Thesis Abstract

Abstract
The recognition of human emotions is a crucial aspect of human-computer interaction, with applications spanning various fields such as marketing, healthcare, and entertainment. In recent years, deep learning techniques have shown remarkable success in emotion recognition tasks, particularly in the analysis of video data. This thesis presents the development of a deep learning-based system for real-time emotion recognition in video streams. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The literature review in Chapter 2 covers ten key topics related to emotion recognition, deep learning, video analysis, and existing approaches in the field. Chapter 3 details the research methodology adopted in this study, including data collection, preprocessing techniques, deep learning model selection, training procedures, and evaluation metrics. The chapter also discusses the hardware and software tools used in the implementation of the system. In Chapter 4, the findings of the research are presented and discussed in detail. The performance of the developed deep learning system in real-time emotion recognition tasks is evaluated based on accuracy, speed, and robustness. The chapter also includes a comparative analysis with existing approaches to highlight the strengths and limitations of the proposed system. Finally, Chapter 5 provides a comprehensive conclusion and summary of the project thesis. The key contributions, implications, and future directions for research in the field of real-time emotion recognition using deep learning are discussed. The thesis concludes with recommendations for further research and practical applications of the developed system. Overall, this thesis contributes to the advancement of emotion recognition technology by proposing a novel deep learning-based system for real-time analysis of emotions in video streams. The findings of this research have significant implications for various industries and pave the way for future developments in human-computer interaction systems.

Thesis Overview