facial biometrics

Connect with authentic customers and block fraud attacks

Verify physical presence during ID checks to prevent spoofing, deepfakes, and identity fraud — all while accelerating customer acquisition — with Shufti’s Facial Biometrics solution.

Error Rate

Less than 0.5%

Response Time

Between 2-3 seconds

Spoof Types Prevented

Over 56

Prevent deepfake attacks

Shufti’s iBeta Level 1 and 2 complaint Facial Biometrics solution prevents sophisticated spoofing attacks, including AI-generated deepfakes, to ensure secure and reliable identity verification. Our comprehensive defense framework instantly detects image alterations, use of emulators, or video stream manipulation. By using advanced liveness detection, facial matching, and 3D depth analysis, Shufti accurately detects replay attacks, printed photo spoofs, and deepfakes in real time.

Boost user confidence and comfort

Deliver a frictionless, lightning-fast user experience with no extra steps, gestures, or waiting time — all in under 1 second. Shufti’s auto-capture technology delivers fast, accurate, and hassle-free document and face capture while maintaining high-quality data for verification. The product works on any device, automatically detects blur, glare, and improper framing, and provides real-time user guidance to capture the best possible image — without relying on third-party tools.

Strong compliance and security

Shufti Facial Biometrics is an iBeta Level 1 and 2 compliant solution from the accredited NIST NVLAP independent test lab. This certification verifies Shufti’s accuracy by employing multi-layered forensic analysis to ensure that every captured image is genuine, unaltered, and verifiable in real time. Shufti delivers a total accuracy of 98.72%, with a False Acceptance Rate (FAR) of 0% and a False Rejection Rate (FRR) of 2.5%.

Biometric Verification and Liveness

Bind an identity document to a live, physically present user

Face Liveness Detection

Active Liveness Checks:
Compare 68 facial landmarks, including jawline and eye spacing, between the document photo and the selfie.
Account for aging using time-decay models, such as facial hair or wrinkles.
Passive Liveness Checks:
Analyze micro-textures such as skin pores using hyperspectral imaging.
Detect deepfakes via inconsistent eye reflections or unnatural facial features.
Check for replay attacks in the form of printed, screen captured, screenshots, and scanned faces.

Document-Selfie Matching

Facial Landmark Analysis:
Detect spoofing by analyzing real-time facial variations in response to prompts.
Validate facial authenticity through dynamic response-based security checks.
3D Face Reconstruction:
Generate 3D mesh models to validate depth consistency and counter 2D spoofs
1:N face matching:
Efficiently perform face searches across 10M+ records within seconds. If historical data is available, it can be integrated into our database, enhancing search accuracy and delivering more precise results.
Age estimation:
Uses the power of our deep neural networks to instantly estimate the age of any group with 98.72% of accuracy.
Duplicate Account Detection:
Cross-checks user-provided names and facial images against previously verified records in the system to detect duplicate accounts and prevent identity misuse.

What makes Shufti’s facial biometric authentication unique?

Comprehensive attack detection that evaluates payload integrity and detects emulators and video stream manipulation
Document-selfie matching compares 68 facial landmarks between document and live selfie
Active and passive liveness detection tailored to business requirements
Auto capture technology for fast, accurate and hassle-free document and face capture
iBeta Level 1 and 2 certified

How it works

Video replay attacK

Identifies pre-recorded video submissions attempting to mimic live users.

Screen replay attacks

Prevents screen-recorded videos from being used as live user submissions.

3D depth and shape analysis

Confirms facial depth and contours to prevent flat image attacks.

3D mask impersonations

Detects hyper-realistic 3D masks used to replicate real facial features.

eye movement simulations

Detects artificial blinking or static eyes in spoof attempts.

Light reflection analysis

Identifies inconsistent lighting or reflections, a sign of spoofing.

Facial feature obfuscation

Flags fraudsters altering their appearance with makeup, tattoos, or digital overlays.

Shadow detection

Confirms natural shadow behavior, preventing 2D photo attacks.

AI-generated deepfakes

Flags AI-created faces designed to imitate real users.

Pupil dilation monitoring

Analyzes pupil response to light to confirm real human presence.

Lip movement verification

Analyzes lip sync and movement to confirm live interactions.

Digital injection attacks

Detects fake face streams injected into the verification process.

Skin texture analysis

Differentiates between real skin and synthetic materials like silicone masks.

High-definition photo spoofs

Detects high-quality printed or digital photos used for impersonation.

Our platform has you covered

Global Coverage

Break through growth limits thanks to comprehensive reach and accessibility.

Customizable orchestration

Adapt verification workflows to fit seamlessly with your business operations.

All in One SDK

Integrate effortlessly with our versatile toolkit, compatible with various platforms and devices.

Explore the Shufti Global Trust Platform

Unlock your business potential with a verification platform built for worldwide scalability and reliability.

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User Identification and Authentication

Learn how Shufti User Identification and Authentication delivers an unsurpassed user experience that reduces onboarding timeframes and fraud exposure, while safeguarding the reputation of your brand.

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