Thesis Abstract

Abstract
Face recognition technology has gained significant attention in recent years due to its wide range of applications in various fields, including security, surveillance, and personal identification. This thesis focuses on the implementation of a real-time face recognition system using deep learning techniques. Deep learning has revolutionized the field of computer vision and has shown remarkable performance in facial recognition tasks. The primary objective of this research is to develop a robust and efficient face recognition system that can accurately identify individuals in real-time scenarios. The proposed system leverages deep learning algorithms, specifically convolutional neural networks (CNNs), for feature extraction and classification. The system will be trained on a large dataset of facial images to learn discriminative features that can differentiate between different individuals. The thesis begins with an introduction that provides an overview of the project and its significance. The background of the study explores the existing literature and research related to face recognition systems and deep learning techniques. The problem statement highlights the challenges and limitations of current face recognition systems, motivating the need for an improved real-time solution. The objectives of the study are clearly defined to guide the development and evaluation of the face recognition system. The limitations and scope of the study are also discussed to establish the boundaries and constraints of the research. The significance of the study is emphasized, highlighting the potential impact of the proposed system on practical applications. The structure of the thesis is outlined to provide a roadmap of the research methodology and chapters. Chapter two presents a comprehensive literature review that covers various aspects of face recognition systems, deep learning techniques, and related works in the field. The literature review sets the foundation for the research and identifies gaps that the proposed system aims to address. Chapter three describes the research methodology, including data collection, preprocessing, model design, training, and evaluation procedures. The chapter also discusses the experimental setup and performance metrics used to assess the effectiveness of the face recognition system. The methodology is designed to ensure the reliability and validity of the research findings. Chapter four presents a detailed discussion of the experimental results and findings obtained from evaluating the proposed face recognition system. The performance of the system is analyzed in terms of accuracy, speed, robustness, and scalability. The strengths and limitations of the system are critically evaluated, and potential areas for improvement are identified. Finally, chapter five concludes the thesis by summarizing the key findings, discussing the contributions of the research, and outlining future directions for further research and development. The conclusion emphasizes the significance of the proposed real-time face recognition system and its potential applications in various domains. In conclusion, this thesis contributes to the advancement of face recognition technology by developing an efficient and accurate real-time system using deep learning techniques. The research findings demonstrate the feasibility and effectiveness of the proposed system, paving the way for future advancements in the field of facial recognition.

Thesis Overview