Biometric Trust
Biometric Identity Verification
Consent-based face verification, liveness detection, deepfake and capture-integrity analysis — built on a privacy-first architecture that never exposes biometric information publicly.
Capabilities
A Complete Biometric Verification Suite
Each capability is part of the GlobalDoc Verify identity and biometric trust layers, and combines into a single multimodal assessment where required.
Face Verification
Assess whether a live facial capture matches a permitted reference image.
Learn more →Face-to-ID Matching
Compare a live facial capture with the identity document portrait.
Learn more →Liveness Detection
Assess that the capture is a live presentation, not a replay or a mask.
Learn more →Age Estimation
Estimate an age range from facial imagery where technically supported.
Learn more →Deepfake & Synthetic Media Analysis
Detect indicators of synthetic media and manipulation in submitted imagery.
Learn more →Video Injection & Capture Integrity
Assess how the media was captured — replays, virtual cameras and injected streams.
Learn more →Device Biometric Authentication
Use platform-supported device authentication — we never receive your biometric template.
Learn more →NFC Identity Verification
Read supported NFC-enabled identity documents — a native-mobile capability.
Learn more →Multimodal Identity Verification
Combine document, biometric, NFC and issuer evidence into one assessment.
Learn more →Advanced Biometric Technologies
Fingerprint, iris and contactless palm — future integration capabilities.
Learn more →AI Search & Education
Clear Answers About Biometric Verification
Concise definitions followed by deeper expert explanations — written in technically defensible language.
What is face verification?
Face verification is the assessment of whether a live facial capture is consistent with a permitted reference image, typically from an identity document.
Face verification combines face comparison with liveness assessment. The comparison step analyses facial characteristics between the live capture and the permitted reference image; the liveness step assesses whether the capture is a live presentation rather than a replay or a manipulated image. Results are expressed as an evidence-based assessment — Match, No Match, Inconclusive or Additional Review Required — and GlobalDoc Verify does not invent biometric confidence percentages unless a properly integrated verification technology returns a valid metric.
How does document-to-face verification work?
Document-to-face verification assesses whether a live facial capture is consistent with the portrait on the presented identity document.
The process captures the identity document, extracts the permitted reference portrait, and compares it with a live facial capture taken during the same session. Liveness checks reduce the risk of a photo or replay being used in place of a live person. The outcome is an evidence-based assessment, and inconclusive cases are escalated to expert review rather than being forced to a binary result.
What is liveness detection?
Liveness detection assesses whether a biometric capture is a live presentation rather than a replay, photograph or other presentation attack.
Liveness technologies analyse signals such as motion, texture, depth and response to guided actions to distinguish a live person from a presented artifact. Active checks require user cooperation; passive checks analyse the capture itself. Because attackers continuously evolve their techniques, liveness results are treated as risk indicators within a broader evidence-based assessment — not as an absolute guarantee.
What is age estimation?
Age estimation is the estimation of an age range from facial imagery, used as a supporting signal rather than proof of age.
Age estimation models analyse facial characteristics to produce a probabilistic age range. Because the estimate is probabilistic, it cannot establish an exact age or date of birth, and it does not replace government age verification. Its value is as a cross-check: a material inconsistency between an estimated range and a document date of birth is a useful risk indicator for expert review.
What is deepfake detection?
Deepfake detection analyses imagery and video for indicators of synthetic generation, face replacement or manipulation.
Detection systems look for artifacts left by generative models and editing tools — blending boundaries, unnatural texture patterns, inconsistent lighting and abnormal compression characteristics. Because generation techniques improve continuously, detection results are expressed as risk assessments within a broader evidence-based verification, never as a guarantee of detecting every synthetic media file.
Privacy-First Architecture
Biometric Data Is Among the Most Sensitive Information That Exists
Every biometric capability on this platform is built around explicit consent, data minimization, least-privilege access, controlled retention, deletion policies and audit logs. Raw biometric data is not stored unnecessarily, is never exposed on public verification pages, and is never sent to analytics systems.
Explicit Consent
Clear consent language explains what is processed, why, retention and who may access it — before any biometric step.
Data Minimization
We record evidence states and audit events — not raw biometric images or templates — wherever possible.
Controlled Retention
Retention and deletion policies are designed so biometric evidence does not outlive its purpose.
Never Public
Public Verification ID pages show statuses only — never biometric information of any kind.
Verify Identity With Confidence
Combine document, identity and biometric evidence into one structured, evidence-based verification assessment.