai [verfasserIn] Bo, Liling [verfasserIn] Li, Hongran [verfasserIn] Zhang, Heng [verfasserIn] Zhong, Zhaoman [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Regression analysis Image similarity measure Robust face recognition Subspace cross representation Pattern recognition

Übergeordnetes Werk:	Enthalten in: Computers & electrical engineering - Amsterdam [u.a.] : Elsevier Science, 1973, 102
Übergeordnetes Werk:	volume:102

DOI / URN:	10.1016/j.compeleceng.2022.108162

Katalog-ID:	ELV008743045

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520			\|a Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition.
650		4	\|a Regression analysis
650		4	\|a Image similarity measure
650		4	\|a Robust face recognition
650		4	\|a Subspace cross representation
650		4	\|a Pattern recognition
700	1		\|a Qin, Xin \|e verfasserin \|4 aut
700	1		\|a Xiao, Yuchen \|e verfasserin \|4 aut
700	1		\|a Fei, Rong \|e verfasserin \|4 aut
700	1		\|a Zang, Qiyan \|e verfasserin \|4 aut
700	1		\|a Xu, Shuai \|e verfasserin \|4 aut
700	1		\|a Bo, Liling \|e verfasserin \|4 aut
700	1		\|a Li, Hongran \|e verfasserin \|4 aut
700	1		\|a Zhang, Heng \|e verfasserin \|4 aut
700	1		\|a Zhong, Zhaoman \|e verfasserin \|4 aut
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publishDate	2022
allfields	10.1016/j.compeleceng.2022.108162 doi (DE-627)ELV008743045 (ELSEVIER)S0045-7906(22)00407-4 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Zhang, Jian verfasserin aut Subspace cross representation measure for robust face recognition with few samples 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition. Regression analysis Image similarity measure Robust face recognition Subspace cross representation Pattern recognition Qin, Xin verfasserin aut Xiao, Yuchen verfasserin aut Fei, Rong verfasserin aut Zang, Qiyan verfasserin aut Xu, Shuai verfasserin aut Bo, Liling verfasserin aut Li, Hongran verfasserin aut Zhang, Heng verfasserin aut Zhong, Zhaoman verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 102 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:102 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 102
spelling	10.1016/j.compeleceng.2022.108162 doi (DE-627)ELV008743045 (ELSEVIER)S0045-7906(22)00407-4 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Zhang, Jian verfasserin aut Subspace cross representation measure for robust face recognition with few samples 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition. Regression analysis Image similarity measure Robust face recognition Subspace cross representation Pattern recognition Qin, Xin verfasserin aut Xiao, Yuchen verfasserin aut Fei, Rong verfasserin aut Zang, Qiyan verfasserin aut Xu, Shuai verfasserin aut Bo, Liling verfasserin aut Li, Hongran verfasserin aut Zhang, Heng verfasserin aut Zhong, Zhaoman verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 102 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:102 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 102
allfields_unstemmed	10.1016/j.compeleceng.2022.108162 doi (DE-627)ELV008743045 (ELSEVIER)S0045-7906(22)00407-4 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Zhang, Jian verfasserin aut Subspace cross representation measure for robust face recognition with few samples 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition. Regression analysis Image similarity measure Robust face recognition Subspace cross representation Pattern recognition Qin, Xin verfasserin aut Xiao, Yuchen verfasserin aut Fei, Rong verfasserin aut Zang, Qiyan verfasserin aut Xu, Shuai verfasserin aut Bo, Liling verfasserin aut Li, Hongran verfasserin aut Zhang, Heng verfasserin aut Zhong, Zhaoman verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 102 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:102 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 102
allfieldsGer	10.1016/j.compeleceng.2022.108162 doi (DE-627)ELV008743045 (ELSEVIER)S0045-7906(22)00407-4 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Zhang, Jian verfasserin aut Subspace cross representation measure for robust face recognition with few samples 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition. Regression analysis Image similarity measure Robust face recognition Subspace cross representation Pattern recognition Qin, Xin verfasserin aut Xiao, Yuchen verfasserin aut Fei, Rong verfasserin aut Zang, Qiyan verfasserin aut Xu, Shuai verfasserin aut Bo, Liling verfasserin aut Li, Hongran verfasserin aut Zhang, Heng verfasserin aut Zhong, Zhaoman verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 102 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:102 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 102
allfieldsSound	10.1016/j.compeleceng.2022.108162 doi (DE-627)ELV008743045 (ELSEVIER)S0045-7906(22)00407-4 DE-627 ger DE-627 rda eng 620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Zhang, Jian verfasserin aut Subspace cross representation measure for robust face recognition with few samples 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition. Regression analysis Image similarity measure Robust face recognition Subspace cross representation Pattern recognition Qin, Xin verfasserin aut Xiao, Yuchen verfasserin aut Fei, Rong verfasserin aut Zang, Qiyan verfasserin aut Xu, Shuai verfasserin aut Bo, Liling verfasserin aut Li, Hongran verfasserin aut Zhang, Heng verfasserin aut Zhong, Zhaoman verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 102 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:102 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.00 Elektrotechnik: Allgemeines 35.06 Computeranwendungen Chemie 54.00 Informatik: Allgemeines AR 102
language	English
source	Enthalten in Computers & electrical engineering 102 volume:102
sourceStr	Enthalten in Computers & electrical engineering 102 volume:102
format_phy_str_mv	Article
bklname	Elektrotechnik: Allgemeines Computeranwendungen Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Regression analysis Image similarity measure Robust face recognition Subspace cross representation Pattern recognition
dewey-raw	620
isfreeaccess_bool	false
container_title	Computers & electrical engineering
authorswithroles_txt_mv	Zhang, Jian @@aut@@ Qin, Xin @@aut@@ Xiao, Yuchen @@aut@@ Fei, Rong @@aut@@ Zang, Qiyan @@aut@@ Xu, Shuai @@aut@@ Bo, Liling @@aut@@ Li, Hongran @@aut@@ Zhang, Heng @@aut@@ Zhong, Zhaoman @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	306715872
dewey-sort	3620
id	ELV008743045
language_de	englisch
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author	Zhang, Jian
spellingShingle	Zhang, Jian ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc Regression analysis misc Image similarity measure misc Robust face recognition misc Subspace cross representation misc Pattern recognition Subspace cross representation measure for robust face recognition with few samples
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topic_title	620 DE-600 53.00 bkl 35.06 bkl 54.00 bkl Subspace cross representation measure for robust face recognition with few samples Regression analysis Image similarity measure Robust face recognition Subspace cross representation Pattern recognition
topic	ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc Regression analysis misc Image similarity measure misc Robust face recognition misc Subspace cross representation misc Pattern recognition
topic_unstemmed	ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc Regression analysis misc Image similarity measure misc Robust face recognition misc Subspace cross representation misc Pattern recognition
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title	Subspace cross representation measure for robust face recognition with few samples
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title_full	Subspace cross representation measure for robust face recognition with few samples
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author_browse	Zhang, Jian Qin, Xin Xiao, Yuchen Fei, Rong Zang, Qiyan Xu, Shuai Bo, Liling Li, Hongran Zhang, Heng Zhong, Zhaoman
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title_sort	subspace cross representation measure for robust face recognition with few samples
title_auth	Subspace cross representation measure for robust face recognition with few samples
abstract	Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition.
abstractGer	Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition.
abstract_unstemmed	Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition.
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title_short	Subspace cross representation measure for robust face recognition with few samples
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author2	Qin, Xin Xiao, Yuchen Fei, Rong Zang, Qiyan Xu, Shuai Bo, Liling Li, Hongran Zhang, Heng Zhong, Zhaoman
author2Str	Qin, Xin Xiao, Yuchen Fei, Rong Zang, Qiyan Xu, Shuai Bo, Liling Li, Hongran Zhang, Heng Zhong, Zhaoman
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up_date	2024-07-06T20:45:07.961Z
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Subspace cross representation measure for robust face recognition with few samples

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