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Face recognition for location detection of occupants in a building

Face recognition technology has undergone remarkable advancements in recent times, largely owing to improvements in digital camera technology. This technology would benefit from being incorporated into current systems like building management systems to improve the quality of human life and stren...

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Bibliographic Details
Main Author: Cheong, Kok Siong
Format: Final Year Project / Dissertation / Thesis
Published: 2023
Subjects:
T Technology (General)
TH Building construction
Online Access:http://eprints.utar.edu.my/6011/1/fyp__IB_2023_CKS.pdf
http://eprints.utar.edu.my/6011/
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spelling my-utar-eprints.60112024-01-02T16:11:12Z Face recognition for location detection of occupants in a building Cheong, Kok Siong T Technology (General) TH Building construction Face recognition technology has undergone remarkable advancements in recent times, largely owing to improvements in digital camera technology. This technology would benefit from being incorporated into current systems like building management systems to improve the quality of human life and strengthen security in varied contexts. The Face Recognition for Location Detection (FRLD) of Occupants in a Building system in the proposed project aims to fully use face recognition technology for pinpointing occupant location within a building. By utilising the strength of the current video network and an extensive database of registered residents, this modern technology will effectively identify people and locate their locations within the building. The system promises to revolutionise building management and improve security by seamlessly combining these components. The development of this project is based on existing infrastructure and equipment, and refers to the exploration and development of all similar systems in the market. The main purpose is to address the lack of effectiveness in tracking the location of all residents in the current process, inaccurate real-time data, and potential cybersecurity risks and privacy issues that may arise from the current process. In addition, the project will also address challenges related to user experience and facial recognition technology. Last but not least, the proposed system is expected to be implemented in various environments such as office buildings, hospitals, apartments, and universities to enhance existing building management and safety measures. Through this measure, we are expected to bring more efficient management and stronger. In this report, we also provide a detailed introduction to methodology and software development lifecycle to clarify the main core of the software and explore into the process of subsequence to ensure smooth development and completion of the software. Although some challenges were encountered during the development process, leading to deviations in planning, but those have been replaced by other approaches and did not affect the final outcome. 2023-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6011/1/fyp__IB_2023_CKS.pdf Cheong, Kok Siong (2023) Face recognition for location detection of occupants in a building. Final Year Project, UTAR. http://eprints.utar.edu.my/6011/
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 T Technology (General)
TH Building construction
spellingShingle T Technology (General)
TH Building construction
Cheong, Kok Siong
Face recognition for location detection of occupants in a building
description Face recognition technology has undergone remarkable advancements in recent times, largely owing to improvements in digital camera technology. This technology would benefit from being incorporated into current systems like building management systems to improve the quality of human life and strengthen security in varied contexts. The Face Recognition for Location Detection (FRLD) of Occupants in a Building system in the proposed project aims to fully use face recognition technology for pinpointing occupant location within a building. By utilising the strength of the current video network and an extensive database of registered residents, this modern technology will effectively identify people and locate their locations within the building. The system promises to revolutionise building management and improve security by seamlessly combining these components. The development of this project is based on existing infrastructure and equipment, and refers to the exploration and development of all similar systems in the market. The main purpose is to address the lack of effectiveness in tracking the location of all residents in the current process, inaccurate real-time data, and potential cybersecurity risks and privacy issues that may arise from the current process. In addition, the project will also address challenges related to user experience and facial recognition technology. Last but not least, the proposed system is expected to be implemented in various environments such as office buildings, hospitals, apartments, and universities to enhance existing building management and safety measures. Through this measure, we are expected to bring more efficient management and stronger. In this report, we also provide a detailed introduction to methodology and software development lifecycle to clarify the main core of the software and explore into the process of subsequence to ensure smooth development and completion of the software. Although some challenges were encountered during the development process, leading to deviations in planning, but those have been replaced by other approaches and did not affect the final outcome.
format Final Year Project / Dissertation / Thesis
author Cheong, Kok Siong
author_facet Cheong, Kok Siong
author_sort Cheong, Kok Siong
title Face recognition for location detection of occupants in a building
title_short Face recognition for location detection of occupants in a building
title_full Face recognition for location detection of occupants in a building
title_fullStr Face recognition for location detection of occupants in a building
title_full_unstemmed Face recognition for location detection of occupants in a building
title_sort face recognition for location detection of occupants in a building
publishDate 2023
url http://eprints.utar.edu.my/6011/1/fyp__IB_2023_CKS.pdf
http://eprints.utar.edu.my/6011/
_version_ 1787140949180153856
score 13.211869

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