Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies

Human recognition technology is a task that determines the people existing in images with the purpose of identifying them. However, automatic human recognition at night is still a challenge because of its need to align requirements with a high accuracy rate and speed. This article aims to design a n...

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Autores principales: Manssor, Samah A. F., Sun, Shaoyuan, Elhassan, Mohammed A. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272095/
https://www.ncbi.nlm.nih.gov/pubmed/34202659
http://dx.doi.org/10.3390/s21134323
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author Manssor, Samah A. F.
Sun, Shaoyuan
Elhassan, Mohammed A. M.
author_facet Manssor, Samah A. F.
Sun, Shaoyuan
Elhassan, Mohammed A. M.
author_sort Manssor, Samah A. F.
collection PubMed
description Human recognition technology is a task that determines the people existing in images with the purpose of identifying them. However, automatic human recognition at night is still a challenge because of its need to align requirements with a high accuracy rate and speed. This article aims to design a novel approach that applies integrated face and gait analyses to enhance the performance of real-time human recognition in TIR images at night under various walking conditions. Therefore, a new network is proposed to improve the YOLOv3 model by fusing face and gait classifiers to identify individuals automatically. This network optimizes the TIR images, provides more accurate features (face, gait, and body segment) of the person, and possesses it through the PDM-Net to detect the person class; then, PRM-Net classifies the images for human recognition. The proposed methodology uses accurate features to form the face and gait signatures by applying the YOLO-face algorithm and YOLO algorithm. This approach was pre-trained on three night (DHU Night, FLIR, and KAIST) databases to simulate realistic conditions during the surveillance-protecting areas. The experimental results determined that the proposed method is superior to other results-related methods in the same night databases in accuracy and detection time.
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spelling pubmed-82720952021-07-11 Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies Manssor, Samah A. F. Sun, Shaoyuan Elhassan, Mohammed A. M. Sensors (Basel) Article Human recognition technology is a task that determines the people existing in images with the purpose of identifying them. However, automatic human recognition at night is still a challenge because of its need to align requirements with a high accuracy rate and speed. This article aims to design a novel approach that applies integrated face and gait analyses to enhance the performance of real-time human recognition in TIR images at night under various walking conditions. Therefore, a new network is proposed to improve the YOLOv3 model by fusing face and gait classifiers to identify individuals automatically. This network optimizes the TIR images, provides more accurate features (face, gait, and body segment) of the person, and possesses it through the PDM-Net to detect the person class; then, PRM-Net classifies the images for human recognition. The proposed methodology uses accurate features to form the face and gait signatures by applying the YOLO-face algorithm and YOLO algorithm. This approach was pre-trained on three night (DHU Night, FLIR, and KAIST) databases to simulate realistic conditions during the surveillance-protecting areas. The experimental results determined that the proposed method is superior to other results-related methods in the same night databases in accuracy and detection time. MDPI 2021-06-24 /pmc/articles/PMC8272095/ /pubmed/34202659 http://dx.doi.org/10.3390/s21134323 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Manssor, Samah A. F.
Sun, Shaoyuan
Elhassan, Mohammed A. M.
Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies
title Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies
title_full Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies
title_fullStr Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies
title_full_unstemmed Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies
title_short Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies
title_sort real-time human recognition at night via integrated face and gait recognition technologies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272095/
https://www.ncbi.nlm.nih.gov/pubmed/34202659
http://dx.doi.org/10.3390/s21134323
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