Model Overview
Advanced facial expression analysis system utilizing the Facial Action Coding System (FACS) to detect and measure facial movements with clinical-grade accuracy
Key Features
- Real-time FACS analysis with sub-second latency
- Clinical-grade accuracy of 90%+ for AU detection
- Support for 44 distinct Action Units
- HIPAA and GDPR compliant processing
- Seamless API integration
- Multi-feature processing
- Gender-adaptive normalization
- Visual analysis output
- Emotion intensity measurement
- Temporal pattern analysis
- Cross-cultural validation
Performance Metrics
Response Time
Accuracy
Max Resolution
AU Detection
Supported AUs
Input Operational
Video Processing Limitations
- Requires clear video input
Minimum 720p resolution, 30fps, good lighting conditions
- Dependent on video quality for accuracy
Accuracy drops by 20% in poor lighting conditions
System Monitoring Requirements
- Best for continuous monitoring
Optimal analysis period: 30+ minutes for baseline establishment
Clinical Usage Limitations
- For medical diagnosis (Clinical Trials in progress)
Clinical trials completion expected Q1 2025
API Implementation Guide
Integration example using our Python SDK:
from dyagnosys import FacsAnalyzer
def analyze_expression(video_stream):
analyzer = FacsAnalyzer()
# Initialize real-time analysis
analyzer.start_stream(video_stream)
# Configure detection parameters
analyzer.set_detection_threshold(0.85)
analyzer.enable_temporal_smoothing(True)
# Get real-time results
while True:
aus = analyzer.get_current_aus()
emotions = analyzer.interpret_emotions(aus)
yield emotions
Video Analysis
FACS-Based Emotion Recognition
Our FACS Analysis system detects and measures facial action units to identify emotions with high accuracy. The system tracks 44 distinct Action Units (AUs) and maps them to emotional states using clinically validated patterns.
Emotion Mappings
Facial Action Units Map
Real-time Emotion Detection
Research Based Development
The FACS Analysis System is grounded in decades of psychological research and modern computer vision advances.
Foundational Research
The system builds upon Paul Ekman and Wallace V. Friesen's Facial Action Coding System (FACS), first published in their seminal work Facial Action Coding System: A Technique for the Measurement of Facial Movement (1978). This systematic categorization of facial movements into Action Units (AUs) provides the foundation for our automated analysis system.
- Ekman, P., & Friesen, W. V. (1978). Facial Action Coding System . Consulting Psychologists Press
Modern Implementation
Our implementation leverages deep learning architectures and computer vision techniques to achieve automated, real-time AU detection. The approach is validated through extensive testing and peer review, as documented in works like DISFA: A Spontaneous Facial Action Intensity Database (Mavadati et al., 2013).
- Mavadati, S. M., Mahoor, M. H., Bartlett, K., & Trinh, P. (2013). DISFA: A Spontaneous Facial Action Intensity Database . IEEE Transactions on Affective Computing
Application Areas
By analyzing vocal cues for stress and emotion, this system can enhance a wide range of industries. From healthcare to customer experience, the derived insights support decision-making, improve user satisfaction, and enable more empathetic interaction environments.
Healthcare & Professional Services
Healthcare & Telemedicine
Monitor patient stress and mood remotely, aiding early intervention and supporting personalized care plans.
Mental Health & Therapy
Identify stress patterns in vocal behavior to assist therapists, counselors, and support lines in understanding patient well-being.
Corporate Wellness & HR Analytics
Assess employee stress levels during meetings or interviews, informing HR policies and improving workplace well-being.
Customer Support & Call Centers
Detect caller frustration or confusion in real-time, enabling agents to adapt their approach and improve customer satisfaction.
User Engagement & Adaptation
Market Research & Product Testing
Understand user emotional reactions to product demos or advertisements, refining strategies and product designs.
Education & E-Learning
Adapt learning materials based on student stress or engagement levels, creating more responsive and supportive educational environments.
Virtual Assistants & Social Robotics
Enhance interaction quality by enabling systems to sense user emotions and respond empathetically in real-time.
Automotive & In-Car Systems
Monitor driver stress and emotions to adjust in-car environments or trigger safety measures, enhancing comfort and security.