Does face restoration improve face verification?
Abstract Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sobiecki, André [verfasserIn] van Dijk, Julius [verfasserIn] Folkertsma, Hidde [verfasserIn] Telea, Alexandru [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), 21-23 vom: 09. Aug., Seite 32863-32883 |
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| Übergeordnetes Werk: |
volume:80 ; year:2021 ; number:21-23 ; day:09 ; month:08 ; pages:32863-32883 |
| Links: |
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| DOI / URN: |
10.1007/s11042-021-11167-6 |
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SPR045246513 |
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| 520 | |a Abstract Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this paper, we survey the capability of existing face restoration and verification methods, with the aim of understanding how useful face restoration methods are for face verification. We propose a qualitative and quantitative comparison benchmark, and apply it on eight methods for face restoration and six methods for face verification, on several real-world low-quality images from a surveillance context, and outline observed advantages and limitations. Experiments shows that each restoration method can affect each face verification method differently, with fewer than the half of face restoration methods helping face verification. Interestingly, some face restoration methods with less good qualitative evaluation helped face verification the most. Experiments also show that face verification works less good if the resolution decreases. | ||
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| 650 | 4 | |a Face restoration |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Image processing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Pattern recognition |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Folkertsma, Hidde |e verfasserin |4 aut | |
| 700 | 1 | |a Telea, Alexandru |e verfasserin |4 aut | |
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10.1007/s11042-021-11167-6 doi (DE-627)SPR045246513 (SPR)s11042-021-11167-6-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Sobiecki, André verfasserin aut Does face restoration improve face verification? 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 Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this paper, we survey the capability of existing face restoration and verification methods, with the aim of understanding how useful face restoration methods are for face verification. We propose a qualitative and quantitative comparison benchmark, and apply it on eight methods for face restoration and six methods for face verification, on several real-world low-quality images from a surveillance context, and outline observed advantages and limitations. Experiments shows that each restoration method can affect each face verification method differently, with fewer than the half of face restoration methods helping face verification. Interestingly, some face restoration methods with less good qualitative evaluation helped face verification the most. Experiments also show that face verification works less good if the resolution decreases. Face verification (dpeaa)DE-He213 Face restoration (dpeaa)DE-He213 Face recognition (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 van Dijk, Julius verfasserin aut Folkertsma, Hidde verfasserin aut Telea, Alexandru verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 21-23 vom: 09. Aug., Seite 32863-32883 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:21-23 day:09 month:08 pages:32863-32883 https://dx.doi.org/10.1007/s11042-021-11167-6 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 21-23 09 08 32863-32883 |
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10.1007/s11042-021-11167-6 doi (DE-627)SPR045246513 (SPR)s11042-021-11167-6-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Sobiecki, André verfasserin aut Does face restoration improve face verification? 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 Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this paper, we survey the capability of existing face restoration and verification methods, with the aim of understanding how useful face restoration methods are for face verification. We propose a qualitative and quantitative comparison benchmark, and apply it on eight methods for face restoration and six methods for face verification, on several real-world low-quality images from a surveillance context, and outline observed advantages and limitations. Experiments shows that each restoration method can affect each face verification method differently, with fewer than the half of face restoration methods helping face verification. Interestingly, some face restoration methods with less good qualitative evaluation helped face verification the most. Experiments also show that face verification works less good if the resolution decreases. Face verification (dpeaa)DE-He213 Face restoration (dpeaa)DE-He213 Face recognition (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 van Dijk, Julius verfasserin aut Folkertsma, Hidde verfasserin aut Telea, Alexandru verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 21-23 vom: 09. Aug., Seite 32863-32883 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:21-23 day:09 month:08 pages:32863-32883 https://dx.doi.org/10.1007/s11042-021-11167-6 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 21-23 09 08 32863-32883 |
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10.1007/s11042-021-11167-6 doi (DE-627)SPR045246513 (SPR)s11042-021-11167-6-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Sobiecki, André verfasserin aut Does face restoration improve face verification? 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 Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this paper, we survey the capability of existing face restoration and verification methods, with the aim of understanding how useful face restoration methods are for face verification. We propose a qualitative and quantitative comparison benchmark, and apply it on eight methods for face restoration and six methods for face verification, on several real-world low-quality images from a surveillance context, and outline observed advantages and limitations. Experiments shows that each restoration method can affect each face verification method differently, with fewer than the half of face restoration methods helping face verification. Interestingly, some face restoration methods with less good qualitative evaluation helped face verification the most. Experiments also show that face verification works less good if the resolution decreases. Face verification (dpeaa)DE-He213 Face restoration (dpeaa)DE-He213 Face recognition (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 van Dijk, Julius verfasserin aut Folkertsma, Hidde verfasserin aut Telea, Alexandru verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 21-23 vom: 09. Aug., Seite 32863-32883 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:21-23 day:09 month:08 pages:32863-32883 https://dx.doi.org/10.1007/s11042-021-11167-6 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 21-23 09 08 32863-32883 |
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10.1007/s11042-021-11167-6 doi (DE-627)SPR045246513 (SPR)s11042-021-11167-6-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Sobiecki, André verfasserin aut Does face restoration improve face verification? 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 Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this paper, we survey the capability of existing face restoration and verification methods, with the aim of understanding how useful face restoration methods are for face verification. We propose a qualitative and quantitative comparison benchmark, and apply it on eight methods for face restoration and six methods for face verification, on several real-world low-quality images from a surveillance context, and outline observed advantages and limitations. Experiments shows that each restoration method can affect each face verification method differently, with fewer than the half of face restoration methods helping face verification. Interestingly, some face restoration methods with less good qualitative evaluation helped face verification the most. Experiments also show that face verification works less good if the resolution decreases. Face verification (dpeaa)DE-He213 Face restoration (dpeaa)DE-He213 Face recognition (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 van Dijk, Julius verfasserin aut Folkertsma, Hidde verfasserin aut Telea, Alexandru verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2021), 21-23 vom: 09. Aug., Seite 32863-32883 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2021 number:21-23 day:09 month:08 pages:32863-32883 https://dx.doi.org/10.1007/s11042-021-11167-6 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 21-23 09 08 32863-32883 |
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Sobiecki, André |
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Sobiecki, André ddc 070 bkl 54.87 misc Face verification misc Face restoration misc Face recognition misc Image processing misc Machine learning misc Pattern recognition Does face restoration improve face verification? |
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070 004 ASE 54.87 bkl Does face restoration improve face verification? Face verification (dpeaa)DE-He213 Face restoration (dpeaa)DE-He213 Face recognition (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 |
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ddc 070 bkl 54.87 misc Face verification misc Face restoration misc Face recognition misc Image processing misc Machine learning misc Pattern recognition |
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does face restoration improve face verification? |
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Does face restoration improve face verification? |
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Abstract Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this paper, we survey the capability of existing face restoration and verification methods, with the aim of understanding how useful face restoration methods are for face verification. We propose a qualitative and quantitative comparison benchmark, and apply it on eight methods for face restoration and six methods for face verification, on several real-world low-quality images from a surveillance context, and outline observed advantages and limitations. Experiments shows that each restoration method can affect each face verification method differently, with fewer than the half of face restoration methods helping face verification. Interestingly, some face restoration methods with less good qualitative evaluation helped face verification the most. Experiments also show that face verification works less good if the resolution decreases. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this paper, we survey the capability of existing face restoration and verification methods, with the aim of understanding how useful face restoration methods are for face verification. We propose a qualitative and quantitative comparison benchmark, and apply it on eight methods for face restoration and six methods for face verification, on several real-world low-quality images from a surveillance context, and outline observed advantages and limitations. Experiments shows that each restoration method can affect each face verification method differently, with fewer than the half of face restoration methods helping face verification. Interestingly, some face restoration methods with less good qualitative evaluation helped face verification the most. Experiments also show that face verification works less good if the resolution decreases. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Methods for face verification works reasonably well on face images with standardized (frontal) face positions and good spatial resolution. However such methods have significant challenges on poor resolution images, poor lighting conditions and not standard (frontal) face positions. In this paper, we survey the capability of existing face restoration and verification methods, with the aim of understanding how useful face restoration methods are for face verification. We propose a qualitative and quantitative comparison benchmark, and apply it on eight methods for face restoration and six methods for face verification, on several real-world low-quality images from a surveillance context, and outline observed advantages and limitations. Experiments shows that each restoration method can affect each face verification method differently, with fewer than the half of face restoration methods helping face verification. Interestingly, some face restoration methods with less good qualitative evaluation helped face verification the most. Experiments also show that face verification works less good if the resolution decreases. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| title_short |
Does face restoration improve face verification? |
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https://dx.doi.org/10.1007/s11042-021-11167-6 |
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| author2 |
van Dijk, Julius Folkertsma, Hidde Telea, Alexandru |
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van Dijk, Julius Folkertsma, Hidde Telea, Alexandru |
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10.1007/s11042-021-11167-6 |
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2024-07-03T14:45:01.118Z |
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| score |
7.4014273 |