Highlights
3 years in a row named a Leader First to achieve iBeta Level 3 on iOS and Android Introducing GovFaceMatch Privacy is the architecture
01/04
01/04
Authentication

Biometric verification for returning users

Passwords get phished and codes get intercepted. Returning customers sign in with a glance, matched to the identity verified at onboarding.

0B+

identity checks processed

0 ms

to match a returning face

0%+

deepfake and injection accuracy

The problem

The password lets in anyone who has it

Credentials leak, one-time codes get intercepted, and generative AI now impersonates real faces on demand. Every login that trusts a secret is a login a criminal can pass.

0%+

of US organizations rank account takeover among their top business threats

Experian

0%

growth in fintech deepfake incidents in a single year

Deloitte, 2024

50-59%

human accuracy spotting deepfakes, barely better than chance

Cooke et al., 2024

<1 min

to generate a convincing deepfake with free AI tools

MIT Technology Review, 2023

How it works

Four steps to sign in the returning customer

The face verified at onboarding becomes the credential for every session after it. End to end in seconds, on app, web, and call center.

Use cases

One face login, every channel

Mobile app sign-in

Returning customers open the app and sign in with a glance, no password to remember.

Web sign-in

The same face login runs in the browser, with no app install required.

Call-center verification

A selfie confirms the caller in seconds, replacing knowledge questions.

Account recovery

Locked-out users recover access with their face, not an emailed reset link.

FAQ

Frequently asked questions

The questions buyers ask most about Authentication, answered straight.

Still have questions? Talk to an expert
What is face authentication?

Signing in with a selfie instead of a password or one-time code. The capture is matched 1:1 against the identity verified at onboarding, with passive liveness confirming a real person is present.

How is it different from passwords and one-time codes?

Passwords and codes authenticate a secret or a phone number, and both are stolen at scale. A face login authenticates the person, matched against the enrollment created when the account was opened.

Do returning users have to enroll again?

No. The face verified at onboarding is the credential. Every later login reuses it, with no re-enrollment and nothing new to remember.

How does it stop deepfakes at login?

Every sign-in runs the same Deepsight deepfake, injection, and device-tampering detection as onboarding, alongside iBeta-certified passive liveness, so synthetic media fails where a stolen password would have passed.

Which channels does it work on?

iOS, Android, and Web SDKs cover the app and the browser, and call-center flows confirm a caller with a selfie instead of knowledge questions. One enrollment works across all of them.

What's next

Replace the password with the person