Face Age Estimation
In-house technology delivering highly accurate, unbiased across demographics, and privacy-preserving age assurance.
In-house technology delivering highly accurate, unbiased across demographics, and privacy-preserving age assurance.
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Our face age estimation technology applies advanced AI models to analyze facial features and predict age ranges with high accuracy. It safeguards privacy, prevents spoofing, and supports compliance while keeping the user experience seamless.
Face is detected on-device, then a privacy-safe facial map is created without identifying who the person is.
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The model analyzes age-related facial features like skin texture and landmarks to estimate an age range, not a precise identity.
Liveness and deepfake checks verify a real, present person, blocking replays, screen attacks, and AI‑generated faces.
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Results return instantly as an age estimate and pass/fail against region/industry-specific policy thresholds, optimized for low latency.
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Data minimization by design, no face templates are stored by default, and processing can run on-device or regionally to meet compliance needs.
Trained on millions of diverse, compliant images, our technology achieves 99.8% benchmark accuracy with no demographic bias, high group-specific precision, and milliseconds speed.
Achieve fast and frictionless age assurance with outstanding accuracy.
Age assurance
What it is: the process of determining a user’s age or confirming whether they fall above or below a required threshold. It helps ensure compliance, protect minors, and enable age-appropriate access.
How it is used:
Age Gating: ensures users meet legal age requirements before accessing products or services.
Age Segmentation: groups users into age brackets to deliver tailored experiences.
Age discrepancy
What it is: the process of identifying mismatches between a user’s estimated age from a selfie or face scan and the date of birth shown on their identity document.
How it is used: to detect tampered or forged identity documents, expose synthetic identities created from stolen data, and flag fraudsters whose claimed age on an ID does not align with their real facial appearance. This strengthens defenses against identity theft and large-scale fraud attempts.
Incode’s age estimation models are NIST-certified and top ranked in MAE benchmarks across all age groups and demographics, tested on millions of images for accuracy.
success rate in spotting and blocking
deepfakes and injections during face capture.
Verifications processed in 20 milliseconds
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BIS PAS 1296 (OAC)
certification for Age Estimation, validating performance and compliance with global standards.
ISO (30107-3) Certified against biometric spoofing and presentation attacks.
Recognized as top age estimation solution by Liminal and best ranked by clients through G2.
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Personalize and simplify your services with accurate facial recognition, built on Incode’s advanced ML models.
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