Integrating Deep Learning Models and Facial Recognition for Advanced Intelligent Surveillance Systems

DOI

10.2991/978-94-6463-718-2_65How to use a DOI?

Keywords

Intelligence surveillance system; convolutional neural network; Recurrent neural network; face recognition

Abstract

It is targeted to automate the whole surveillance systems by the deep learning face recognition. The primary objective of the first part is to develop an intelligent surveillance system that applies deep learning algorithms for timely processing data types and identify patterns. The integrated system utilizes the capabilities of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) to support strong object identification and tracking, allowing for real-time monitoring in different environments. In the second component, the study deals with the integration of facial recognition techniques into the monitoring system. The system can recognize and monitor people in different scenarios as deep learning-based face recognition models are incorporated into the system. This integration enhances the ability of the system to recognize specific individuals of interest, allowing for a more proactive and targeted surveillance posture. In addition to automating surveillance procedures, the integrated strategy forms an intelligent system, capable of learning and taking steps to counter the security threats. The use of deep learning to synthesize face recognition technology is realizing a synergetic outcome, providing an innovative and convenient solution for advanced surveillance applications in a wide range of scenarios. Incorporating such innovative technologies, this research develops intelligent surveillance systems and provides a secure protective environment.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Balamurugan
AU  - R. Kalaiarasi
AU  - D. Gopinath
AU  - H. J. Shanthi
PY  - 2025
DA  - 2025/05/23
TI  - Integrating Deep Learning Models and Facial Recognition for Advanced Intelligent Surveillance Systems
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 755
EP  - 766
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_65
DO  - 10.2991/978-94-6463-718-2_65
ID  - Balamurugan2025
ER  -