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Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

Okokpujie, Kennedy O. and Okokpujie, Imhade P. and Subair, Roselyn E. and SIMONYAN, EMMANUEL OLUWATOBI and Akingunsoye, Adenugba Vincent (2023) Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments. International information and Engineering Technology Association, 24 (4). pp. 815-822.

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The advent of smart technology in urban environments has often been hailed as the solution to a plethora of contemporary urban challenges, ranging from environmental conservation to waste management and transportation. However, the critical aspect of security, encompassing crime detection and prevention, is frequently overlooked. Moreover, there is a dearth of research exploring the potential disruption of conventional face detection and recognition systems by new smart city surveillance security cameras, particularly those which autonomously update their databases. This paper addresses this gap by proposing the enhancement of security in smart cities through the development of an adaptive Age-Invariant Face Recognition (AIFR) model. A non-intrusive AIFR model was constructed using a convolutional neural network and transfer learning techniques, and was then integrated into surveillance cameras. These cameras, designed to capture the faces of city residents at regular intervals, consequently updated their databases autonomously. Upon testing, the developed model demonstrated its potential to substantially improve security by effectively detecting and identifying the residents and visitors of smart cities, and updating their database profiles. Remarkably, the model retained its effectiveness even with significant age intra-class variation, with the capability to alert relevant authorities about potential criminals or missing individuals. This research underscores the potential of adaptive face recognition systems in bolstering security measures within smart urban environments.

Item Type: Article
Uncontrolled Keywords: biometric, smart city, FG-NET ad, Age Invariant Face Recognition (AIFR), surveillance cameras, convolutional neutral network, data augmentation
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User: AKINWUMI
Date Deposited: 17 Nov 2023 07:48
Last Modified: 17 Nov 2023 07:48

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