ofekponzo/Face_Emotion_Insight_Recognition_Dataset

Multimodal Emotion & Physiological Analysis

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) and Facial Landmark metrics (distances and ratios). Research Question: How does the intensity of movement in different facial regions (Action Units) influence the prediction of emotional states, and is the lower face more reliable than the upper face for detecting negative emotions? Target Variable: emotion_label, which classifies each data sample into one of six emotional states: Neutral, Happy, Sad, Angry, Fear, or Surprise. Prediction Goal: To use numeric physiological and facial features to accurately classify the patient’s emotional state.

2. Data Cleaning & Preprocessing Missing Values & Duplicates: The dataset was found to be complete with no missing values and no duplicate rows. Fixing Typos: I applied the strip() method to the emotion label column to remove potential leading or trailing spaces. The data was kept as an 'object' type for readability during visualization. Scaling & Normalization: I identified a significant scaling issue (e.g., heart rate max of 113.7 vs. face_au01 max of 0.42). I performed Standardization and created a copy named "df_scaled". This ensures that the algorithm evaluates the "signal" from each data channel equally.

3. Outlier Detection & Handling Identification: Outliers were detected using Box Plots and the IQR method. Notably, there were 67 outliers in "hr_std", 51 in "face_au01", and 41 in "eda_peaks". Handling: I decided to keep all outliers rather than removing them. Justification: For my research question, these outliers represent the most clinically significant moments, such as peak emotional arousal or critical micro expressions. Removing them would create a "sterile" dataset incapable of detecting extreme emotional states.

4. Descriptive Statistics & Insights Physiological Range: The mean heart rate is 81.28, with a wide range (50 to 113) indicating significant emotional variance. Regional Intensity: The mean for a smile (face_au12) is 0.44, significantly higher than the mean for "face_au01" (0.15). This suggests that lower-face movements are numerically more "intense". Correlations:

• Physiological Cohesion: A remarkable 0.90 correlation was found between heart rate and skin conductance, allowing the algorithm to cross-reference these metrics for reliable arousal assessment. • Facial Synchronization: A 0.91 correlation between face_au06 and face_au12 identifies the "Duchenne Smile," helping the algorithm distinguish between social masks and genuine connection. • Face-Body Alignme

5. Visualizations & Research Findings

Emotion Distribution: The visualization reveals a perfectly balanced dataset with exactly 833 samples per emotion category.

Physiological Clusters: The scatter plot reveals two distinct "data islands" (Baseline and Arousal). This indicates that the algorithm should be designed to detect physiological "jumps" rather than gradual changes.

Negative Emotion Patterns:

Upper vs. Lower Face (Research Question Answer): While heart rate increases during 'Angry' and 'Fear' states, smile intensity (au12) drops into negative values. This dissonance is a primary indicator of distress.

Research Questions: Analysis & Findings:

• RQ1 (Physiological Signature): The violin plot reveals a distinct physiological profile for each emotional state, allowing the algorithm to use heart rate as a reliable primary differentiator.

• RQ2 (Upper vs. Lower Face): In negative emotions, the upper face acts as an active alarm while the lower face signifies distress through its inhibition. The algorithm can accurately predict distress by identifying the gap where brows rise but smiles vanish.

• RQ3 (Structural-Physiological Link): Physiological spikes are physically anchored to structural facial changes. This allows the algorithm to use rapid geometric shifts as a reliable proxy for internal arousal jumps.

Final Conclusion

The exploratory analysis of this multimodal dataset proves that emotional recognition is most accurate when combining both physiological and behavioral signals. By cross referencing heart rate, skin conductance, and facial action units, we can identify emotional states that might otherwise be missed by a single channel analysis.

Key Takeaways:

Reliability of Arousal: The 0.90 correlation between Heart Rate and EDA confirms that the body’s physiological response is highly cohesive, allowing the algorithm to detect stress "triggers" or sudden emotional "jumps" with high confidence.

The "Dissonance" Marker: One of the most significant findings is that negative emotions like anger and fear are best identified through regional dissonance an increase in upper-face intensity (AU01) coupled with the inhibition of lower face movement (AU12).

Clinical Significance: Retaining statistical outliers was a critical decision, as these spikes represent the most valuable clinical moments, such as breakthroughs or anxiety peaks, which are essential for the algorithm to detect in a therapeutic setting.

In conclusion, this research provides a clear "digital signature" for distress, demonstrating that while the lower face may "mask" emotions, the combined data from the upper face and internal physiology provides a transparent view of the patient's true emotional state.