Icarus013/YodhaCameraInput

Facial Stress Prediction Model 🧠

Model Description

This model predicts stress levels (0-100 scale) from facial images using geometric facial landmark features. It's designed for real-time stress monitoring applications in healthcare, mental health assessment, and wellness tracking.

Model Type: XGBoost Regressor
Task: Facial Stress Level Regression
License: MIT (or specify your license)

Model Architecture

• Algorithm: XGBoost Regressor with gradient boosting
• Features: 9 geometric facial landmarks extracted using MediaPipe Face Mesh
  • Eye Aspect Ratio (EAR) - left, right, and average
  • Eyebrow tension - left, right, and average
  • Mouth openness
  • Jaw width and jaw drop
• Output: Continuous stress score (0-100)
  • 0-30: Low stress
  • 30-55: Moderate stress
  • 55-75: High stress
  • 75-100: Extreme stress

Training Data

• Dataset: FER-2013 (Facial Expression Recognition)
• Samples: ~4,900 images (700 per emotion category)
• Emotion Categories: 7 (happy, neutral, surprise, sad, disgust, angry, fear)
• Supervision Method: Weak supervision - emotion labels mapped to stress scores
• Split: 80% training (3,920 samples) / 20% testing (980 samples)

Emotion-to-Stress Mapping

happy    → 10  (Very low stress)
neutral  → 25  (Low stress)
surprise → 40  (Mild stress)
sad      → 55  (Moderate stress)
disgust  → 70  (High stress)
angry    → 80  (Very high stress)
fear     → 90  (Extreme stress)

Training Details

Hyperparameters

• n_estimators: 300 trees
• max_depth: 6
• learning_rate: 0.1
• tree_method: gpu_hist (GPU-accelerated) or hist (CPU fallback)
• objective: reg:squarederror
• Feature normalization: StandardScaler

Training Infrastructure

• Training time: < 2 hours on GPU / < 4 hours on CPU
• Hardware: NVIDIA GPU with CUDA support (optional but recommended)
• Framework: XGBoost 2.x with scikit-learn
• Face detection: MediaPipe Face Mesh (468 landmarks)

Performance Metrics

• Mean Absolute Error (MAE): 22.66 stress points
• Root Mean Squared Error (RMSE): 27.29
• Approximate Accuracy: 77.3%
• Target Performance: 60-70% accuracy for MVP deployment ✅ Achieved!

Training Set Performance:

• Training MAE: 6.15 stress points
• Training RMSE: 8.12
• Training Accuracy: 93.9%

Intended Use

Primary Use Cases

✅ Mental health monitoring systems
✅ Workplace wellness applications
✅ Telemedicine platforms
✅ Research in affective computing
✅ Educational tools for stress recognition

Out-of-Scope Use Cases

❌ Clinical diagnosis without professional oversight
❌ High-stakes decision making (hiring, security clearance, etc.)
❌ Surveillance or privacy-invasive applications
❌ Biased or discriminatory profiling

How to Use

Installation

pip install xgboost scikit-learn opencv-python mediapipe numpy

Inference Example

import pickle
import cv2
import numpy as np
from mediapipe import solutions

# Load model and scaler
with open('stress_predictor.pkl', 'rb') as f:
    model = pickle.load(f)
with open('feature_scaler.pkl', 'rb') as f:
    scaler = pickle.load(f)

# Initialize MediaPipe Face Mesh
mp_face_mesh = solutions.face_mesh
face_mesh = mp_face_mesh.FaceMesh(
    static_image_mode=True,
    max_num_faces=1,
    min_detection_confidence=0.5
)

# Extract features from image (using FacialFeatureExtractor class)
# ... feature extraction code ...

# Predict stress level
features_scaled = scaler.transform(features.reshape(1, -1))
stress_level = np.clip(model.predict(features_scaled)[0], 0, 100)

print(f"Predicted Stress Level: {stress_level:.1f}/100")

Limitations and Biases

Known Limitations

• Weak supervision: Model trained on emotion labels, not actual stress measurements
• Dataset bias: FER-2013 may not represent all demographics equally
• Context-agnostic: Doesn't account for situational context
• Still images only: Trained on static images, not video sequences
• Lighting sensitivity: Performance may degrade in poor lighting conditions

Bias Considerations

• Training data may have demographic imbalances
• Facial landmark detection may perform differently across ethnicities
• Should be validated on diverse populations before deployment

Ethical Considerations

⚠️ Privacy: Ensure informed consent when processing facial images
⚠️ Transparency: Users should know when stress analysis is being performed
⚠️ Accountability: Results should be reviewed by qualified professionals in clinical settings
⚠️ Fairness: Monitor for performance disparities across demographic groups

Model Files

• stress_predictor.pkl - Trained XGBoost model (main file)
• feature_scaler.pkl - StandardScaler for feature normalization
• model_metadata.pkl - Training configuration and metrics

Citation

If you use this model in your research or application, please cite:

@misc{facial_stress_predictor_2026,
  author = {Your Name/Organization},
  title = {Facial Stress Prediction Model},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/your-username/model-name}}
}

Contact & Support

• Repository: [GitHub Link]
• Issues: [GitHub Issues Link]
• Contact: [Your Email or Contact Info]

Changelog

v1.0.0 (January 2026)

• Initial release
• XGBoost regressor trained on FER-2013
• 9 facial landmark features
• Target: 60-70% accuracy achieved

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Technical Details

Dependencies:

• Python >= 3.8
• xgboost >= 2.0.0
• scikit-learn >= 1.0.0
• opencv-python >= 4.5.0
• mediapipe >= 0.10.0
• numpy >= 1.20.0

Model Size: ~XXX MB (update based on actual file size)

Inference Speed: ~XX ms per image on CPU / ~XX ms on GPU (update with benchmarks)

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License: [Specify your license - MIT, Apache 2.0, etc.]

Last Updated: January 10, 2026

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Remember to Update:

1. ✏️ Performance metrics (MAE, RMSE, Accuracy) with your actual results
2. ✏️ Contact information and GitHub links
3. ✏️ Model file sizes and inference speed benchmarks
4. ✏️ Specify your chosen license
5. ✏️ Update citation details with your information
6. ✏️ Add actual repository URLs