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Cybersecurity Information SecurityTop 10 Best Liveness Detection Software of 2026
Compare liveness detection software options, including Incode, with notes on key features and use cases for teams assessing identity verification tools.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Search agents gather the source set and the review record, including transcriptions of video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
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Incode is the strongest choice for regulated organizations building API-driven identity workflows because it carries liveness and document checks into authentication, account recovery, and compliance decisions, while FaceTec suits teams reusing face biometrics across mobile and browser onboarding since its 3D FaceMap templates support repeat matching.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Incode
Incode carries identity verification into later authentication, account recovery, and compliance workflows, combining document checks, facial matching, liveness checks, and configurable decisions within one enterprise platform.
Best for: Financial institutions, government agencies, healthcare organizations, and regulated digital businesses using API-driven onboarding, KYC and AML review, biometric identity proofing, authentication, account recovery, or sensitive-access checks.
FaceTec
Editor pickReusable 3D FaceMap templates support repeat face matching from guided selfie captures.
Best for: Fits when identity teams need reusable face biometrics across mobile and browser onboarding.
Daon
Editor pickIdentityX links liveness checks to identity proofing and ongoing biometric authentication.
Best for: Fits when identity teams want selfie checks within a wider enrollment and biometric authentication flow.
Comparison Table
Incode
Enterprise identity verification and orchestrationIncode combines document checks, facial matching, and passive liveness detection to verify identities and route onboarding, authentication, and compliance decisions.
Incode carries identity verification into later authentication, account recovery, and compliance workflows, combining document checks, facial matching, liveness checks, and configurable decisions within one enterprise platform.
Incode brings document verification, facial matching, and configurable identity decisions into a single workflow. Organizations can use identity signals for initial proofing and later authentication or account recovery, with decision records that trace inputs to outcomes.
Implementation typically requires engineering work to configure workflows, matching, and decision routing. For example, a bank can use Incode in remote customer onboarding to verify submitted identity evidence and route cases for approval or manual review.
- +Combines document verification, facial matching, and liveness detection in a single identity workflow.
- +Configurable orchestration can route users to approval, step-up checks, or manual review.
- +Supports both initial identity proofing and later authentication or account recovery decisions.
- –Organizations seeking complete municipal case management, benefits administration, or service delivery need a separate system; Incode focuses on identity verification.
- –Small-volume buyers seeking a self-serve verification product may be better served by a simpler tool than Incode's enterprise-focused platform.
Financial institutions
Remote customer onboarding
Routed verification decisions
Government agencies
Identity checks for access
Consistent access decisions
Show 1 more scenario
Healthcare organizations
Sensitive account recovery
Identity-informed recovery
Organizations can reuse identity signals to inform recovery decisions and additional checks.
Best for: Financial institutions, government agencies, healthcare organizations, and regulated digital businesses using API-driven onboarding, KYC and AML review, biometric identity proofing, authentication, account recovery, or sensitive-access checks.
FaceTec
API-first3D face verification and liveness detection software delivered through SDKs and identity platform integrations.
Reusable 3D FaceMap templates support repeat face matching from guided selfie captures.
FaceTec's mobile and browser SDKs let product teams embed capture in onboarding or account recovery, while backend APIs handle liveness and face matching. Organizations can deploy processing on premises when biometric data must remain within controlled infrastructure.
Guided capture asks users to follow on-screen instructions, adding interaction compared with passive selfie flows. That tradeoff suits banks verifying remote account applications, where an additional face check can justify extra capture steps.
- +Reusable 3D FaceMap templates support repeat face matching.
- +Mobile and browser SDKs pair with backend APIs.
- +iBeta Level 1 and Level 2 testing covers presentation attacks.
- –Guided capture adds steps compared with passive selfie checks.
- –Implementation requires client SDK and backend API work.
Retail banks
Remote account opening
Stronger applicant checks
Fintech support teams
Account recovery
Fewer weak recovery checks
Show 1 more scenario
Identity providers
Partner onboarding flows
Reusable face verification
SDKs and backend APIs let providers add FaceTec capture to partner applications.
Best for: Fits when identity teams need reusable face biometrics across mobile and browser onboarding.
Daon
enterpriseIdentity assurance platform with biometric verification and liveness detection for remote enrollment and login.
IdentityX links liveness checks to identity proofing and ongoing biometric authentication.
Daon’s IdentityX platform links facial liveness checks with identity proofing and biometric authentication rather than treating selfie checks as a standalone step. Teams can incorporate those checks into onboarding and later authentication flows through SDKs and APIs.
The broader platform gives organizations more workflow coverage, but implementation can involve more integration work than a focused liveness API. It suits identity teams building a shared verification flow for customer enrollment and repeat access.
- +IdentityX connects selfie checks with identity proofing and biometric authentication.
- +SDKs and APIs support integration into customer-facing identity flows.
- +Facial biometrics can serve both onboarding and repeat authentication.
- –The broader IdentityX scope can add integration work for teams needing liveness alone.
- –Implementation details depend on the selected IdentityX workflow and deployment.
Digital banking teams
Remote account enrollment
Fewer spoofed enrollments
Enterprise identity teams
Biometric sign-in
Consistent face authentication
Show 1 more scenario
Mobile application developers
Embedded selfie checks
In-app identity checks
Daon SDKs let developers place liveness checks inside existing mobile identity journeys.
Best for: Fits when identity teams want selfie checks within a wider enrollment and biometric authentication flow.
iProov
enterpriseBiometric face verification platform focused on passive and dynamic liveness detection for remote identity checks.
Flashmark analyzes how changing screen light reflects from a live face, rather than relying on a static selfie.
iProov brings screen illumination to liveness detection, using its Flashmark technology to assess whether a face is physically present. Changing colors reflected from the face help distinguish a live person from photos, replayed video, and masks.
- +Designed to resist photo, replay, and mask spoofing.
- +Web and mobile SDKs support embedded capture for onboarding and returning-user checks.
- +A managed cloud check reduces the need to operate face-analysis infrastructure.
- –Visible flashing colors may create accessibility concerns for users with photosensitivity.
- –Application teams must integrate iProov’s capture flow rather than swap in an interchangeable liveness component.
- –Camera-dependent checks exclude voice-only and other nonvisual verification channels.
Bank identity teams
Remote account opening
Spoof-resistant onboarding checks
Government digital services
Citizen account recovery
Remote identity recovery
Show 1 more scenario
Fintech fraud teams
High-risk transaction reauthentication
Reduced account takeover risk
Returning users complete a face check before sensitive account changes or transfers.
Best for: Fits when banks and public agencies need high-assurance remote face checks across web and mobile.
Jumio
enterpriseIdentity verification platform with selfie capture, face matching, and liveness checks for fraud prevention.
Linked document capture and selfie comparison within one identity-verification flow.
Jumio checks a selfie against an identity document portrait and assesses whether the selfie comes from a live person. Its passive liveness check does not require users to follow prompted facial movements.
- +Combines document portrait matching and selfie liveness in one capture flow.
- +Passive capture avoids asking users to turn or blink.
- +Mobile SDKs and APIs support embedding checks in onboarding.
- –The broader identity-verification workflow may exceed the needs of standalone face checks.
- –Implementation must account for both camera capture and document submission.
Digital banking teams
Remote account opening
Fewer disconnected checks
Online marketplaces
Seller identity onboarding
Consistent seller checks
Show 1 more scenario
Fintech operations teams
API-based onboarding
Integrated onboarding flow
Mobile SDKs and APIs let teams embed identity checks into existing account-opening flows.
Best for: Fits when digital onboarding teams need identity-document and selfie checks in one flow.
Veriff
enterpriseIdentity verification software with facial biometrics and anti-spoofing checks for online user verification.
Video selfie capture combines face movement analysis with document-portrait matching in one identity session.
Veriff fits digital businesses onboarding remote customers who need selfie liveness checks alongside identity-document review. Its video-based identity flow captures a selfie and document, compares the face with the document portrait, and checks for presentation attacks. SDKs and a REST API support web and mobile integration, while decision data can feed downstream onboarding workflows.
- +Video selfies connect face checks with document-portrait matching in one identity session.
- +SDKs and a REST API support web and mobile onboarding flows.
- +Fraud signals add context beyond the selfie and document images.
- –Identity-document steps add friction for products that only need a liveness signal.
- –Unclear camera footage or document images can interrupt remote onboarding.
Digital banking teams
Remote account opening
Fewer manual identity checks
Marketplace trust teams
Seller identity screening
More controlled seller access
Show 1 more scenario
Mobile product teams
Embedded customer verification
Integrated identity checks
Veriff SDKs let teams add selfie and document capture to mobile signup journeys.
Best for: Fits when remote onboarding teams need selfie checks integrated with identity-document review.
Shufti Pro
SMBIdentity verification software with facial authentication and liveness detection for online onboarding.
A unified KYC workflow combines face liveness, document checks, face matching, and sanctions screening through one API.
Shufti Pro bundles face liveness with document verification and identity screening, making it broader than a standalone selfie check. Its API and mobile SDKs support embedding selfie capture and face matching into customer onboarding. Teams can also route document and watchlist checks through the same identity workflow, though liveness-only deployments may need to configure around broader KYC steps.
- +Document checks and face matching can share one onboarding workflow.
- +API and mobile SDKs support embedded verification.
- +Identity screening extends the service beyond face capture.
- –Broader KYC workflows can add configuration for liveness-only deployments.
- –Public materials provide limited detail on error rates and tuning controls.
Fintech onboarding teams
New customer identity checks
Fewer separate verification integrations
Digital banks
Remote account opening
Consistent onboarding checks
Show 1 more scenario
Online marketplaces
Seller identity screening
More complete seller profiles
Route seller document checks and face matching through a shared identity workflow.
Best for: Fits when teams want face checks, document verification, and identity screening in one onboarding flow.
Didit
API-firstIdentity verification platform with face biometrics and liveness checks aimed at digital onboarding.
One verification flow combines selfie liveness, document checks, and face matching.
Didit places liveness detection inside a broader identity-check flow rather than treating facial capture as a standalone step. The flow can pair liveness checks with identity-document verification and face matching.
- +Combines facial capture, document verification, and face matching in one configurable identity flow.
- +Hosted capture screens and API access support different integration approaches.
- –Public technical materials provide limited attack-specific performance data.
- –Its broad identity-check flow may be excessive for teams needing only a liveness component.
Digital banking teams
New customer onboarding
Fewer separate verification steps
Online marketplaces
Seller identity screening
Consistent seller checks
Show 1 more scenario
Fintech developers
API-based identity checks
Connected onboarding workflow
Developers can connect verification steps to onboarding systems and process results through webhooks.
Best for: Fits when digital onboarding teams want liveness checks alongside document and face verification.
Microblink
API-firstIdentity verification SDK and API platform with face verification and liveness detection.
BlinkID combines identity-document capture and selfie liveness checks within the same verification workflow.
Microblink combines selfie liveness checks with identity-document capture in its BlinkID verification workflow. Its SDK and API options support document scanning, face comparison, and selfie checks during digital onboarding.
- +Combines identity-document capture, face comparison, and selfie checks.
- +SDK and API options support integration into digital onboarding.
- –Liveness is packaged with identity verification rather than offered as a narrow standalone check.
- –Teams must integrate the SDK or API into their onboarding flow.
Digital banking teams
New customer account opening
Combined identity checks
Fintech onboarding teams
Remote borrower enrollment
Fewer separate verification steps
Show 1 more scenario
Marketplace trust teams
Seller identity onboarding
Checked seller identities
Marketplaces can request document and selfie checks before granting sellers access to platform features.
Best for: Fits when mobile onboarding must check an identity document and selfie in one workflow.
Ping Identity
enterpriseIdentity security platform with biometric identity verification and liveness detection capabilities.
PingOne DaVinci orchestration connects verification outcomes to identity journeys without treating face checks as a standalone service.
Ping Identity fits organizations building customer identity flows in PingOne, where liveness detection can sit inside document-and-selfie verification rather than a standalone PAD stack. PingOne Verify combines identity document checks and selfie verification, while PingOne DaVinci can route verification outcomes into account-opening workflows. Its strongest fit is identity proofing connected to Ping’s broader identity services, not teams seeking a dedicated face anti-spoofing workbench.
- +PingOne Verify combines document checks and selfie verification in an identity-proofing flow.
- +DaVinci connects verification outcomes with customer identity journeys.
- +Fits organizations already operating Ping identity services.
- –Liveness detection is part of identity proofing, not a dedicated PAD workbench.
- –Organizations outside PingOne may face additional integration and administration work.
- –Fine-grained attack-class tuning is not a primary product workflow.
Fintech onboarding teams
New customer account opening
Fewer disconnected proofing steps
Retail banking teams
Remote account recovery
Connected recovery workflow
Show 1 more scenario
Digital service operators
Customer identity proofing
Joined verification and access
DaVinci routes verification results into existing customer access journeys.
Best for: Fits when existing PingOne teams need identity proofing inside customer onboarding flows.
Conclusion
After evaluating 10 liveness detection software, Incode stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Liveness Detection Software
Options differ most in how they handle Presentation Attack verification and decision control: whether you can adjust the liveness decision behavior and get trustworthy results across your actual camera conditions.
Liveness threshold tuning
Why it matters
Liveness threshold tuning changes how often real users pass versus presentation attacks get rejected. That tradeoff matters because lighting, camera quality, and user variability are usually messier in production than in a showcase.
How to check
During the demo, ask to see a threshold slider (or equivalent configuration) and rerun the same sample set. Watch whether the pass/fail boundary changes consistently, and whether the system exposes any guidance for selecting a threshold for your environment.
Attack coverage breadth
Why it matters
Attack coverage breadth matters because real attempts vary: screens, printed material, masks, and replay-style attempts don’t all fail under the same settings. If coverage is narrow, your false accept risk rises when your threat model is broader than the vendor’s examples.
How to check
Request a documented list of supported attack patterns and ask the vendor to show at least one live rejection for each major class in that list. If they won’t, ask what kinds they can’t evaluate and how they behave when those attempts appear.
Video capture and frame handling
Why it matters
Video capture and frame handling impacts stability under motion blur, low light, and shaky phones—common causes of inconsistent liveness results. If frame selection is opaque, you can’t predict how the system will behave for your workflow.
How to check
Ask for the capture settings and frame window used for the decision. Run a short clip with intentional motion/lighting changes and compare results across multiple takes to see whether the system is stable and repeatable.
Evidence output for each decision
Why it matters
Evidence output for each decision helps you understand failures and verify that the system is working as intended. Without it, you can’t triage user complaints or tune configuration based on real footage.
How to check
In the demo, enable whatever evidence artifacts are available (for example, diagnostic outputs or stored clips) and review them after both a pass and a fail. Ask what metadata is captured (timestamp, device context, configuration version) and whether you can export it for your review.
On-device versus server decision choice
Why it matters
On-device versus server decision choice affects latency, offline behavior, and what data you can keep in your environment. If the decision path is fixed, you may be forced into an architecture that doesn’t match your device and network reality.
How to check
Ask the vendor to show the decision flow for your chosen environment and what runs where. Confirm whether the decision can be made in a fully offline mode and what happens when connectivity changes mid-session.
Audit logs and access control
Why it matters
Audit logs and access control determine whether you can trace configuration changes, review events, and restrict who can modify liveness behavior. For high-friction user flows, that governance reduces operational risk.
How to check
Request an example audit log record and test whether you can filter by event type and configuration version. Ask about role-based access controls and how the system records who changed liveness threshold tuning and evidence settings.
Red flags
- Threshold settings change outcomes unpredictably across repeated runs.
- The demo shows only favorable footage, with limited evidence of rejection behavior.
- Audit logs or access controls are vague or don’t show configuration change history.
Fit by situation
- If you need consistent results across mixed lighting and camera quality, prioritize Video capture and frame handling.
- If your team must tune policy safely and review decisions later, prioritize Audit logs and access control.
- If you expect varied attack attempts beyond the demo set, prioritize Attack coverage breadth.
Frequently Asked Questions About Liveness Detection Software
How do liveness checks fit into identity-document onboarding?
When should a team choose iProov over a passive selfie flow?
Which integration options connect liveness checks to mobile and web applications?
What tradeoff comes with placing liveness inside a broader identity platform?
How should teams compare evidence about resistance to spoof attacks?
What breaks if a liveness step adds friction to onboarding?
Can existing face templates transfer when a team changes providers?
What security and admin controls should teams check before connecting a liveness service?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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