The Do’s and Don’ts for Increasing the Accuracy of Face Recognition on VGGFace2 Dataset
Abstract In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Erbir, Muhammed Ali [verfasserIn] Ünver, Halil Murat [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: |
© King Fahd University of Petroleum & Minerals 2021 |
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| Übergeordnetes Werk: |
Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 46(2021), 9 vom: 11. Mai, Seite 8901-8911 |
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| Übergeordnetes Werk: |
volume:46 ; year:2021 ; number:9 ; day:11 ; month:05 ; pages:8901-8911 |
| Links: |
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| DOI / URN: |
10.1007/s13369-021-05693-6 |
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| Katalog-ID: |
SPR04495607X |
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| 520 | |a Abstract In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems. | ||
| 650 | 4 | |a Face recognition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Face verification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Biometric verification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image processing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a VGGFace2 dataset |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ünver, Halil Murat |e verfasserin |4 aut | |
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10.1007/s13369-021-05693-6 doi (DE-627)SPR04495607X (SPR)s13369-021-05693-6-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Erbir, Muhammed Ali verfasserin aut The Do’s and Don’ts for Increasing the Accuracy of Face Recognition on VGGFace2 Dataset 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2021 Abstract In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems. Face recognition (dpeaa)DE-He213 Face verification (dpeaa)DE-He213 Biometric verification (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 VGGFace2 dataset (dpeaa)DE-He213 Ünver, Halil Murat verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 46(2021), 9 vom: 11. Mai, Seite 8901-8911 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:46 year:2021 number:9 day:11 month:05 pages:8901-8911 https://dx.doi.org/10.1007/s13369-021-05693-6 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_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_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_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 31.00 ASE AR 46 2021 9 11 05 8901-8911 |
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10.1007/s13369-021-05693-6 doi (DE-627)SPR04495607X (SPR)s13369-021-05693-6-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Erbir, Muhammed Ali verfasserin aut The Do’s and Don’ts for Increasing the Accuracy of Face Recognition on VGGFace2 Dataset 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2021 Abstract In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems. Face recognition (dpeaa)DE-He213 Face verification (dpeaa)DE-He213 Biometric verification (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 VGGFace2 dataset (dpeaa)DE-He213 Ünver, Halil Murat verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 46(2021), 9 vom: 11. Mai, Seite 8901-8911 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:46 year:2021 number:9 day:11 month:05 pages:8901-8911 https://dx.doi.org/10.1007/s13369-021-05693-6 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_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_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_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 31.00 ASE AR 46 2021 9 11 05 8901-8911 |
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10.1007/s13369-021-05693-6 doi (DE-627)SPR04495607X (SPR)s13369-021-05693-6-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Erbir, Muhammed Ali verfasserin aut The Do’s and Don’ts for Increasing the Accuracy of Face Recognition on VGGFace2 Dataset 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2021 Abstract In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems. Face recognition (dpeaa)DE-He213 Face verification (dpeaa)DE-He213 Biometric verification (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 VGGFace2 dataset (dpeaa)DE-He213 Ünver, Halil Murat verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 46(2021), 9 vom: 11. Mai, Seite 8901-8911 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:46 year:2021 number:9 day:11 month:05 pages:8901-8911 https://dx.doi.org/10.1007/s13369-021-05693-6 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_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_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_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 31.00 ASE AR 46 2021 9 11 05 8901-8911 |
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10.1007/s13369-021-05693-6 doi (DE-627)SPR04495607X (SPR)s13369-021-05693-6-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Erbir, Muhammed Ali verfasserin aut The Do’s and Don’ts for Increasing the Accuracy of Face Recognition on VGGFace2 Dataset 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2021 Abstract In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems. Face recognition (dpeaa)DE-He213 Face verification (dpeaa)DE-He213 Biometric verification (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 VGGFace2 dataset (dpeaa)DE-He213 Ünver, Halil Murat verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 46(2021), 9 vom: 11. Mai, Seite 8901-8911 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:46 year:2021 number:9 day:11 month:05 pages:8901-8911 https://dx.doi.org/10.1007/s13369-021-05693-6 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_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_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_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 31.00 ASE AR 46 2021 9 11 05 8901-8911 |
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10.1007/s13369-021-05693-6 doi (DE-627)SPR04495607X (SPR)s13369-021-05693-6-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Erbir, Muhammed Ali verfasserin aut The Do’s and Don’ts for Increasing the Accuracy of Face Recognition on VGGFace2 Dataset 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2021 Abstract In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems. Face recognition (dpeaa)DE-He213 Face verification (dpeaa)DE-He213 Biometric verification (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 VGGFace2 dataset (dpeaa)DE-He213 Ünver, Halil Murat verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 46(2021), 9 vom: 11. Mai, Seite 8901-8911 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:46 year:2021 number:9 day:11 month:05 pages:8901-8911 https://dx.doi.org/10.1007/s13369-021-05693-6 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_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_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_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 31.00 ASE AR 46 2021 9 11 05 8901-8911 |
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Enthalten in The Arabian journal for science and engineering 46(2021), 9 vom: 11. Mai, Seite 8901-8911 volume:46 year:2021 number:9 day:11 month:05 pages:8901-8911 |
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Face recognition Face verification Biometric verification Deep learning Image processing VGGFace2 dataset |
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Erbir, Muhammed Ali @@aut@@ Ünver, Halil Murat @@aut@@ |
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do’s and don’ts for increasing the accuracy of face recognition on vggface2 dataset |
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The Do’s and Don’ts for Increasing the Accuracy of Face Recognition on VGGFace2 Dataset |
| abstract |
Abstract In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems. © King Fahd University of Petroleum & Minerals 2021 |
| abstractGer |
Abstract In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems. © King Fahd University of Petroleum & Minerals 2021 |
| abstract_unstemmed |
Abstract In this study, developments in face recognition are examined. Some methods are presented to increase the accuracy rate in face recognition by using transfer learning with VGGFace2 dataset and 4 different CNN models. While some of these tested offers decreased the accuracy rate, some of them increased. Effects of histogram balancing, expanding the training data, extracting the effect of non-facial portions of images and vertically aligning images on the accuracy rate were determined and compared to the accuracy rates of original images. As the optimal solution, transfer learning from the InceptionV3 model was preferred, vertical positioning was made, and an accuracy rate of 95.47% was achieved when 10% of the images were used for testing and 90% for training in a 100 people subset of VGGFace2 dataset. In LFW, one of the widely used datasets in the literature, an accuracy rate of 100% has been achieved by exceeding the highest accuracy achieved so far and all images in the LFW database have been recognized without any problems. © King Fahd University of Petroleum & Minerals 2021 |
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The Do’s and Don’ts for Increasing the Accuracy of Face Recognition on VGGFace2 Dataset |
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https://dx.doi.org/10.1007/s13369-021-05693-6 |
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Ünver, Halil Murat |
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10.1007/s13369-021-05693-6 |
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2024-07-04T02:56:31.717Z |
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|
| score |
7.3993654 |