emotivae™ decodes emotional and attentional states from continuous video streams using neuroscience-driven models and advanced computer vision.
Rather than classifying faces or assigning rigid labels, emotivae analyzes subtle facial signals and their evolution over time, extracting probabilistic emotional insights that are more stable, reliable, and suited for real-world environments.
Designed to operate on live video streams and recorded footage, enabling both real-time monitoring and post-event emotional analysis.
Emotion analysis without personal identification by default. Facial recognition can be enabled only in regulated security contexts, when legally permitted and explicitly configured.
Most Emotion AI systems classify faces into predefined emotion labels.
emotivae™ is designed to model emotional dynamics over time, combining neuroscience-based principles with real-time and video-based AI inference.
By analyzing continuous streams rather than isolated frames, emotivae captures how emotions evolve, resulting in more stable, reliable, and context-aware insights.
The system achieves over 90% accuracy, validated across the world’s leading public facial expression and emotion datasets through continuous signal analysis.
emotivae analyzes subtle facial muscle activations and micro-expressions extracted from video streams, both live and recorded.
These involuntary signals are mapped into Facial Action Units (AUs), grounded in affective neuroscience and validated behavioral science.
Facial Action Units are translated into continuous emotional dimensions, avoiding rigid emotion labels.
These dimensions include:
• valence
• arousal
• attention
• stress and emotional tension trends
Emotional states are inferred through temporal modeling, analyzing how signals evolve, stabilize, or escalate over time.
By focusing on emotional dynamics rather than single frames, emotivae delivers more reliable, context-aware insights than snapshot-based emotion detection.
01. Robust Signal Extraction
Designed to operate reliably across varying lighting conditions, camera angles, motion, and partial occlusions commonly found in real-world environments.
02. Temporal Stability
Emotional inference is stabilized over time to reduce noise, false positives, and transient artifacts, enabling more consistent and trustworthy signals.
03. Context-Aware Interpretation
Emotional signals are interpreted within behavioral and situational context, rather than as isolated facial events, improving relevance and reducing misinterpretation.
04. Real-Time Performance
Optimized for low-latency, continuous inference on live video streams, while also supporting analysis of recorded footage for post-event insights.