My thesis focuses on advancing Facial Emotion Recognition through the utilization of Vision Transformers (ViT). The primary goal is to design and implement a real-time facial expression identification system that can be seamlessly integrated into applications using webcam or smartphone cameras.
Research ViT Architectures: Investigate and analyze various Vision Transformer architectures, including but not limited to models like ViT-squeeze, to understand their strengths and limitations in the context of facial emotion recognition.
Fine-Tuning for Diverse Datasets: Implement fine-tuning techniques to adapt ViT models for diverse facial expression datasets. This involves training the model on datasets with a wide range of facial expressions, ensuring robust performance across different scenarios.
Real-Time Emotion Recognition: Develop an application that leverages the fine-tuned ViT model to perform real-time facial emotion recognition using live video streams from webcams or smartphone cameras.
Literature Review: Conduct an in-depth review of existing literature on Vision Transformers, facial emotion recognition, and related fields to establish a solid foundation for the research.
Model Selection and Fine-Tuning: Select a suitable ViT architecture based on the literature review and experiment with fine-tuning approaches to enhance the model's performance on specific facial expression datasets.
Implementation: Develop a software application that integrates the fine-tuned ViT model, allowing users to experience real-time facial emotion recognition through their webcams or smartphone cameras.
The successful completion of this thesis aims to contribute to the field of computer vision and emotion recognition, providing a practical solution for real-time facial emotion identification. The resulting application can find applications in various domains, including human-computer interaction, user experience design, and emotion-aware technology.
Stay updated on the progress of my thesis by checking the Thesis Project Repository on GitHub.