Shufti-Sphere-Website-Banner
burger-menu cross-icon-2

Resources

us

216.73.216.240

Face Verification

Face Verification Software for Secure Identity Verification

Stop sophisticated spoofing before it compromises your platform. Shufti's proprietary models, trained and maintained entirely in-house, identify deepfakes, printed masks, and injected video streams through layered liveness checks and real-time frequency-domain analysis.

PERFORMANCE YOU CAN QUANTIFY

Independently Validated Results

<0.001
False Match Rate (DHS RIVR 2025)
~99%
True Acceptance Rate
10M+
1:N Face Search (In Under One Second)
Trusted By 2000+ Clients Worldwide
cashew gemone HERO Gaming Bitget IronFX PENN National Rakuten Witzeal Noteris banxy

CERTIFIED LIVENESS FOR REGULATED ONBOARDING

End-to-End Face Verification Service Built to Defeat Sophisticated Fraud

Smart Face Liveness

Passive Liveness

Confirms a real human is present using texture, reflections, and depth cues. Shufti holds iBeta PAD Level 3 passive liveness certification on both Android and iOS.

Passive Liveness

Active Liveness

Prompted actions such as head movement or gestures confirm physical presence. Shufti uses 3D depth, motion, and texture analysis through the same owned engine, audit trail, and certification coverage as passive mode.

Active Liveness

Deepfake, Replay & Injection Defence

AI deepfakes, replays, emulators, virtual cameras, and injected streams are detected in real time with layered liveness signals. Built and maintained by Shufti’s in-house data science teams, not licensed from a third party.

Deepfake, Replay & Injection Defence
Passive Liveness Active Liveness Deepfake, Replay & Injection Defence Device & Session Signals

Multi-Stream Detection Architecture

Parallel RGB + DCT Processing

Shufti analyses visual inputs with high-frequency DCT signals to catch generative artefacts RGB-only systems can miss. Multi-signal decisioning is built in-house through Shufti’s owned detection stack and data science teams.

Parallel RGB + DCT Processing

Region-Based Facial Analysis

Shufti analyses eyes, nose, mouth, and skin boundaries separately to detect localised manipulation full-frame checks can miss, even on partial or cropped faces. Its owned model pipeline lets each regional model update independently without production disruption.

Region-Based Facial Analysis

Resolution-Aware Models

Shufti uses separate models for low-res and high-res inputs, so compressed mobile captures get consistent detection quality with no minimum quality threshold. Built for mobile-first, low-bandwidth markets across Southeast Asia, MENA, and Latin America.

Resolution-Aware Models
Parallel RGB + DCT Processing Region-Based Facial Analysis Resolution-Aware Models Fuzzy Matching

Image Forensics

Brings Hidden Tampering to the Surface

Shufti enhances areas where fraud leaves traces: edge seams, texture mismatches, and abnormal noise. Its AI detects near-invisible tampering on low-quality images, beyond surface-level checks.

Brings hidden tampering to the surface

Reads the Image, Not the Metadata

Shufti detects fraud from image content itself, even when metadata, camera signatures, or timestamps are removed. In-house models hold up on repackaged or re-saved files, without third-party dependency.

Reads the image, not the metadata

Attention map for every decision

Every flagged image includes a colour-coded attention map showing natural, moderate, and high-anomaly zones. Explainable AI shows fraud teams where and why risk was flagged, supporting EU AI Act readiness.

Attention map for every decision
Brings hidden tampering to the surface Reads the image, not the metadata Attention map for every decision Device & Session Signals

Continuous Learning Pipeline

Synthetic Training Data Generation

Shufti uses multiple deepfake frameworks to generate attack-like training data, including face swaps, compression, and replay artefacts. Its owned training pipeline absorbs new attack patterns without third-party dependency.

Synthetic Training Data Generation

Retrospective Biometric Audit

Rescan past biometric records with current deepfake models to flag high-risk accounts for review, re-verification, or closure. Continuous biometric risk monitoring after onboarding, aligned with AMLD6 and equivalent frameworks.

Retrospective Biometric Audit

Modular Production Updates

Update models independently without disrupting live services. Shufti monitors production shifts to catch degradation early, resolve issues faster, and reduce downtime risk.

Modular Production Updates
Synthetic Training Data Generation Retrospective Biometric Audit Modular Production Updates Fuzzy Matching
Built for Compliance: Go live in minutes with our flexible API and lightweight SDKs

Single API, Seamless Integration

Build fully customisable verification flows with seamless backend integration.

  • Gain full control by customising verification flows end-to-end.
  • Integrate seamlessly with your backend for quick implementation.
  • Design flexible verification journeys tailored to your users.
Explore API Docs
RESTful API img

Launch a native verification experience in your mobile app within minutes.

  • Launch native verification within minutes on iOS or Android.
  • Use ready-made UI with camera, capture, and real-time feedback.
  • Customise flows to fit seamlessly into your mobile app.
Explore SDKs Docs
Lightweight SDK image

With KYC Journey Builder, create personalised verification journeys without writing a single line of code.

  • Customise your journey effortlessly with drag-and-drop functionality.
  • Instantly see how your verification flow looks for your users.
  • Easily connect with Hosted Verification for a consistent, branded experience.
Explore More
On-Premise Deployment image

Run Shufti within your own identical-capability infrastructure for maximum data control and privacy.

  • Keep all sensitive information in-house to meet strict governance and data residency requirements.
  • Keep sensitive information fully private and secure in-house.
  • Deploy in highly regulated sectors without compromising compliance.
Contact Sales
On-Premise Deployment image

Where AI Face Verification Fits Best

Built For Regulated & High-Risk Businesses

Built For Regulated & High-Risk Businesses

Verify the seller is real at onboarding, then prevent re-joins with duplicate detection and optional 1:N matching across verified records.

Recognized as a G2 Leader, Summer 2026

Shufti has earned multiple G2 distinctions this season, rated by real users and trusted by enterprises worldwide.

See All Reviews
G2 Summer 2026 Leader G2 Summer 2026 Momentum Leader G2 Users Love Us G2 Summer 2026 Regional Leader, Asia G2 Summer 2026 Regional Leader, Asia Pacific
."

Don’t just take our word for it, hear from our customers

The Confidence Our Clients Share

The future of digital identity is defined by trust, interoperability, and regulatory alignment, so our partnership with Shufti reinforces DevCode Identity’s commitment to supporting our global customers with the most secure, best-in-class, compliant identity verification solutions available today.

Combining our Conversion Driven Compliance Orchestration Platform with Shufti’s global KYC and IDV capabilities allows our customers not only to navigate complex regulatory demands but also to maintain a seamless customer onboarding experience with the highest achievable conversion rates.

Mark Knighton
Chief Global Development Officer -
Global Alliances, DevCode
"

Everything you need to know in one place

Frequently Asked Questions

Is face verification only for onboarding with an ID?

No. Use it anywhere fraud shows up, login challenges, account recovery, withdrawals, payouts, and resets. It can also match to an ID photo when required.

How does face verification detect deepfakes?

Face verification systems apply multiple detection layers. Shufti uses RGB colour-space analysis combined with DCT frequency-domain decomposition to identify synthetic artefacts in the captured image. This detects AI-generated faces, digitally manipulated photos, and injected data streams that bypass the camera.

What levels of iBeta liveness certification does Shufti provide?

Shufti provides iBeta-certified liveness detection across Level 1, Level 2, and Level 3 testing. These levels assess presentation attack detection against increasingly advanced spoofing attempts, from basic printed attacks to higher-quality replays, masks, and more sophisticated presentation attacks.

Can historic KYC selfies be rescanned for deepfakes?

Yes. Shufti's retrospective deepfake audit tool, available through AWS Marketplace, rescans biometric records collected during previous onboarding against current-generation deepfake detection models. This identifies selfies that passed verification at the time but would fail under today's detection standards.

What happens when a user wears glasses, a headscarf, or has a facial difference?

The 68-landmark facial mapping and 3D depth reconstruction are designed to work across a range of facial presentations. iBeta Level 2 testing includes demographic diversity requirements.

Prove the Person Is Real Before Risk Moves Forward

Stop deepfakes, masks, and injected video streams with face verification built on Shufti’s owned stack. iBeta Level 3 certified, DHS RIVR 2025 validated, and deployable on-premise or in sovereign cloud.