Facial recognition system for crowd security
Public safety is a top priority, but crowded areas witness numerous crimes annually, posing a threat to global peace and security. Identifying criminals and potential threats before they commit heinous acts like bombings, mass shootings, child abduction, and sexual assaults in public spaces is vi...
| Main Author: | |
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/6024/ http://eprints.utar.edu.my/6024/1/fyp_IB_2023_TKGHZ.pdf |
| _version_ | 1848886567370752000 |
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| author | Giam, Tia-kaztenie Hui Zhi |
| author_facet | Giam, Tia-kaztenie Hui Zhi |
| author_sort | Giam, Tia-kaztenie Hui Zhi |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | Public safety is a top priority, but crowded areas witness numerous crimes annually, posing a
threat to global peace and security. Identifying criminals and potential threats before they
commit heinous acts like bombings, mass shootings, child abduction, and sexual assaults in
public spaces is vital. While CCTV cameras offer post-incident monitoring, integrating facial
recognition technology with live video feeds can proactively prevent such tragedies. A facial
recognition system is developed to identify known criminals and missing persons from a face
database, enabling public surveillance cameras to track their whereabouts, monitor their
activities, and notify authorities promptly when needed. This Python project uses
Convolutional Neural Network (CNN) face recognition with Dlib and Haar Cascade Classifier
to effectively detect and monitor known and potentially dangerous individuals in publics areas,
facilitating swift emergency responses when necessary, while keeping watch for missing
persons. The system developed uses Firebase’s Realtime Database and Storage Bucket to store
and retrieve data in real-time to expedite system functionalities like reports generation and
database management. |
| first_indexed | 2025-11-15T19:40:33Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6024 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:40:33Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-60242024-01-02T16:21:07Z Facial recognition system for crowd security Giam, Tia-kaztenie Hui Zhi HT Communities. Classes. Races T Technology (General) TD Environmental technology. Sanitary engineering Public safety is a top priority, but crowded areas witness numerous crimes annually, posing a threat to global peace and security. Identifying criminals and potential threats before they commit heinous acts like bombings, mass shootings, child abduction, and sexual assaults in public spaces is vital. While CCTV cameras offer post-incident monitoring, integrating facial recognition technology with live video feeds can proactively prevent such tragedies. A facial recognition system is developed to identify known criminals and missing persons from a face database, enabling public surveillance cameras to track their whereabouts, monitor their activities, and notify authorities promptly when needed. This Python project uses Convolutional Neural Network (CNN) face recognition with Dlib and Haar Cascade Classifier to effectively detect and monitor known and potentially dangerous individuals in publics areas, facilitating swift emergency responses when necessary, while keeping watch for missing persons. The system developed uses Firebase’s Realtime Database and Storage Bucket to store and retrieve data in real-time to expedite system functionalities like reports generation and database management. 2023-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6024/1/fyp_IB_2023_TKGHZ.pdf Giam, Tia-kaztenie Hui Zhi (2023) Facial recognition system for crowd security. Final Year Project, UTAR. http://eprints.utar.edu.my/6024/ |
| spellingShingle | HT Communities. Classes. Races T Technology (General) TD Environmental technology. Sanitary engineering Giam, Tia-kaztenie Hui Zhi Facial recognition system for crowd security |
| title | Facial recognition system for crowd security |
| title_full | Facial recognition system for crowd security |
| title_fullStr | Facial recognition system for crowd security |
| title_full_unstemmed | Facial recognition system for crowd security |
| title_short | Facial recognition system for crowd security |
| title_sort | facial recognition system for crowd security |
| topic | HT Communities. Classes. Races T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/6024/ http://eprints.utar.edu.my/6024/1/fyp_IB_2023_TKGHZ.pdf |