Thesis Abstract

Abstract
Face recognition is a crucial area in computer vision with applications ranging from security systems to personalized user experiences. This thesis presents the design and implementation of a real-time face recognition system using deep learning techniques. The project aims to leverage the power of deep learning algorithms to achieve accurate and efficient face recognition in real-time scenarios. The introduction provides a comprehensive overview of the importance of face recognition technology in various domains. The background of the study delves into the existing literature and research in the field of face recognition, emphasizing the significance of deep learning techniques in improving recognition accuracy. The problem statement highlights the challenges faced in traditional face recognition systems and sets the foundation for the proposed solution. The objectives of the study include developing a real-time face recognition system that can accurately identify individuals in varying conditions such as different poses, lighting, and occlusions. The limitations of the study are acknowledged, including constraints in dataset size, computational resources, and external factors affecting system performance. The scope of the study outlines the specific aspects of face recognition that will be covered, such as feature extraction, model training, and real-time implementation. The significance of the study lies in the potential impact of an efficient real-time face recognition system, including enhanced security measures, personalized user experiences, and improved accessibility in various applications. The structure of the thesis provides a roadmap for the reader, guiding them through the chapters and sections that detail the research process and findings. Definitions of key terms are also included to clarify the terminology used throughout the thesis. The literature review chapter explores existing works in the field of face recognition, focusing on deep learning approaches such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The research methodology chapter outlines the data collection process, preprocessing techniques, model architecture design, training procedures, and evaluation metrics used to assess system performance. The discussion of findings chapter presents the results of experiments conducted to test the real-time face recognition system, including accuracy rates, processing speeds, and robustness to various conditions. The conclusions drawn from the study highlight the effectiveness of deep learning techniques in improving face recognition accuracy and efficiency. The summary encapsulates the key findings and contributions of the research, along with suggestions for future work in this field. In conclusion, this thesis presents a detailed investigation into the design and implementation of a real-time face recognition system using deep learning techniques. The project aims to advance the state-of-the-art in face recognition technology and contribute to the development of more reliable and efficient systems for real-world applications.

Thesis Overview