AI Biometric age estimation
No document upload required

AI-powered Age estimation with active liveness detection. Users smile, turn their head, and they're verified. No document upload, no form filling, no friction that kills conversion rates.

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Why users choose
age estimation

Biometric age estimation delivers the perfect balance between security and user experience. Here's why platforms see 2x better completion rates compared to traditional document verification.

10 seconds approx
Age estimation verification completes in under 10 seconds. Document upload requires users to locate their ID, photograph it clearly, wait for OCR processing, and often retry due to lighting issues or damaged documents. Time matters—every extra minute increases abandonment risk.
No sensitive data uploaded
Users hesitate to upload government IDs to third-party platforms. Age estimation eliminates this concern entirely—verification uses only facial geometry analysis with no document photography. No driver's license numbers, no passport data, no addresses stored in external databases.
Always ready, no wallet needed
Age estimation works with the camera users already have in their hand. No searching for wallets, no fumbling through cards. Gaming at midnight? Streaming on the couch? Shopping on your commute? Verification happens instantly without leaving your current environment.
85-90% completion rates
Document verification sees 40-60% completion as users abandon when asked to photograph IDs. Age estimation maintains 85-90% completion by removing friction points. For platforms where conversion directly impacts revenue, this difference is measurable in the bottom line.
Works across all devices
Desktop without a webcam? QR handoff to mobile. Poor lighting for document OCR? Age estimation adapts to varied conditions. Damaged or expired ID? Not needed. The system meets users where they are rather than forcing them into specific technical requirements.
No language barriers
Document upload requires users to understand instructions for photographing IDs correctly. Age estimation uses simple visual prompts—smile, turn head—that transcend language barriers. Icons and animations guide the process, making it accessible to global audiences without extensive localization.
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Four steps age verification
10 seconds (approx) total

The entire age estimation flow completes faster than most users can find their wallet. Here's exactly what happens from the user's perspective.

Camera Permission
Browser requests camera access with a standard permission prompt. Users click "Allow" once the permission persists for future visits. On desktop without a suitable camera, the SDK automatically displays a QR code for mobile verification with real-time result sync via WebSocket.
Liveness Challenges
User follows simple prompts: smile, turn head left, turn head right. These gesture challenges prove the person is real and present not a static photo, pre-recorded video, or deepfake. The system detects head rotation angle and smile intensity in real-time, rejecting attempts to bypass verification with photographs or screens.
AI Age Analysis
Neural network analyzes facial geometry to estimate age range. This advanced AI age recognition software examines bone structure, skin texture, facial proportions, and other biometric indicators trained on millions of verified age samples.
Result & Access
If estimated age meets or exceeds your configured minimum, verification passes. The SDK stores a cryptographically signed token (default 30-day validity) in both cookie and localStorage. User is not interrupted again for 30 days. If verification fails, user sees a configurable error message with optional support contact information.
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Facial age verification online using AI liveness detection checks

Age estimation
Liveness detection

Active liveness detection is what separates Age estimation from simple facial recognition. Without liveness checks, attackers could bypass verification by holding up a photograph of someone over 18 or playing a video on a screen. Liveness detection makes these attacks impractical.

The system requires real-time responsive gestures that cannot be pre-recorded. Head rotation is analyzed in 3D space the system detects actual depth and motion vectors, not just 2D pixel shifts that could be faked with animation. Smile detection measures facial muscle movement patterns that don't appear in static images or simple video playback.

Texture analysis identifies the telltale characteristics of photographs and screens: pixel grids, moire patterns, edge artifacts, and screen reflections. Frame sequence validation ensures temporal consistency across the video stream deepfakes and morphing attacks introduce frame-to-frame inconsistencies the AI detects immediately.

Configurable thresholds let you balance security versus user experience. Stricter thresholds (higher rotation angles, more intense smiles) increase security but may fail legitimate users in poor lighting. Default settings are calibrated for optimal pass rates while blocking >95% of spoofing attempts.

Age estimation
QR handoff.

Most desktop computers lack front-facing cameras suitable for facial verification or users simply prefer not to grant camera access to their desktop browser. The QR handoff flow solves this elegantly: when the SDK detects a desktop environment or camera unavailability, it automatically generates a unique QR code tied to the current verification session.

When users scan the QR code, they can seamlessly verify their age using a smartphone camera. Their phone opens an optimized mobile verification page to complete the Age estimation liveness challenges. The moment verification completes on mobile, the result transmits back to the desktop browser via WebSocket in real-time.

The desktop widget updates instantly to show verification success no page refresh required, no manual token entry, no additional clicks. From the user's perspective: scan QR, smile at phone, desktop page unlocks. The entire handoff adds only 5-10 seconds to the verification flow while maintaining the no-document-required convenience that makes Age estimation convert so well.

QR codes are single-use and expire after 5 minutes. WebSocket channels are encrypted and session-scoped. The mobile verification page is optimized for small screens with large touch targets and clear visual feedback for each liveness challenge.

Smartphone camera scanning QR code for non-intrusive biometric age check

Privacy architecture
Zero biometric data stored

Age estimation is engineered around ephemeral processing. Facial images are analyzed on our servers and immediately discarded after the age estimation completes. No biometric database is created. No facial templates are retained. No photographs persist to disk.

Facial images never stored
Video frames from the liveness challenges are processed in server and deleted immediately after AI analysis completes. No photographs at any point in the verification flow. Database contains no facial image data verification logs store only the age range result and confidence score.
No biometric templates generated
The AI model does not create persistent biometric templates, facial embeddings, or recognition signatures. Each verification is stateless the system cannot identify returning users by their face. This architectural choice eliminates Age estimation from GDPR Article 9 biometric data processing requirements in most implementations.
IP addresses hashed with SHA-256
User IP addresses are converted to irreversible SHA-256 hashes before any database storage. The hash is useful for rate limiting and abuse detection but cannot be reversed to identify the original IP. This one-way transformation prevents user tracking while maintaining platform security.
Age stored as ranges, not exact dates
Verification logs record age ranges (18-20, 21-25, 26-30) rather than exact ages or dates of birth. This grouped categorization provides audit trail data for regulatory purposes without storing precise age information that could identify individuals when combined with other data points.
No user profiles or tracking
The system does not build user profiles, track verification history across sites, or correlate verification attempts. Each verification is independent. No cross-site tracking. No behavioral analysis. No data aggregation that could de-anonymize users through statistical inference.
GDPR Article 9 exemption by design
By not storing biometric templates or facial images, Age estimation avoids classification as biometric data processing under GDPR Article 9 in most legal analyses. No special category data is retained. Processing is ephemeral and purpose-limited. Legal basis is typically explicit consent under Article 6(1)(a) rather than the more complex Article 9 framework.

Where age estimation
performs best.

Age estimation delivers the optimal balance of assurance and conversion for platforms where users are unlikely to have documents readily available and where conversion rates directly impact revenue.

Gaming & Esports Platforms
Players verifying age for M-rated games or in-game purchases rarely have their driver's license nearby while gaming. Age estimation lets them verify instantly using their webcam or phone without interrupting the gaming session. Critical for platforms where verification friction translates directly to lost sales and player drop-off during onboarding.
Streaming & VOD Services
Users streaming adult-rated content on their TV, tablet, or phone don't want to photograph their ID just to watch a movie. Age estimation verification completes faster than finding a wallet. Especially valuable for platforms with free trials where document upload friction kills conversion before users experience the content value proposition.
E-Commerce (Alcohol/Tobacco)
Online stores selling age-restricted products need verification that doesn't sabotage checkout completion rates. Age estimation adds 10 seconds to checkout versus 2-3 minutes for document upload flows. For impulse purchases and subscription boxes, this friction difference is the margin between conversion and cart abandonment.
Social & Dating Applications
Perfect for mobile apps where users expect seamless onboarding measured in seconds, not minutes. These flexible age assurance solutions for digital apps fit naturally into mobile-first signup flows where asking for document upload would destroy activation rates. Users are already comfortable with face-based features (face filters, AR effects), making the verification feel native to the platform experience.
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Global identity & age verification
done in seconds.

Meet the strictest regulatory compliance with a frictionless, automated KYC flow your users can complete in under a minute.

Full identity age verification