Thermal face recognition under different conditions

Abstract Background A thermal face recognition under different conditions is proposed in this article. The novelty of the proposed method is applying temperature information in the recognition of thermal face. The physiological information is obtained from the face using a thermal camera, and a mach...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shinfeng D. Lin, Luming Chen, Wensheng Chen
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Convolutional neural network Face recognition Thermal image Bayesian framework Random forest Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b53925967e634433ad078f8799c699ae
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b53925967e634433ad078f8799c699ae
record_format	dspace
spelling	oai:doaj.org-article:b53925967e634433ad078f8799c699ae2021-11-14T12:12:59ZThermal face recognition under different conditions10.1186/s12859-021-04228-y1471-2105https://doaj.org/article/b53925967e634433ad078f8799c699ae2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04228-yhttps://doaj.org/toc/1471-2105Abstract Background A thermal face recognition under different conditions is proposed in this article. The novelty of the proposed method is applying temperature information in the recognition of thermal face. The physiological information is obtained from the face using a thermal camera, and a machine learning classifier is utilized for thermal face recognition. The steps of preprocessing, feature extraction and classification are incorporated in training phase. First of all, by using Bayesian framework, the human face can be extracted from thermal face image. Several thermal points are selected as a feature vector. These points are utilized to train Random Forest (RF). Random Forest is a supervised learning algorithm. It is an ensemble of decision trees. Namely, RF merges multiple decision trees together to obtain a more accurate classification. Feature vectors from the testing image are fed into the classifier for face recognition. Results Experiments were conducted under different conditions, including normal, adding noise, wearing glasses, face mask, and glasses with mask. To compare the performance with the convolutional neural network-based technique, experimental results of the proposed method demonstrate its robustness against different challenges. Conclusions Comparisons with other techniques demonstrate that the proposed method is robust under less feature points, which is around one twenty-eighth to one sixtieth of those by other classic methods.Shinfeng D. LinLuming ChenWensheng ChenBMCarticleConvolutional neural networkFace recognitionThermal imageBayesian frameworkRandom forestComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss S5, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Convolutional neural network Face recognition Thermal image Bayesian framework Random forest Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Convolutional neural network Face recognition Thermal image Bayesian framework Random forest Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Shinfeng D. Lin Luming Chen Wensheng Chen Thermal face recognition under different conditions
description	Abstract Background A thermal face recognition under different conditions is proposed in this article. The novelty of the proposed method is applying temperature information in the recognition of thermal face. The physiological information is obtained from the face using a thermal camera, and a machine learning classifier is utilized for thermal face recognition. The steps of preprocessing, feature extraction and classification are incorporated in training phase. First of all, by using Bayesian framework, the human face can be extracted from thermal face image. Several thermal points are selected as a feature vector. These points are utilized to train Random Forest (RF). Random Forest is a supervised learning algorithm. It is an ensemble of decision trees. Namely, RF merges multiple decision trees together to obtain a more accurate classification. Feature vectors from the testing image are fed into the classifier for face recognition. Results Experiments were conducted under different conditions, including normal, adding noise, wearing glasses, face mask, and glasses with mask. To compare the performance with the convolutional neural network-based technique, experimental results of the proposed method demonstrate its robustness against different challenges. Conclusions Comparisons with other techniques demonstrate that the proposed method is robust under less feature points, which is around one twenty-eighth to one sixtieth of those by other classic methods.
format	article
author	Shinfeng D. Lin Luming Chen Wensheng Chen
author_facet	Shinfeng D. Lin Luming Chen Wensheng Chen
author_sort	Shinfeng D. Lin
title	Thermal face recognition under different conditions
title_short	Thermal face recognition under different conditions
title_full	Thermal face recognition under different conditions
title_fullStr	Thermal face recognition under different conditions
title_full_unstemmed	Thermal face recognition under different conditions
title_sort	thermal face recognition under different conditions
publisher	BMC
publishDate	2021
url	https://doaj.org/article/b53925967e634433ad078f8799c699ae
work_keys_str_mv	AT shinfengdlin thermalfacerecognitionunderdifferentconditions AT lumingchen thermalfacerecognitionunderdifferentconditions AT wenshengchen thermalfacerecognitionunderdifferentconditions
_version_	1718429368302174208

Thermal face recognition under different conditions

Ejemplares similares