Infrared Handprint Classification Using Deep Convolution Neural Network
Abstract Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtainin...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhou, Zijie [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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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: Neural processing letters - Springer US, 1994, 53(2021), 2 vom: 20. Jan., Seite 1065-1079 |
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| Übergeordnetes Werk: |
volume:53 ; year:2021 ; number:2 ; day:20 ; month:01 ; pages:1065-1079 |
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| DOI / URN: |
10.1007/s11063-021-10429-6 |
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OLC2124898639 |
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| 520 | |a Abstract Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtaining more information for solving cases. However, due to thermal diffusion, the depth fuzzy feature of infrared handprint is not conducive to detection and classification, and the convolution neural network technology is widely used in the field of natural image classification because of its excellent feature extraction ability.Therefore, aiming at the problem of fuzzy infrared handprint classification, we design a novel convolution neural network, which includes a convolutional layer, small MBConv block and fully connected layer.We choose EfficientNet which is suitable for infrared handprint classification from classical convolution neural network as our basic network. And propose a small MBConv block to improve the network model, so that the network has fewer training parameters, effectively reduces the problem of over fitting, and improves the classification performance compared with the original model.We use our model for the automatic classification of infrared handprint images. The results show that our model achieves the average accuracyto 95.78% for multi-class classification, which is 2.19% higher than the original model. | ||
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| 650 | 4 | |a Infrared thermal trace detection | |
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10.1007/s11063-021-10429-6 doi (DE-627)OLC2124898639 (DE-He213)s11063-021-10429-6-p DE-627 ger DE-627 rakwb eng 000 VZ Zhou, Zijie verfasserin aut Infrared Handprint Classification Using Deep Convolution Neural Network 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtaining more information for solving cases. However, due to thermal diffusion, the depth fuzzy feature of infrared handprint is not conducive to detection and classification, and the convolution neural network technology is widely used in the field of natural image classification because of its excellent feature extraction ability.Therefore, aiming at the problem of fuzzy infrared handprint classification, we design a novel convolution neural network, which includes a convolutional layer, small MBConv block and fully connected layer.We choose EfficientNet which is suitable for infrared handprint classification from classical convolution neural network as our basic network. And propose a small MBConv block to improve the network model, so that the network has fewer training parameters, effectively reduces the problem of over fitting, and improves the classification performance compared with the original model.We use our model for the automatic classification of infrared handprint images. The results show that our model achieves the average accuracyto 95.78% for multi-class classification, which is 2.19% higher than the original model. Infrared handprint image Infrared handprint classification Infrared thermal trace detection Zhang, Baofeng aut Yu, Xiao (orcid)0000-0002-2462-012X aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 2 vom: 20. Jan., Seite 1065-1079 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:2 day:20 month:01 pages:1065-1079 https://doi.org/10.1007/s11063-021-10429-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 2 20 01 1065-1079 |
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10.1007/s11063-021-10429-6 doi (DE-627)OLC2124898639 (DE-He213)s11063-021-10429-6-p DE-627 ger DE-627 rakwb eng 000 VZ Zhou, Zijie verfasserin aut Infrared Handprint Classification Using Deep Convolution Neural Network 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtaining more information for solving cases. However, due to thermal diffusion, the depth fuzzy feature of infrared handprint is not conducive to detection and classification, and the convolution neural network technology is widely used in the field of natural image classification because of its excellent feature extraction ability.Therefore, aiming at the problem of fuzzy infrared handprint classification, we design a novel convolution neural network, which includes a convolutional layer, small MBConv block and fully connected layer.We choose EfficientNet which is suitable for infrared handprint classification from classical convolution neural network as our basic network. And propose a small MBConv block to improve the network model, so that the network has fewer training parameters, effectively reduces the problem of over fitting, and improves the classification performance compared with the original model.We use our model for the automatic classification of infrared handprint images. The results show that our model achieves the average accuracyto 95.78% for multi-class classification, which is 2.19% higher than the original model. Infrared handprint image Infrared handprint classification Infrared thermal trace detection Zhang, Baofeng aut Yu, Xiao (orcid)0000-0002-2462-012X aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 2 vom: 20. Jan., Seite 1065-1079 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:2 day:20 month:01 pages:1065-1079 https://doi.org/10.1007/s11063-021-10429-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 2 20 01 1065-1079 |
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10.1007/s11063-021-10429-6 doi (DE-627)OLC2124898639 (DE-He213)s11063-021-10429-6-p DE-627 ger DE-627 rakwb eng 000 VZ Zhou, Zijie verfasserin aut Infrared Handprint Classification Using Deep Convolution Neural Network 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtaining more information for solving cases. However, due to thermal diffusion, the depth fuzzy feature of infrared handprint is not conducive to detection and classification, and the convolution neural network technology is widely used in the field of natural image classification because of its excellent feature extraction ability.Therefore, aiming at the problem of fuzzy infrared handprint classification, we design a novel convolution neural network, which includes a convolutional layer, small MBConv block and fully connected layer.We choose EfficientNet which is suitable for infrared handprint classification from classical convolution neural network as our basic network. And propose a small MBConv block to improve the network model, so that the network has fewer training parameters, effectively reduces the problem of over fitting, and improves the classification performance compared with the original model.We use our model for the automatic classification of infrared handprint images. The results show that our model achieves the average accuracyto 95.78% for multi-class classification, which is 2.19% higher than the original model. Infrared handprint image Infrared handprint classification Infrared thermal trace detection Zhang, Baofeng aut Yu, Xiao (orcid)0000-0002-2462-012X aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 2 vom: 20. Jan., Seite 1065-1079 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:2 day:20 month:01 pages:1065-1079 https://doi.org/10.1007/s11063-021-10429-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 2 20 01 1065-1079 |
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10.1007/s11063-021-10429-6 doi (DE-627)OLC2124898639 (DE-He213)s11063-021-10429-6-p DE-627 ger DE-627 rakwb eng 000 VZ Zhou, Zijie verfasserin aut Infrared Handprint Classification Using Deep Convolution Neural Network 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtaining more information for solving cases. However, due to thermal diffusion, the depth fuzzy feature of infrared handprint is not conducive to detection and classification, and the convolution neural network technology is widely used in the field of natural image classification because of its excellent feature extraction ability.Therefore, aiming at the problem of fuzzy infrared handprint classification, we design a novel convolution neural network, which includes a convolutional layer, small MBConv block and fully connected layer.We choose EfficientNet which is suitable for infrared handprint classification from classical convolution neural network as our basic network. And propose a small MBConv block to improve the network model, so that the network has fewer training parameters, effectively reduces the problem of over fitting, and improves the classification performance compared with the original model.We use our model for the automatic classification of infrared handprint images. The results show that our model achieves the average accuracyto 95.78% for multi-class classification, which is 2.19% higher than the original model. Infrared handprint image Infrared handprint classification Infrared thermal trace detection Zhang, Baofeng aut Yu, Xiao (orcid)0000-0002-2462-012X aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 2 vom: 20. Jan., Seite 1065-1079 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:2 day:20 month:01 pages:1065-1079 https://doi.org/10.1007/s11063-021-10429-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 2 20 01 1065-1079 |
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10.1007/s11063-021-10429-6 doi (DE-627)OLC2124898639 (DE-He213)s11063-021-10429-6-p DE-627 ger DE-627 rakwb eng 000 VZ Zhou, Zijie verfasserin aut Infrared Handprint Classification Using Deep Convolution Neural Network 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtaining more information for solving cases. However, due to thermal diffusion, the depth fuzzy feature of infrared handprint is not conducive to detection and classification, and the convolution neural network technology is widely used in the field of natural image classification because of its excellent feature extraction ability.Therefore, aiming at the problem of fuzzy infrared handprint classification, we design a novel convolution neural network, which includes a convolutional layer, small MBConv block and fully connected layer.We choose EfficientNet which is suitable for infrared handprint classification from classical convolution neural network as our basic network. And propose a small MBConv block to improve the network model, so that the network has fewer training parameters, effectively reduces the problem of over fitting, and improves the classification performance compared with the original model.We use our model for the automatic classification of infrared handprint images. The results show that our model achieves the average accuracyto 95.78% for multi-class classification, which is 2.19% higher than the original model. Infrared handprint image Infrared handprint classification Infrared thermal trace detection Zhang, Baofeng aut Yu, Xiao (orcid)0000-0002-2462-012X aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 2 vom: 20. Jan., Seite 1065-1079 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:2 day:20 month:01 pages:1065-1079 https://doi.org/10.1007/s11063-021-10429-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 2 20 01 1065-1079 |
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Abstract Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtaining more information for solving cases. However, due to thermal diffusion, the depth fuzzy feature of infrared handprint is not conducive to detection and classification, and the convolution neural network technology is widely used in the field of natural image classification because of its excellent feature extraction ability.Therefore, aiming at the problem of fuzzy infrared handprint classification, we design a novel convolution neural network, which includes a convolutional layer, small MBConv block and fully connected layer.We choose EfficientNet which is suitable for infrared handprint classification from classical convolution neural network as our basic network. And propose a small MBConv block to improve the network model, so that the network has fewer training parameters, effectively reduces the problem of over fitting, and improves the classification performance compared with the original model.We use our model for the automatic classification of infrared handprint images. The results show that our model achieves the average accuracyto 95.78% for multi-class classification, which is 2.19% higher than the original model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstractGer |
Abstract Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtaining more information for solving cases. However, due to thermal diffusion, the depth fuzzy feature of infrared handprint is not conducive to detection and classification, and the convolution neural network technology is widely used in the field of natural image classification because of its excellent feature extraction ability.Therefore, aiming at the problem of fuzzy infrared handprint classification, we design a novel convolution neural network, which includes a convolutional layer, small MBConv block and fully connected layer.We choose EfficientNet which is suitable for infrared handprint classification from classical convolution neural network as our basic network. And propose a small MBConv block to improve the network model, so that the network has fewer training parameters, effectively reduces the problem of over fitting, and improves the classification performance compared with the original model.We use our model for the automatic classification of infrared handprint images. The results show that our model achieves the average accuracyto 95.78% for multi-class classification, which is 2.19% higher than the original model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtaining more information for solving cases. However, due to thermal diffusion, the depth fuzzy feature of infrared handprint is not conducive to detection and classification, and the convolution neural network technology is widely used in the field of natural image classification because of its excellent feature extraction ability.Therefore, aiming at the problem of fuzzy infrared handprint classification, we design a novel convolution neural network, which includes a convolutional layer, small MBConv block and fully connected layer.We choose EfficientNet which is suitable for infrared handprint classification from classical convolution neural network as our basic network. And propose a small MBConv block to improve the network model, so that the network has fewer training parameters, effectively reduces the problem of over fitting, and improves the classification performance compared with the original model.We use our model for the automatic classification of infrared handprint images. The results show that our model achieves the average accuracyto 95.78% for multi-class classification, which is 2.19% higher than the original model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Infrared Handprint Classification Using Deep Convolution Neural Network |
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https://doi.org/10.1007/s11063-021-10429-6 |
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Zhang, Baofeng Yu, Xiao |
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2024-07-04T01:48:35.927Z |
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