01
Detection is a proposal, not an approval
Models operate on visual evidence and confidence thresholds. Profiles, occlusion, extreme scale, motion blur and unusual lighting can reduce certainty. Objects with face-like patterns can also create false detections.
A useful system makes both outcomes reviewable: missed faces and unnecessary masks. CensorFlow confirms detections across frames and exposes corrections before export.
02
Human review should be targeted
Review does not have to mean manually drawing every frame. Start with suggested gaps, track beginnings and endings, scene cuts and sections with dense or fast-moving crowds.
- Confirm every intended subject is covered.
- Remove persistent false detections.
- Check the selected blur strength.
- Inspect the actual exported file.
03
Define the acceptance rule
Teams should decide what counts as acceptable before a deadline. A sensitive HR disclosure may require a stricter rule and second reviewer than a public walking-tour edit.
Record the reviewer and approval date. That small step makes the workflow easier to repeat, audit and improve.
Apply the workflow
Review real footage locally.
CensorFlow detects and tracks visible faces on your Windows PC, then lets a person correct and approve the result before export.