Source-linked review inside your product
Plan a review experience that keeps time-coded expression signals alongside the relevant recorded video.
Buyer
Video platforms, research tools, and workflow products with an existing review experience.
Consented input
Consented recorded media governed by your documented purpose, access, retention, and deletion policy.
API role
Process recorded video asynchronously into time-coded signals that your team can connect with the footage.
Human review and action
Your team defines who may inspect recordings, document observations, question an interpretation, and request appropriate follow-up.
Business value
Evaluate the usefulness of a contextual review aid without replacing domain expertise or judging a person.
Proof and validation
Define reviewer agreement, unusable-media handling, and access checks for the surrounding product experience.
Implementation path
- Document consent and retention responsibilities
- Track asynchronous job status
- Keep expression scores beside source footage
- Test your own review and failure handling
Explicit boundaries
- Surveillance
- Student scoring or athlete selection
- Employment, diagnosis, or suitability decisions
All workflows Integration readinessBefore you start
Write down the purpose, the people involved, and the question a reviewer will answer. Obtain appropriate consent before recording or processing media. Decide who can access footage and when your team will delete it. These are responsibilities to plan, not a claim that the API supplies a consent or retention system.
Keep the proposed use separate from judging a person's feelings, health, or suitability. For workplace and educational contexts, consider EU AI Act Article 5(1)(f). It prohibits emotion-inference AI there, except for medical or safety reasons, from February 2, 2025. Talk to counsel about your use case. This is not legal advice.
Example review loop
Choose one recorded clip and one narrow review question for your prototype. Keep the time-coded signal next to the footage in your own review experience. Ask a reviewer to describe the visible movement before offering an interpretation. Allow an unresolved question to remain unresolved rather than forcing a category verdict.
The developer API processes facial expression analysis as asynchronous jobs. The documented states are Queued, Processing, Completed, and Failed. Plan how your product will distinguish those states without displaying unfinished results as conclusions. Test the surrounding experience with held-out clips, including cases reviewers decide are unusable.
How to measure whether it helps
Reserve held-out clips before changing the review protocol. Ask independent reviewers to apply the same written codebook, then inspect disagreements. Noldus The Observer XT supports inter-rater reliability using Cohen's kappa. BORIS is a free, open-source event-logging tool for video, audio, and live observation. These are separate tools, not built-in Mental Edge integrations.
Choose agreement criteria before seeing results, and retain an uncertain or unusable category. Record whether reviewers locate useful moments without mistaking agreement for emotional truth. Report failures alongside useful observations. This is a suggested evaluation loop, not a claim of measured improvement.
What it is not for
Barrett and colleagues describe why expressions should not be read as a person's emotion from the face alone. Keep the explicit boundaries above throughout your proposed implementation. The workflow is not for hiring, promotion, diagnosis, deception detection, threat assessment, student scoring, or surveillance.