Project Overview
This project explores the relationship between physiological signals and facial micro expressions to improve emotion recognition in therapeutic settings. This research assists in determining which facial and physiological signals should be prioritized to detect clinical "misalignment" or hidden distress during therapy sessions.
1. Dataset Selection & Description
Source: The dataset is sourced from Kaggle (Face Emotion & Physiological Insight Dataset), containing pre-processed multimodal data for emotion recognition.
Size: The dataset consists of 4,998 rows and 13 features, meeting the requirement for a substantial, non-basic dataset.
Features: The dataset includes 12 numeric predictors and 1 categorical target variable:
• Physiological Measures (6 features): Electrodermal Activity (eda_mean, eda_std, eda_peaks) and Heart Rate (hr_mean, hr_std, hr_skewness).
• Facial Behavioral Measures (6 features): Intensity of Facial Action Units (face_au01, face_au06, face_au12) a…
