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Introduction

Computer vision has become a rapidly growing field with applications in various domains, including healthcare, security, entertainment, and human-computer interaction. One of the emerging applications of computer vision is emotion recognition, which involves analyzing facial expressions to infer human emotions. Emotion recognition has gained significant attention due to its potential in various fields such as mental health, human-computer interaction, and market research.

This thesis explores the use of computer vision techniques for emotion recognition. The goal is to develop a system that can accurately recognize and classify human emotions based on facial expressions. The study will investigate different approaches and methodologies for emotion recognition, analyze the state-of-the-art techniques, and propose a novel framework for emotion recognition using computer vision.

The remainder of this thesis is structured as follows:

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter Two: Literature Review
2.1 Introduction to Emotion Recognition
2.2 Historical Overview of Emotion Recognition
2.3 Facial Expression Analysis Techniques
2.4 Machine Learning Approaches for Emotion Recognition
2.5 Deep Learning for Emotion Recognition
2.6 Challenges in Emotion Recognition
2.7 Applications of Emotion Recognition
2.8 Commercial Emotion Recognition Systems
2.9 Ethical Considerations in Emotion Recognition
2.10 Gaps in Existing Literature

Chapter Three: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Feature Extraction
3.4 Emotion Classification Algorithms
3.5 Model Training and Evaluation
3.6 Performance Metrics
3.7 Cross-validation Techniques
3.8 Parameter Tuning
3.9 Experimental Setup
3.10 Ethical Considerations

Chapter Four: System Implementation
4.1 Implementation Overview
4.2 Software and Hardware Requirements
4.3 Data Acquisition and Preprocessing
4.4 Feature Extraction Module
4.5 Emotion Classification Module
4.6 Model Training and Testing
4.7 Performance Evaluation
4.8 Results Analysis
4.9 System Optimization
4.10 Future Work

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Practice
5.4 Recommendations for Future Research
5.5 Conclusion

In summary, this thesis aims to explore the potential of computer vision for emotion recognition and develop a novel framework for accurate emotion classification based on facial expressions. The study will contribute to the existing body of knowledge in emotion recognition and provide insights into the practical applications of computer vision in understanding human emotions.

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