Design and Implementation of a Real-Time Face Recognition System using Convolutional Neural Networks

Thesis Abstract

Abstract
This thesis presents the design and implementation of a real-time face recognition system using Convolutional Neural Networks (CNNs). The utilization of CNNs in image processing has shown significant advancements in various applications, including facial recognition. The primary objective of this research is to develop a robust face recognition system that can operate in real-time scenarios with high accuracy and efficiency. The study begins with a comprehensive introduction to the background of facial recognition technology, highlighting the evolution of CNNs and their significance in image classification. The problem statement identifies the challenges faced in existing face recognition systems and emphasizes the need for an improved real-time solution. The objectives of the study are outlined to guide the research towards achieving specific goals, such as enhancing accuracy, reducing processing time, and optimizing system performance. Limitations and scope of the study are defined to set boundaries and clarify the extent of the research. The significance of the study underscores the potential impact of the proposed face recognition system in enhancing security, surveillance, and authentication processes. The structure of the thesis provides a roadmap for the reader, outlining the organization of chapters and sections to follow. Additionally, key terms and definitions are provided to ensure a clear understanding of the terminology used throughout the thesis. Chapter two presents a detailed literature review, covering ten key studies and advancements in the field of facial recognition and CNN technology. The review provides insights into existing methodologies, algorithms, and best practices in face recognition systems, serving as a foundation for the research. Chapter three delves into the research methodology, outlining the step-by-step process of designing and implementing the real-time face recognition system. Key components such as data collection, preprocessing, model training, and system evaluation are elaborated upon, along with the selection of appropriate tools and techniques. Chapter four presents an elaborate discussion of the findings obtained from the implementation of the face recognition system. Results related to accuracy, speed, and performance metrics are analyzed and compared with existing systems to gauge the effectiveness of the proposed solution. Finally, chapter five concludes the thesis by summarizing the key findings, highlighting the contributions of the research, and discussing potential avenues for future work. The conclusion emphasizes the significance of the developed face recognition system and its potential impact on real-world applications. Overall, this thesis contributes to the advancement of facial recognition technology through the design and implementation of a real-time system using Convolutional Neural Networks.