Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units

Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial r...
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Autor*in:	Luis Lopes Chambino [verfasserIn] José Silvestre Silva [verfasserIn] Alexandre Bernardino [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	facial recognition multispectral images infrared presentation attack detector

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 21(2021), 13, p 4520
Übergeordnetes Werk:	volume:21 ; year:2021 ; number:13, p 4520

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s21134520

Katalog-ID:	DOAJ079184677

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authorswithroles_txt_mv	Luis Lopes Chambino @@aut@@ José Silvestre Silva @@aut@@ Alexandre Bernardino @@aut@@
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callnumber-first	T - Technology
author	Luis Lopes Chambino
spellingShingle	Luis Lopes Chambino misc TP1-1185 misc facial recognition misc multispectral images misc infrared misc presentation attack detector misc Chemical technology Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units
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topic_title	TP1-1185 Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units facial recognition multispectral images infrared presentation attack detector
topic	misc TP1-1185 misc facial recognition misc multispectral images misc infrared misc presentation attack detector misc Chemical technology
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title	Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units
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title_sort	multispectral face recognition using transfer learning with adaptation of domain specific units
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title_auth	Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units
abstract	Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial recognition that uses multiple deep convolutional neural networks and multispectral images is proposed. A domain-specific transfer-learning methodology applied to a deep neural network pre-trained in RGB images is shown to generalize well to the multispectral domain. We also propose a skin detector module for forgery detection. Several experiments were planned to assess the performance of our methods. First, we evaluate the performance of the forgery detection module using face masks and coverings of different materials. A second study was carried out with the objective of tuning the parameters of our domain-specific transfer-learning methodology, in particular which layers of the pre-trained network should be retrained to obtain good adaptation to multispectral images. A third study was conducted to evaluate the performance of support vector machines (SVM) and k-nearest neighbor classifiers using the embeddings obtained from the trained neural network. Finally, we compare the proposed method with other state-of-the-art approaches. The experimental results show performance improvements in the Tufts and CASIA NIR-VIS 2.0 multispectral databases, with a rank-1 score of 99.7% and 99.8%, respectively.
abstractGer	Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial recognition that uses multiple deep convolutional neural networks and multispectral images is proposed. A domain-specific transfer-learning methodology applied to a deep neural network pre-trained in RGB images is shown to generalize well to the multispectral domain. We also propose a skin detector module for forgery detection. Several experiments were planned to assess the performance of our methods. First, we evaluate the performance of the forgery detection module using face masks and coverings of different materials. A second study was carried out with the objective of tuning the parameters of our domain-specific transfer-learning methodology, in particular which layers of the pre-trained network should be retrained to obtain good adaptation to multispectral images. A third study was conducted to evaluate the performance of support vector machines (SVM) and k-nearest neighbor classifiers using the embeddings obtained from the trained neural network. Finally, we compare the proposed method with other state-of-the-art approaches. The experimental results show performance improvements in the Tufts and CASIA NIR-VIS 2.0 multispectral databases, with a rank-1 score of 99.7% and 99.8%, respectively.
abstract_unstemmed	Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial recognition that uses multiple deep convolutional neural networks and multispectral images is proposed. A domain-specific transfer-learning methodology applied to a deep neural network pre-trained in RGB images is shown to generalize well to the multispectral domain. We also propose a skin detector module for forgery detection. Several experiments were planned to assess the performance of our methods. First, we evaluate the performance of the forgery detection module using face masks and coverings of different materials. A second study was carried out with the objective of tuning the parameters of our domain-specific transfer-learning methodology, in particular which layers of the pre-trained network should be retrained to obtain good adaptation to multispectral images. A third study was conducted to evaluate the performance of support vector machines (SVM) and k-nearest neighbor classifiers using the embeddings obtained from the trained neural network. Finally, we compare the proposed method with other state-of-the-art approaches. The experimental results show performance improvements in the Tufts and CASIA NIR-VIS 2.0 multispectral databases, with a rank-1 score of 99.7% and 99.8%, respectively.
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title_short	Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units
url	https://doi.org/10.3390/s21134520 https://doaj.org/article/4048cc5ededd44809fd86e7779ddeba6 https://www.mdpi.com/1424-8220/21/13/4520 https://doaj.org/toc/1424-8220
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up_date	2024-07-03T22:09:19.877Z
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