Methodology:
The first step involves conducting a thorough review of existing research on facial expression recognition and stress detection. This literature review will focus on recent advancements in deep learning techniques and their applications in affective computing, drawing from academic journals, conference papers, and the article “Deep Facial Expression Recognition: A Survey.” This review will help identify gaps in current research and inform the development of your system.
Next, the MuSE (Multimodal Dataset of Stressed Emotion) dataset will be utilized. This dataset provides a comprehensive collection of facial images and videos annotated with corresponding stress levels, making it ideal for capturing a wide range of expressions and stress indicators. The data will be normalized and augmented to enhance the model’s robustness, involving resizing images, adjusting lighting conditions, and applying transformations to simulate various real-world conditions. These preprocessing steps are crucial for training a model that can accurately recognize stress in diverse environments.
For model selection, state-of-the-art deep learning models discussed in the article “Deep Facial Expression Recognition: A Survey” will be explored. Advanced architectures such as Convolutional Neural Networks (CNNs), Residual Networks (ResNets), and models incorporating attention mechanisms will be considered. These models are well-suited for capturing the intricate features of facial expressions that correlate with stress.
The selected model will be trained on the preprocessed MuSE dataset. The data will be split into training, validation, and test sets to evaluate the model’s performance accurately. During training, the model will learn to identify subtle cues in facial expressions that indicate stress. The model’s performance will be assessed using metrics such as accuracy, precision, recall, and F1-score, providing insights into its effectiveness and highlighting areas for improvement.
To enhance accuracy and reduce overfitting, the model parameters and architecture will be fine-tuned. This optimization step is essential for developing a reliable and robust stress recognition system. Once the model achieves satisfactory performance, it will be implemented into a real-time application capable of processing live video feeds or images. This application should provide immediate feedback on an individual’s stress levels, making it practical for real-world use.
Continuous validation with new data will be conducted to maintain the system’s accuracy and reliability. This ongoing validation ensures the system remains effective in various real-world scenarios and adapts to new challenges.
Conclusion: This thesis will not only advance the field of facial expression recognition but also offer practical solutions for stress detection. By leveraging the MuSE dataset and state-of-the-art deep learning techniques, the proposed system aims to provide accurate and real-time stress recognition, contributing to better mental health monitoring and improved human-computer interactions.