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

Thesis Abstract

Abstract
The advancement of technology has led to the development of various systems that aim to enhance security and convenience in different domains. One such system is the face recognition system, which has gained significant attention due to its potential applications in security, surveillance, and access control. This thesis presents the design and implementation of a real-time face recognition system using deep learning techniques to improve the accuracy and efficiency of face recognition tasks. Chapter 1 provides an introduction to the research topic, outlining the background of the study, stating the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review, analyzing existing research and technologies related to face recognition systems and deep learning techniques. Chapter 3 details the research methodology employed in this study, including data collection, preprocessing, model selection, training, and evaluation. The chapter also discusses the tools and techniques used for implementation and testing of the face recognition system. Chapter 4 presents a detailed discussion of the findings obtained from the implementation of the system, highlighting the performance metrics, accuracy rates, and challenges encountered during the development process. The real-time face recognition system utilizes deep learning techniques such as convolutional neural networks (CNNs) for feature extraction and classification. The system is designed to detect and recognize faces in real-time video streams, providing accurate and efficient identification of individuals. Experimental results demonstrate the effectiveness of the proposed system in achieving high accuracy rates and fast processing speeds. In conclusion, this thesis contributes to the field of face recognition systems by presenting a novel approach to real-time face recognition using deep learning techniques. The developed system offers improved performance and reliability for various applications, including security, surveillance, and access control. Future research directions include exploring additional optimization techniques and enhancing the scalability and robustness of the system for deployment in real-world scenarios.