Face sketch synthesis: a survey
Abstract Face sketch synthesis (FSS) has been widely applied to various computer vision tasks, such as criminal detection, information security, digital entertainment, etc. In the past several years, various FSS models with promising performance have been proposed. However, an in-depth understanding...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bi, Hongbo [verfasserIn] Liu, Ziqi [verfasserIn] Yang, Lina [verfasserIn] Wang, Kang [verfasserIn] Li, Ning [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 80(2021), 12 vom: 13. Feb., Seite 18007-18026 |
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| Übergeordnetes Werk: |
volume:80 ; year:2021 ; number:12 ; day:13 ; month:02 ; pages:18007-18026 |
| Links: |
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| DOI / URN: |
10.1007/s11042-020-10301-0 |
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| Katalog-ID: |
SPR044090838 |
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| 520 | |a Abstract Face sketch synthesis (FSS) has been widely applied to various computer vision tasks, such as criminal detection, information security, digital entertainment, etc. In the past several years, various FSS models with promising performance have been proposed. However, an in-depth understanding of these models in this topic remains lacking. The current survey: i) investigates few models; ii) classifies the models abstractly and monotonously; iii) lacks analysis of existing databases. iv) evaluates models in single evaluation metric. In this paper, we provide a comprehensive survey of the 50 state-of-the-art (SOTA) FSS models. Then we further describe the typical models objectively and analyze the results subjectively. Moreover, we divide these models into two main categories: traditional models and deep learning models. In addition, a novel classification is proposed: coefficient models and regression models. Finally, for the aforementioned problems, we discuss several challenges and highlight some directions of FSS for future research about new database and evaluation strategy. | ||
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| 650 | 4 | |a Traditional models |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Deep learning models |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Liu, Ziqi |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Lina |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Kang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ning |e verfasserin |4 aut | |
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10.1007/s11042-020-10301-0 doi (DE-627)SPR044090838 (DE-599)SPRs11042-020-10301-0-e (SPR)s11042-020-10301-0-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Bi, Hongbo verfasserin aut Face sketch synthesis: a survey 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Face sketch synthesis (FSS) has been widely applied to various computer vision tasks, such as criminal detection, information security, digital entertainment, etc. In the past several years, various FSS models with promising performance have been proposed. However, an in-depth understanding of these models in this topic remains lacking. The current survey: i) investigates few models; ii) classifies the models abstractly and monotonously; iii) lacks analysis of existing databases. iv) evaluates models in single evaluation metric. In this paper, we provide a comprehensive survey of the 50 state-of-the-art (SOTA) FSS models. Then we further describe the typical models objectively and analyze the results subjectively. Moreover, we divide these models into two main categories: traditional models and deep learning models. In addition, a novel classification is proposed: coefficient models and regression models. Finally, for the aforementioned problems, we discuss several challenges and highlight some directions of FSS for future research about new database and evaluation strategy. Face sketch synthesis (FSS) (dpeaa)DE-He213 Face sketch-photo synthesis (dpeaa)DE-He213 Face hallucination (dpeaa)DE-He213 Traditional models (dpeaa)DE-He213 Deep learning models (dpeaa)DE-He213 Liu, Ziqi verfasserin aut Yang, Lina verfasserin aut Wang, Kang verfasserin aut Li, Ning verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 12 vom: 13. Feb., Seite 18007-18026 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:12 day:13 month:02 pages:18007-18026 https://dx.doi.org/10.1007/s11042-020-10301-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 80 2021 12 13 02 18007-18026 |
| spelling |
10.1007/s11042-020-10301-0 doi (DE-627)SPR044090838 (DE-599)SPRs11042-020-10301-0-e (SPR)s11042-020-10301-0-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Bi, Hongbo verfasserin aut Face sketch synthesis: a survey 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Face sketch synthesis (FSS) has been widely applied to various computer vision tasks, such as criminal detection, information security, digital entertainment, etc. In the past several years, various FSS models with promising performance have been proposed. However, an in-depth understanding of these models in this topic remains lacking. The current survey: i) investigates few models; ii) classifies the models abstractly and monotonously; iii) lacks analysis of existing databases. iv) evaluates models in single evaluation metric. In this paper, we provide a comprehensive survey of the 50 state-of-the-art (SOTA) FSS models. Then we further describe the typical models objectively and analyze the results subjectively. Moreover, we divide these models into two main categories: traditional models and deep learning models. In addition, a novel classification is proposed: coefficient models and regression models. Finally, for the aforementioned problems, we discuss several challenges and highlight some directions of FSS for future research about new database and evaluation strategy. Face sketch synthesis (FSS) (dpeaa)DE-He213 Face sketch-photo synthesis (dpeaa)DE-He213 Face hallucination (dpeaa)DE-He213 Traditional models (dpeaa)DE-He213 Deep learning models (dpeaa)DE-He213 Liu, Ziqi verfasserin aut Yang, Lina verfasserin aut Wang, Kang verfasserin aut Li, Ning verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 12 vom: 13. Feb., Seite 18007-18026 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:12 day:13 month:02 pages:18007-18026 https://dx.doi.org/10.1007/s11042-020-10301-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 80 2021 12 13 02 18007-18026 |
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10.1007/s11042-020-10301-0 doi (DE-627)SPR044090838 (DE-599)SPRs11042-020-10301-0-e (SPR)s11042-020-10301-0-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Bi, Hongbo verfasserin aut Face sketch synthesis: a survey 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Face sketch synthesis (FSS) has been widely applied to various computer vision tasks, such as criminal detection, information security, digital entertainment, etc. In the past several years, various FSS models with promising performance have been proposed. However, an in-depth understanding of these models in this topic remains lacking. The current survey: i) investigates few models; ii) classifies the models abstractly and monotonously; iii) lacks analysis of existing databases. iv) evaluates models in single evaluation metric. In this paper, we provide a comprehensive survey of the 50 state-of-the-art (SOTA) FSS models. Then we further describe the typical models objectively and analyze the results subjectively. Moreover, we divide these models into two main categories: traditional models and deep learning models. In addition, a novel classification is proposed: coefficient models and regression models. Finally, for the aforementioned problems, we discuss several challenges and highlight some directions of FSS for future research about new database and evaluation strategy. Face sketch synthesis (FSS) (dpeaa)DE-He213 Face sketch-photo synthesis (dpeaa)DE-He213 Face hallucination (dpeaa)DE-He213 Traditional models (dpeaa)DE-He213 Deep learning models (dpeaa)DE-He213 Liu, Ziqi verfasserin aut Yang, Lina verfasserin aut Wang, Kang verfasserin aut Li, Ning verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 12 vom: 13. Feb., Seite 18007-18026 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:12 day:13 month:02 pages:18007-18026 https://dx.doi.org/10.1007/s11042-020-10301-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 80 2021 12 13 02 18007-18026 |
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10.1007/s11042-020-10301-0 doi (DE-627)SPR044090838 (DE-599)SPRs11042-020-10301-0-e (SPR)s11042-020-10301-0-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Bi, Hongbo verfasserin aut Face sketch synthesis: a survey 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Face sketch synthesis (FSS) has been widely applied to various computer vision tasks, such as criminal detection, information security, digital entertainment, etc. In the past several years, various FSS models with promising performance have been proposed. However, an in-depth understanding of these models in this topic remains lacking. The current survey: i) investigates few models; ii) classifies the models abstractly and monotonously; iii) lacks analysis of existing databases. iv) evaluates models in single evaluation metric. In this paper, we provide a comprehensive survey of the 50 state-of-the-art (SOTA) FSS models. Then we further describe the typical models objectively and analyze the results subjectively. Moreover, we divide these models into two main categories: traditional models and deep learning models. In addition, a novel classification is proposed: coefficient models and regression models. Finally, for the aforementioned problems, we discuss several challenges and highlight some directions of FSS for future research about new database and evaluation strategy. Face sketch synthesis (FSS) (dpeaa)DE-He213 Face sketch-photo synthesis (dpeaa)DE-He213 Face hallucination (dpeaa)DE-He213 Traditional models (dpeaa)DE-He213 Deep learning models (dpeaa)DE-He213 Liu, Ziqi verfasserin aut Yang, Lina verfasserin aut Wang, Kang verfasserin aut Li, Ning verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 12 vom: 13. Feb., Seite 18007-18026 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:12 day:13 month:02 pages:18007-18026 https://dx.doi.org/10.1007/s11042-020-10301-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 80 2021 12 13 02 18007-18026 |
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Face sketch synthesis (FSS) Face sketch-photo synthesis Face hallucination Traditional models Deep learning models |
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Bi, Hongbo @@aut@@ Liu, Ziqi @@aut@@ Yang, Lina @@aut@@ Wang, Kang @@aut@@ Li, Ning @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR044090838</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111024821.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210521s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-020-10301-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR044090838</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs11042-020-10301-0-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11042-020-10301-0-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.87</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bi, Hongbo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Face sketch synthesis: a survey</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Face sketch synthesis (FSS) has been widely applied to various computer vision tasks, such as criminal detection, information security, digital entertainment, etc. In the past several years, various FSS models with promising performance have been proposed. However, an in-depth understanding of these models in this topic remains lacking. The current survey: i) investigates few models; ii) classifies the models abstractly and monotonously; iii) lacks analysis of existing databases. iv) evaluates models in single evaluation metric. In this paper, we provide a comprehensive survey of the 50 state-of-the-art (SOTA) FSS models. Then we further describe the typical models objectively and analyze the results subjectively. Moreover, we divide these models into two main categories: traditional models and deep learning models. In addition, a novel classification is proposed: coefficient models and regression models. 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Bi, Hongbo |
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Face sketch synthesis: a survey |
| abstract |
Abstract Face sketch synthesis (FSS) has been widely applied to various computer vision tasks, such as criminal detection, information security, digital entertainment, etc. In the past several years, various FSS models with promising performance have been proposed. However, an in-depth understanding of these models in this topic remains lacking. The current survey: i) investigates few models; ii) classifies the models abstractly and monotonously; iii) lacks analysis of existing databases. iv) evaluates models in single evaluation metric. In this paper, we provide a comprehensive survey of the 50 state-of-the-art (SOTA) FSS models. Then we further describe the typical models objectively and analyze the results subjectively. Moreover, we divide these models into two main categories: traditional models and deep learning models. In addition, a novel classification is proposed: coefficient models and regression models. Finally, for the aforementioned problems, we discuss several challenges and highlight some directions of FSS for future research about new database and evaluation strategy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstractGer |
Abstract Face sketch synthesis (FSS) has been widely applied to various computer vision tasks, such as criminal detection, information security, digital entertainment, etc. In the past several years, various FSS models with promising performance have been proposed. However, an in-depth understanding of these models in this topic remains lacking. The current survey: i) investigates few models; ii) classifies the models abstractly and monotonously; iii) lacks analysis of existing databases. iv) evaluates models in single evaluation metric. In this paper, we provide a comprehensive survey of the 50 state-of-the-art (SOTA) FSS models. Then we further describe the typical models objectively and analyze the results subjectively. Moreover, we divide these models into two main categories: traditional models and deep learning models. In addition, a novel classification is proposed: coefficient models and regression models. Finally, for the aforementioned problems, we discuss several challenges and highlight some directions of FSS for future research about new database and evaluation strategy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Face sketch synthesis (FSS) has been widely applied to various computer vision tasks, such as criminal detection, information security, digital entertainment, etc. In the past several years, various FSS models with promising performance have been proposed. However, an in-depth understanding of these models in this topic remains lacking. The current survey: i) investigates few models; ii) classifies the models abstractly and monotonously; iii) lacks analysis of existing databases. iv) evaluates models in single evaluation metric. In this paper, we provide a comprehensive survey of the 50 state-of-the-art (SOTA) FSS models. Then we further describe the typical models objectively and analyze the results subjectively. Moreover, we divide these models into two main categories: traditional models and deep learning models. In addition, a novel classification is proposed: coefficient models and regression models. Finally, for the aforementioned problems, we discuss several challenges and highlight some directions of FSS for future research about new database and evaluation strategy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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| title_short |
Face sketch synthesis: a survey |
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https://dx.doi.org/10.1007/s11042-020-10301-0 |
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Liu, Ziqi Yang, Lina Wang, Kang Li, Ning |
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Liu, Ziqi Yang, Lina Wang, Kang Li, Ning |
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| doi_str |
10.1007/s11042-020-10301-0 |
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2024-07-03T22:50:52.972Z |
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|
| score |
7.401017 |