Design and Implementation of a Real-Time Face Recognition System Using Deep Learning Techniques in Embedded Systems

Thesis Abstract

Abstract
This thesis presents the design and implementation of a real-time face recognition system using deep learning techniques in embedded systems. Face recognition technology has gained significant attention due to its wide range of applications in security systems, surveillance, and human-computer interaction. Deep learning algorithms, particularly convolutional neural networks (CNNs), have shown remarkable performance in image recognition tasks, making them well-suited for face recognition applications. This project focuses on developing a system that can accurately and efficiently recognize faces in real-time using deep learning models deployed on embedded systems. The thesis begins with Chapter 1, which provides an introduction to the research topic, background information on face recognition technology, the problem statement, objectives of the study, limitations, scope, significance, structure of the thesis, and a definition of key terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to face recognition systems, deep learning techniques, embedded systems, and relevant research studies in the field. In Chapter 3, the research methodology is detailed, outlining the steps taken to design and implement the real-time face recognition system. This chapter includes sections on data collection, preprocessing, model selection, training, optimization, hardware selection, and system integration. The methodology focuses on leveraging the capabilities of deep learning frameworks to achieve high accuracy and efficiency in face recognition tasks while ensuring compatibility with embedded systems. Chapter 4 delves into an in-depth discussion of the findings obtained through the implementation of the face recognition system. This chapter evaluates the performance metrics of the system, including accuracy, speed, and resource utilization. It also discusses the challenges encountered during the development process and proposes potential solutions for further improvement. Finally, Chapter 5 presents the conclusion and summary of the project thesis. The key findings, contributions, limitations, and future research directions are discussed in this section. The conclusion highlights the effectiveness of using deep learning techniques in real-time face recognition applications on embedded systems and emphasizes the importance of continuous research and development in this field. Overall, this thesis contributes to the advancement of face recognition technology by demonstrating the feasibility and benefits of deploying deep learning models in embedded systems for real-time applications. The results obtained from this research provide valuable insights for researchers, developers, and practitioners interested in enhancing face recognition systems using state-of-the-art deep learning techniques.