Age Estimation

The model reads visual features of a live selfie that correlate with age — skin texture, facial structure — and returns an estimated age together with the probability that the person is over your age threshold. No document is collected and no identity established: the system answers how old this person roughly is, not who they are. ScamAI pairs the estimate with liveness and capture checks so a photo, replay, or injected feed cannot substitute for a real face, and the selfie is analyzed and then discarded.

No, and the difference matters for privacy and compliance. Facial recognition matches a face against a database to identify or verify a specific person, which creates and depends on biometric records. Age estimation infers one attribute — an approximate age — from the image in front of it, matches nothing, identifies no one, and stores no template. That is why estimation-based age gates are lighter for privacy teams to approve: a compliance signal without an identity system built around it.

Estimation is probabilistic, so well-designed gates act on the over-threshold probability rather than a single cutoff on the estimated age. Users whose probability of clearing the required age is high pass automatically; when it is low, the gate holds. The uncertain middle routes to a stronger check — typically document verification — so the heavier, higher-friction step is reserved for cases that genuinely need it. In practice, most users clear the gate instantly from a selfie, and the ID upload becomes the exception path.