MorphCast Emotion AI
Emotion recognition SDK and web components that analyze facial cues and attention in real time to adapt media, learning, or retail experiences.
Overview
MorphCast provides client side and cloud tools that estimate emotion and attention from a face visible to the camera. Developers embed a JavaScript SDK or use no code widgets to read signals like engagement, arousal, valence, and basic expressions. The system runs on device for many scenarios which reduces latency and improves privacy by avoiding unnecessary uploads.
Creative teams use these signals to branch interactive videos, personalize learning modules, or measure attention in research studies with consent. Dashboards aggregate session data and export summaries for BI tools. Documentation covers accuracy caveats and ethical guidelines so implementers design respectful experiences, including opt in flows and storage limits.
Industry users range from training and education to advertising labs and museums. Support helps teams evaluate lighting, device, and placement constraints for reliable results.
Key features
- JavaScript SDK: Read emotions attention and basic expressions with on device execution for speed and privacy
- Web components: Drop in widgets that visualize signals and simplify rapid prototyping for non coders
- Adaptive media: Trigger branches or overlays in video and learning tools based on engagement and valence
- Consent tooling: Implement opt in prompts storage rules and transparency aligned with ethical guidance
- Dashboards: Aggregate session metrics and export summaries to reports and BI tools
- Documentation: Guidance on lighting placement and device limits to improve practical accuracy
- Hybrid processing: Use local inference and optional cloud services depending on scenario needs
- Partner support: Evaluate pilots and calibrate thresholds for specific industries and venues
Best for
- Create interactive videos that branch by audience engagement
- Personalize e learning modules to pace lessons by attention
- Run research studies that compare content impact ethically
- Design museum or retail installations that react to visitors
- Provide real time feedback for presenters during training
- Measure ad creative attention in labs with opt in cohorts
- Test lighting and camera placement to improve reliability
- Export anonymized metrics for BI dashboards
Capabilities
On device analysis
Estimate engagement valence and expressions on the client to reduce latency and keep raw frames local when possible.
Interactive logic
Use thresholds to branch media pacing and overlays so content responds to real audience signals.
Dashboards and export
Collect anonymized metrics across sessions and export summaries to BI or research tools.
Ethics and consent
Follow implementation guidance for consent storage limits and disclosures suitable for public deployments.
Frequently Asked Questions
What about privacy and consent?
Deploy opt in prompts and store only necessary metrics; keep processing on device when possible and follow the published ethical guidelines.
How accurate is detection?
Accuracy depends on lighting pose and camera quality; prototypes should test thresholds and calibrate for the venue and device mix.
Does it work in the browser?
Yes, a JavaScript SDK and web components support modern browsers and can run locally for many scenarios.
Can it export metrics to analytics?
Dashboards summarize engagement over time and allow exports for external analysis and reporting.
Is cloud processing required?
No, many cases use on device inference; cloud options exist when aggregation or heavy models are needed.
