Thesis Abstract

The abstract of this thesis focuses on the development of a real-time facial recognition system utilizing deep learning algorithms. The project aims to leverage the capabilities of deep learning to enhance the accuracy and efficiency of facial recognition technology. Facial recognition systems have gained significant attention in various applications such as security, surveillance, and biometric authentication due to their potential to provide reliable identification and verification mechanisms. However, traditional facial recognition systems often face challenges in handling variations in facial appearance, lighting conditions, and occlusions, which can affect their performance. Deep learning algorithms, particularly convolutional neural networks (CNNs), have shown remarkable success in image recognition tasks, including facial recognition. By training CNNs on large datasets of facial images, these algorithms can automatically learn features and patterns that are crucial for accurate facial recognition. This project will explore the implementation of deep learning algorithms, specifically CNNs, to develop a real-time facial recognition system capable of accurately identifying individuals in dynamic environments. The thesis will begin with an introduction that provides an overview of the significance of facial recognition technology and the motivation behind utilizing deep learning algorithms for this purpose. The background of study will delve into the evolution of facial recognition technology, highlighting the challenges faced by traditional methods and the emergence of deep learning as a promising solution. The problem statement will identify the limitations of existing facial recognition systems and the need for a more robust and efficient approach. The objectives of the study will outline the specific goals and outcomes expected from the development of the real-time facial recognition system. The literature review will explore existing research and advancements in facial recognition technology, with a focus on deep learning approaches and their applications. This section will provide a comprehensive overview of the theoretical foundations and methodologies relevant to the project, including the architecture of CNNs, training techniques, and performance evaluation metrics. The research methodology will detail the process of data collection, preprocessing, model training, and evaluation methods employed in developing the real-time facial recognition system. This section will also discuss the dataset used for training and testing the deep learning model, as well as the implementation details of the system. The discussion of findings will present the results of the experiments conducted to evaluate the performance of the developed facial recognition system. This section will analyze the accuracy, speed, and robustness of the system in real-time scenarios and compare it with existing methods to demonstrate its effectiveness. In conclusion, the thesis will summarize the key findings, contributions, and implications of the project. The significance of the study in advancing facial recognition technology using deep learning algorithms will be highlighted, along with recommendations for future research and development in this field. Overall, this thesis aims to contribute to the advancement of facial recognition technology by developing a real-time system that leverages the power of deep learning algorithms to enhance accuracy and efficiency in identifying individuals.

Thesis Overview