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Multi-camera face detection and recognition in an unconstrained environment

Multi-camera face detection and recognition is an Artificial Intelligence (AI) based technology that leverages multiple cameras placed at different locations to detect and recognize human faces in real-world conditions accurately. While face detection and recognition technologies have exhibited high...

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Bibliographic Details
Main Author: Lee, Kian Huang
Format: Final Year Project / Dissertation / Thesis
Published: 2023
Subjects:
R Medicine (General)
Online Access:http://eprints.utar.edu.my/5807/1/BI_1804050_Final_LEE_KIAN_HUANG.pdf
http://eprints.utar.edu.my/5807/
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spelling my-utar-eprints.58072023-08-08T12:20:33Z Multi-camera face detection and recognition in an unconstrained environment Lee, Kian Huang R Medicine (General) Multi-camera face detection and recognition is an Artificial Intelligence (AI) based technology that leverages multiple cameras placed at different locations to detect and recognize human faces in real-world conditions accurately. While face detection and recognition technologies have exhibited high accuracy rates in controlled conditions, recognizing individuals in open environments remains challenging due to factors such as changes in illumination, movement, and occlusion. In this project, the multi-camera face detection and recognition is developed in unconstrained environment setting. The multi-camera solution can overcome these challenges by capturing images of individuals from different angles and lighting conditions, thus providing a more comprehensive view of the monitored area. The pipeline in this project consists of three main parts – face detection, face recognition, single and multi-camera tracking. A series of models training is done with the open-source dataset to build a robust pipeline, and finally, the pipeline adopted trained YOLOv5n for the face detection model with mean Average Precision (mAP) of 0.495. The system also adopted the SphereFace SFNet20 model with an accuracy of 82.05% and a higher inference rate as compared to SFNet64 for face recognition. These models are then fed into DeepSORT for multi-camera tracking. Our dataset has been applied to the pipeline and shown ideal outcomes with objectives achieved. The solution feasibility is demonstrated via prototype implementation. 2023 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/5807/1/BI_1804050_Final_LEE_KIAN_HUANG.pdf Lee, Kian Huang (2023) Multi-camera face detection and recognition in an unconstrained environment. Final Year Project, UTAR. http://eprints.utar.edu.my/5807/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic R Medicine (General)
spellingShingle R Medicine (General)
Lee, Kian Huang
Multi-camera face detection and recognition in an unconstrained environment
description Multi-camera face detection and recognition is an Artificial Intelligence (AI) based technology that leverages multiple cameras placed at different locations to detect and recognize human faces in real-world conditions accurately. While face detection and recognition technologies have exhibited high accuracy rates in controlled conditions, recognizing individuals in open environments remains challenging due to factors such as changes in illumination, movement, and occlusion. In this project, the multi-camera face detection and recognition is developed in unconstrained environment setting. The multi-camera solution can overcome these challenges by capturing images of individuals from different angles and lighting conditions, thus providing a more comprehensive view of the monitored area. The pipeline in this project consists of three main parts – face detection, face recognition, single and multi-camera tracking. A series of models training is done with the open-source dataset to build a robust pipeline, and finally, the pipeline adopted trained YOLOv5n for the face detection model with mean Average Precision (mAP) of 0.495. The system also adopted the SphereFace SFNet20 model with an accuracy of 82.05% and a higher inference rate as compared to SFNet64 for face recognition. These models are then fed into DeepSORT for multi-camera tracking. Our dataset has been applied to the pipeline and shown ideal outcomes with objectives achieved. The solution feasibility is demonstrated via prototype implementation.
format Final Year Project / Dissertation / Thesis
author Lee, Kian Huang
author_facet Lee, Kian Huang
author_sort Lee, Kian Huang
title Multi-camera face detection and recognition in an unconstrained environment
title_short Multi-camera face detection and recognition in an unconstrained environment
title_full Multi-camera face detection and recognition in an unconstrained environment
title_fullStr Multi-camera face detection and recognition in an unconstrained environment
title_full_unstemmed Multi-camera face detection and recognition in an unconstrained environment
title_sort multi-camera face detection and recognition in an unconstrained environment
publishDate 2023
url http://eprints.utar.edu.my/5807/1/BI_1804050_Final_LEE_KIAN_HUANG.pdf
http://eprints.utar.edu.my/5807/
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score 13.211869

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