Improving face verification using facial marks and deep CNN: IARPA Janus benchmark-A
Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Riaz, Sidra [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Übergeordnetes Werk: |
Enthalten in: Triatoma infestans , to be or not to be autogenic? - Lamattina, D ELSEVIER, 2022, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:104 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.imavis.2020.104020 |
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ELV052202119 |
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| 245 | 1 | 0 | |a Improving face verification using facial marks and deep CNN: IARPA Janus benchmark-A |
| 264 | 1 | |c 2020transfer abstract | |
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| 520 | |a Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. | ||
| 520 | |a Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. | ||
| 700 | 1 | |a Park, Unsang |4 oth | |
| 700 | 1 | |a Natarajan, Prem |4 oth | |
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10.1016/j.imavis.2020.104020 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001216.pica (DE-627)ELV052202119 (ELSEVIER)S0262-8856(20)30152-9 DE-627 ger DE-627 rakwb eng Riaz, Sidra verfasserin aut Improving face verification using facial marks and deep CNN: IARPA Janus benchmark-A 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. Park, Unsang oth Natarajan, Prem oth Enthalten in Elsevier Science Lamattina, D ELSEVIER Triatoma infestans , to be or not to be autogenic? 2022 Amsterdam [u.a.] (DE-627)ELV00877899X volume:104 year:2020 pages:0 https://doi.org/10.1016/j.imavis.2020.104020 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 104 2020 0 |
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10.1016/j.imavis.2020.104020 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001216.pica (DE-627)ELV052202119 (ELSEVIER)S0262-8856(20)30152-9 DE-627 ger DE-627 rakwb eng Riaz, Sidra verfasserin aut Improving face verification using facial marks and deep CNN: IARPA Janus benchmark-A 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. Park, Unsang oth Natarajan, Prem oth Enthalten in Elsevier Science Lamattina, D ELSEVIER Triatoma infestans , to be or not to be autogenic? 2022 Amsterdam [u.a.] (DE-627)ELV00877899X volume:104 year:2020 pages:0 https://doi.org/10.1016/j.imavis.2020.104020 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 104 2020 0 |
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10.1016/j.imavis.2020.104020 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001216.pica (DE-627)ELV052202119 (ELSEVIER)S0262-8856(20)30152-9 DE-627 ger DE-627 rakwb eng Riaz, Sidra verfasserin aut Improving face verification using facial marks and deep CNN: IARPA Janus benchmark-A 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. Park, Unsang oth Natarajan, Prem oth Enthalten in Elsevier Science Lamattina, D ELSEVIER Triatoma infestans , to be or not to be autogenic? 2022 Amsterdam [u.a.] (DE-627)ELV00877899X volume:104 year:2020 pages:0 https://doi.org/10.1016/j.imavis.2020.104020 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 104 2020 0 |
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10.1016/j.imavis.2020.104020 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001216.pica (DE-627)ELV052202119 (ELSEVIER)S0262-8856(20)30152-9 DE-627 ger DE-627 rakwb eng Riaz, Sidra verfasserin aut Improving face verification using facial marks and deep CNN: IARPA Janus benchmark-A 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. Park, Unsang oth Natarajan, Prem oth Enthalten in Elsevier Science Lamattina, D ELSEVIER Triatoma infestans , to be or not to be autogenic? 2022 Amsterdam [u.a.] (DE-627)ELV00877899X volume:104 year:2020 pages:0 https://doi.org/10.1016/j.imavis.2020.104020 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 104 2020 0 |
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10.1016/j.imavis.2020.104020 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001216.pica (DE-627)ELV052202119 (ELSEVIER)S0262-8856(20)30152-9 DE-627 ger DE-627 rakwb eng Riaz, Sidra verfasserin aut Improving face verification using facial marks and deep CNN: IARPA Janus benchmark-A 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. Park, Unsang oth Natarajan, Prem oth Enthalten in Elsevier Science Lamattina, D ELSEVIER Triatoma infestans , to be or not to be autogenic? 2022 Amsterdam [u.a.] (DE-627)ELV00877899X volume:104 year:2020 pages:0 https://doi.org/10.1016/j.imavis.2020.104020 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 104 2020 0 |
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Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. 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Improving face verification using facial marks and deep CNN: IARPA Janus benchmark-A |
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Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. |
| abstractGer |
Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. |
| abstract_unstemmed |
Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. |
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Improving face verification using facial marks and deep CNN: IARPA Janus benchmark-A |
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