Automation of surveillance systems using deep learning and facial recognition
Abstract While it's true that humans are skilled at recognizing faces, what about computers? Machinery is transforming conventional human labor in today's technologically sophisticated world. These cutting-edge machines may be programmed to recognize faces in inconceivable ways, a process...
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| Autor*in: |
Singh, Arpit [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International Journal of Systems Assurance Engineering and Management - Springer-Verlag, 2010, 14(2023), Suppl 1 vom: 06. Jan., Seite 236-245 |
|---|---|
| Übergeordnetes Werk: |
volume:14 ; year:2023 ; number:Suppl 1 ; day:06 ; month:01 ; pages:236-245 |
| Links: |
|---|
| DOI / URN: |
10.1007/s13198-022-01844-6 |
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| 520 | |a Abstract While it's true that humans are skilled at recognizing faces, what about computers? Machinery is transforming conventional human labor in today's technologically sophisticated world. These cutting-edge machines may be programmed to recognize faces in inconceivable ways, a process known as facial recognition or computer vision. In this study, we present a real-time system for detecting and identifying individuals in live or recorded surveillance feeds using deep learning and face recognition algorithms such as Convolution Neural Networks (CNN). The proposed real-time database integrated system is based on the VGGFace deep learning neural architecture. Transfer learning is used to retrain the model on a smaller original tailored dataset of 7500 images of 26 distinct individuals. Details regarding the creation of original tailored datasets and brief tests with various machine learning and deep learning approaches to analyze and improve the recognition accuracy of the proposed system are also reported. The proposed approach shows the highest degree of recognition accuracy by accurately recognizing each of the 26 individuals with a confidence level ranging from 78.54 percent to 100 percent, a mean average of 96 percent on real-time inputs. | ||
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10.1007/s13198-022-01844-6 doi (DE-627)SPR050112899 (SPR)s13198-022-01844-6-e DE-627 ger DE-627 rakwb eng Singh, Arpit verfasserin aut Automation of surveillance systems using deep learning and facial recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract While it's true that humans are skilled at recognizing faces, what about computers? Machinery is transforming conventional human labor in today's technologically sophisticated world. These cutting-edge machines may be programmed to recognize faces in inconceivable ways, a process known as facial recognition or computer vision. In this study, we present a real-time system for detecting and identifying individuals in live or recorded surveillance feeds using deep learning and face recognition algorithms such as Convolution Neural Networks (CNN). The proposed real-time database integrated system is based on the VGGFace deep learning neural architecture. Transfer learning is used to retrain the model on a smaller original tailored dataset of 7500 images of 26 distinct individuals. Details regarding the creation of original tailored datasets and brief tests with various machine learning and deep learning approaches to analyze and improve the recognition accuracy of the proposed system are also reported. The proposed approach shows the highest degree of recognition accuracy by accurately recognizing each of the 26 individuals with a confidence level ranging from 78.54 percent to 100 percent, a mean average of 96 percent on real-time inputs. Deep learning (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 VGGFace (dpeaa)DE-He213 Facial recognition (dpeaa)DE-He213 Original dataset (dpeaa)DE-He213 Bhatt, Saumya aut Nayak, Vishal aut Shah, Manan (orcid)0000-0002-8665-5010 aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 14(2023), Suppl 1 vom: 06. Jan., Seite 236-245 (DE-627)SPR031222420 nnns volume:14 year:2023 number:Suppl 1 day:06 month:01 pages:236-245 https://dx.doi.org/10.1007/s13198-022-01844-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 14 2023 Suppl 1 06 01 236-245 |
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10.1007/s13198-022-01844-6 doi (DE-627)SPR050112899 (SPR)s13198-022-01844-6-e DE-627 ger DE-627 rakwb eng Singh, Arpit verfasserin aut Automation of surveillance systems using deep learning and facial recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract While it's true that humans are skilled at recognizing faces, what about computers? Machinery is transforming conventional human labor in today's technologically sophisticated world. These cutting-edge machines may be programmed to recognize faces in inconceivable ways, a process known as facial recognition or computer vision. In this study, we present a real-time system for detecting and identifying individuals in live or recorded surveillance feeds using deep learning and face recognition algorithms such as Convolution Neural Networks (CNN). The proposed real-time database integrated system is based on the VGGFace deep learning neural architecture. Transfer learning is used to retrain the model on a smaller original tailored dataset of 7500 images of 26 distinct individuals. Details regarding the creation of original tailored datasets and brief tests with various machine learning and deep learning approaches to analyze and improve the recognition accuracy of the proposed system are also reported. The proposed approach shows the highest degree of recognition accuracy by accurately recognizing each of the 26 individuals with a confidence level ranging from 78.54 percent to 100 percent, a mean average of 96 percent on real-time inputs. Deep learning (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 VGGFace (dpeaa)DE-He213 Facial recognition (dpeaa)DE-He213 Original dataset (dpeaa)DE-He213 Bhatt, Saumya aut Nayak, Vishal aut Shah, Manan (orcid)0000-0002-8665-5010 aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 14(2023), Suppl 1 vom: 06. Jan., Seite 236-245 (DE-627)SPR031222420 nnns volume:14 year:2023 number:Suppl 1 day:06 month:01 pages:236-245 https://dx.doi.org/10.1007/s13198-022-01844-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 14 2023 Suppl 1 06 01 236-245 |
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10.1007/s13198-022-01844-6 doi (DE-627)SPR050112899 (SPR)s13198-022-01844-6-e DE-627 ger DE-627 rakwb eng Singh, Arpit verfasserin aut Automation of surveillance systems using deep learning and facial recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract While it's true that humans are skilled at recognizing faces, what about computers? Machinery is transforming conventional human labor in today's technologically sophisticated world. These cutting-edge machines may be programmed to recognize faces in inconceivable ways, a process known as facial recognition or computer vision. In this study, we present a real-time system for detecting and identifying individuals in live or recorded surveillance feeds using deep learning and face recognition algorithms such as Convolution Neural Networks (CNN). The proposed real-time database integrated system is based on the VGGFace deep learning neural architecture. Transfer learning is used to retrain the model on a smaller original tailored dataset of 7500 images of 26 distinct individuals. Details regarding the creation of original tailored datasets and brief tests with various machine learning and deep learning approaches to analyze and improve the recognition accuracy of the proposed system are also reported. The proposed approach shows the highest degree of recognition accuracy by accurately recognizing each of the 26 individuals with a confidence level ranging from 78.54 percent to 100 percent, a mean average of 96 percent on real-time inputs. Deep learning (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 VGGFace (dpeaa)DE-He213 Facial recognition (dpeaa)DE-He213 Original dataset (dpeaa)DE-He213 Bhatt, Saumya aut Nayak, Vishal aut Shah, Manan (orcid)0000-0002-8665-5010 aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 14(2023), Suppl 1 vom: 06. Jan., Seite 236-245 (DE-627)SPR031222420 nnns volume:14 year:2023 number:Suppl 1 day:06 month:01 pages:236-245 https://dx.doi.org/10.1007/s13198-022-01844-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 14 2023 Suppl 1 06 01 236-245 |
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10.1007/s13198-022-01844-6 doi (DE-627)SPR050112899 (SPR)s13198-022-01844-6-e DE-627 ger DE-627 rakwb eng Singh, Arpit verfasserin aut Automation of surveillance systems using deep learning and facial recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract While it's true that humans are skilled at recognizing faces, what about computers? Machinery is transforming conventional human labor in today's technologically sophisticated world. These cutting-edge machines may be programmed to recognize faces in inconceivable ways, a process known as facial recognition or computer vision. In this study, we present a real-time system for detecting and identifying individuals in live or recorded surveillance feeds using deep learning and face recognition algorithms such as Convolution Neural Networks (CNN). The proposed real-time database integrated system is based on the VGGFace deep learning neural architecture. Transfer learning is used to retrain the model on a smaller original tailored dataset of 7500 images of 26 distinct individuals. Details regarding the creation of original tailored datasets and brief tests with various machine learning and deep learning approaches to analyze and improve the recognition accuracy of the proposed system are also reported. The proposed approach shows the highest degree of recognition accuracy by accurately recognizing each of the 26 individuals with a confidence level ranging from 78.54 percent to 100 percent, a mean average of 96 percent on real-time inputs. Deep learning (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 VGGFace (dpeaa)DE-He213 Facial recognition (dpeaa)DE-He213 Original dataset (dpeaa)DE-He213 Bhatt, Saumya aut Nayak, Vishal aut Shah, Manan (orcid)0000-0002-8665-5010 aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 14(2023), Suppl 1 vom: 06. Jan., Seite 236-245 (DE-627)SPR031222420 nnns volume:14 year:2023 number:Suppl 1 day:06 month:01 pages:236-245 https://dx.doi.org/10.1007/s13198-022-01844-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 14 2023 Suppl 1 06 01 236-245 |
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10.1007/s13198-022-01844-6 doi (DE-627)SPR050112899 (SPR)s13198-022-01844-6-e DE-627 ger DE-627 rakwb eng Singh, Arpit verfasserin aut Automation of surveillance systems using deep learning and facial recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract While it's true that humans are skilled at recognizing faces, what about computers? Machinery is transforming conventional human labor in today's technologically sophisticated world. These cutting-edge machines may be programmed to recognize faces in inconceivable ways, a process known as facial recognition or computer vision. In this study, we present a real-time system for detecting and identifying individuals in live or recorded surveillance feeds using deep learning and face recognition algorithms such as Convolution Neural Networks (CNN). The proposed real-time database integrated system is based on the VGGFace deep learning neural architecture. Transfer learning is used to retrain the model on a smaller original tailored dataset of 7500 images of 26 distinct individuals. Details regarding the creation of original tailored datasets and brief tests with various machine learning and deep learning approaches to analyze and improve the recognition accuracy of the proposed system are also reported. The proposed approach shows the highest degree of recognition accuracy by accurately recognizing each of the 26 individuals with a confidence level ranging from 78.54 percent to 100 percent, a mean average of 96 percent on real-time inputs. Deep learning (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 VGGFace (dpeaa)DE-He213 Facial recognition (dpeaa)DE-He213 Original dataset (dpeaa)DE-He213 Bhatt, Saumya aut Nayak, Vishal aut Shah, Manan (orcid)0000-0002-8665-5010 aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 14(2023), Suppl 1 vom: 06. Jan., Seite 236-245 (DE-627)SPR031222420 nnns volume:14 year:2023 number:Suppl 1 day:06 month:01 pages:236-245 https://dx.doi.org/10.1007/s13198-022-01844-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 14 2023 Suppl 1 06 01 236-245 |
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Elektronische Aufsätze |
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Singh, Arpit |
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10.1007/s13198-022-01844-6 |
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automation of surveillance systems using deep learning and facial recognition |
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Automation of surveillance systems using deep learning and facial recognition |
| abstract |
Abstract While it's true that humans are skilled at recognizing faces, what about computers? Machinery is transforming conventional human labor in today's technologically sophisticated world. These cutting-edge machines may be programmed to recognize faces in inconceivable ways, a process known as facial recognition or computer vision. In this study, we present a real-time system for detecting and identifying individuals in live or recorded surveillance feeds using deep learning and face recognition algorithms such as Convolution Neural Networks (CNN). The proposed real-time database integrated system is based on the VGGFace deep learning neural architecture. Transfer learning is used to retrain the model on a smaller original tailored dataset of 7500 images of 26 distinct individuals. Details regarding the creation of original tailored datasets and brief tests with various machine learning and deep learning approaches to analyze and improve the recognition accuracy of the proposed system are also reported. The proposed approach shows the highest degree of recognition accuracy by accurately recognizing each of the 26 individuals with a confidence level ranging from 78.54 percent to 100 percent, a mean average of 96 percent on real-time inputs. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract While it's true that humans are skilled at recognizing faces, what about computers? Machinery is transforming conventional human labor in today's technologically sophisticated world. These cutting-edge machines may be programmed to recognize faces in inconceivable ways, a process known as facial recognition or computer vision. In this study, we present a real-time system for detecting and identifying individuals in live or recorded surveillance feeds using deep learning and face recognition algorithms such as Convolution Neural Networks (CNN). The proposed real-time database integrated system is based on the VGGFace deep learning neural architecture. Transfer learning is used to retrain the model on a smaller original tailored dataset of 7500 images of 26 distinct individuals. Details regarding the creation of original tailored datasets and brief tests with various machine learning and deep learning approaches to analyze and improve the recognition accuracy of the proposed system are also reported. The proposed approach shows the highest degree of recognition accuracy by accurately recognizing each of the 26 individuals with a confidence level ranging from 78.54 percent to 100 percent, a mean average of 96 percent on real-time inputs. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract While it's true that humans are skilled at recognizing faces, what about computers? Machinery is transforming conventional human labor in today's technologically sophisticated world. These cutting-edge machines may be programmed to recognize faces in inconceivable ways, a process known as facial recognition or computer vision. In this study, we present a real-time system for detecting and identifying individuals in live or recorded surveillance feeds using deep learning and face recognition algorithms such as Convolution Neural Networks (CNN). The proposed real-time database integrated system is based on the VGGFace deep learning neural architecture. Transfer learning is used to retrain the model on a smaller original tailored dataset of 7500 images of 26 distinct individuals. Details regarding the creation of original tailored datasets and brief tests with various machine learning and deep learning approaches to analyze and improve the recognition accuracy of the proposed system are also reported. The proposed approach shows the highest degree of recognition accuracy by accurately recognizing each of the 26 individuals with a confidence level ranging from 78.54 percent to 100 percent, a mean average of 96 percent on real-time inputs. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Automation of surveillance systems using deep learning and facial recognition |
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