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Introduction
In recent years, the use of facial recognition technology in security systems has become increasingly popular due to its ability to accurately identify individuals in real-time. However, traditional cloud-based facial recognition systems often face challenges such as latency, privacy concerns, and network bandwidth limitations.
Edge computing, a technology that brings computation and data storage closer to the location where it is needed, has emerged as a potential solution to these challenges. By processing data locally on devices at the edge of the network, edge computing can significantly enhance the performance of real-time facial recognition systems in security applications.
This thesis aims to explore the use of edge computing for real-time facial recognition in security systems. The study will investigate the benefits of edge computing in improving the efficiency and accuracy of facial recognition, as well as addressing the limitations of cloud-based systems.
Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms
Chapter 2: Literature Review
2.1 Evolution of facial recognition technology
2.2 Cloud computing in facial recognition systems
2.3 Edge computing in security applications
2.4 Advantages of edge computing for real-time facial recognition
2.5 Challenges of edge computing in security systems
2.6 Implementations of edge computing in real-time facial recognition
2.7 Comparative analysis of cloud vs edge computing in facial recognition
2.8 Privacy concerns in facial recognition technology
2.9 Ethical considerations in using facial recognition for security
2.10 Future trends in edge computing for facial recognition
Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Sampling techniques
3.5 Ethical considerations
3.6 Research limitations
3.7 Validity and reliability
3.8 Research instruments
Chapter 4: Discussion of Findings
4.1 Performance evaluation of edge computing in facial recognition
4.2 Impact of edge computing on accuracy and efficiency
4.3 Comparison with traditional cloud-based systems
4.4 Case studies of edge computing implementations in security systems
4.5 Integration of edge computing with existing security infrastructure
4.6 Cost-effectiveness of edge computing solutions
4.7 User feedback and acceptance
4.8 Recommendations for future research
Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Implications for practice
5.3 Recommendations for policymakers
5.4 Suggestions for further research
Overall, this thesis will provide valuable insights into the use of edge computing for real-time facial recognition in security systems and contribute to the advancement of this technology in enhancing security and surveillance measures.
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