Facial recognition search in DAM: how it works and why it matters

Bynder explains the mechanics of facial recognition in asset libraries: biometric mapping, similarity scoring, and privacy boundaries. The use case is narrow (find all images of a specific person across thousands of untagged files) but the accuracy threshold is high. This only works when the DAM holds enough samples per face to train the model and when metadata practices are clean enough to validate results. Facial recognition does not replace tagging; it accelerates tagging for one specific dimension.