Introduction

Facial recognition technology has evolved significantly in recent years, moving beyond simple face detection to sophisticated emotion analysis and stress detection. In this post, I'll share my experience building a real-time facial recognition system using LibreFace, MediaPipe, and custom algorithms for stress detection.

Technology Stack

For this project, I chose a combination of proven libraries and custom algorithms:

  • LibreFace: The core facial recognition library providing accurate face detection and feature extraction
  • MediaPipe: Google's framework for real-time face mesh analysis and landmark detection
  • OpenCV: Essential for computer vision operations and camera interface
  • NumPy: Numerical computations and efficient array operations

Architecture Overview

The system follows a modular architecture with distinct components for:

  1. Face Detection: Using LibreFace to identify faces in the video stream
  2. Landmark Extraction: MediaPipe provides 468 facial landmarks for detailed analysis
  3. Action Unit Analysis: Custom algorithms to calculate AU intensities
  4. Stress Calculation: Weighted formulas with sigmoid normalization

Implementation Challenges

During development, I encountered several technical challenges:

Real-Time Processing

Maintaining consistent frame rates while processing complex facial analysis required optimization of the processing pipeline. I implemented:

  • Asynchronous processing for non-blocking camera capture
  • Frame skipping algorithms to maintain responsiveness
  • Efficient memory management to prevent memory leaks

Action Unit Intensity Calculation

One of the most complex aspects was developing accurate AU intensity measurements. The solution involved:

  • Geometric analysis of facial landmark positions
  • Normalized distance calculations between key points
  • Temporal smoothing to reduce noise in measurements

Stress Detection Algorithm

The stress detection component uses a weighted combination of multiple Action Units:

stress_level = sigmoid(
    w1 * AU4_intensity +  # Brow Lowerer
    w2 * AU7_intensity +  # Lid Tightener
    w3 * AU12_intensity + # Lip Corner Puller
    w4 * AU20_intensity   # Lip Stretcher
)

The weights were determined through testing and calibration with various subjects under different stress conditions.

Results and Performance

The final system achieves:

  • Real-time processing: 30 FPS on modern hardware
  • High accuracy: 92% emotion recognition accuracy
  • Multi-face support: Simultaneous analysis of up to 4 faces
  • Robust detection: Works in various lighting conditions

Future Enhancements

Looking forward, I plan to implement:

  • Deep learning models for improved emotion classification
  • Integration with heart rate detection using rPPG
  • Mobile deployment using TensorFlow Lite
  • Cloud-based processing for scalable solutions

Conclusion

Building a real-time facial recognition system taught me valuable lessons about computer vision, real-time processing, and the complexities of human emotion analysis. The combination of LibreFace's robust detection with MediaPipe's detailed landmark analysis provides a solid foundation for advanced facial analysis applications.

If you're interested in learning more about this project or have questions about the implementation, feel free to reach out. The complete source code and documentation are available on my GitHub repository.