Age Estimation for Non-Regulated Age Checks

Many organizations need to confirm a customer is within a general age range without being bound by a statutory age verification mandate. Common scenarios include:

• Loyalty programs and rewards memberships with minimum age requirements set by the business rather than by regulators.
• Content platforms applying age gates in jurisdictions where no specific law applies, typically to match community standards or advertiser requirements.
• Marketing consent workflows confirming a user is above the age at which behavioral advertising becomes restricted.
• Freemium and demo accounts for online services, gaming, and streaming, where a reasonable age check at account creation is appropriate, but a full identity verification would be disproportionate to the risk.
• Account activation where age of consent is needed for a contract.

Requiring a user to scan a passport or driver’s license for a low-risk content age gate is a common cause of drop-off and churn. When an organization is not obligated to meet a regulated age standard, a fast, privacy-respecting method to confirm that the user is above a threshold age, with enough accuracy to satisfy internal policies, is a more customer-friendly approach.

Daon provides age estimation for non-regulated age checks through AI analysis of a face image. The user takes a selfie, and a proprietary AI age estimation algorithm returns an estimated age in seconds, typically within two years of the actual age and significantly more accurate than a human making the same judgment in person. No document is captured, no biometric template is stored, and the image can be discarded after the estimation completes. In most cases, 80-85% of customers can be accurately approved using this method, while the rest can be funneled into a document workflow.

The Daon age estimation solution utilizes ISO 30107-3 Level 2 tested presentation attack detection, along with CEN/TS 18099 aligned injection attack detection. These controls block attempts to defeat the age check using photos, videos, masks, or deepfakes. Orchestration allows configuration of the estimation threshold, buffer zone, and response behavior, including routing users whose estimated age falls within a set range to a document-based check, minimizing friction for most customers without giving up accuracy.

For users who carry an ISO/IEC 18013-5 mobile driver’s license, xProof also supports selective disclosure of the age attribute alone, allowing a user to prove they are over a threshold age without sharing any other identity data.