3DiVi Inc., founded in 2011, is one of the leading developers of AI and machine learning (ML) technologies for computer vision.
Let's talk business
Just fill in the form and we will contact you shortly
On-premise license request
Just fill in the form and we will contact you shortly
Biometric
Anti-Fraud (BAF)
Technology stack for online identity verification with NIST FRVT top-ranked face biometrics, advanced liveness detection and user session data monitoring for face authentication and identity fraud prevention in digital onboarding and eKYC services
Request demo
Fill in the form below to get access to the demo page
BAF enables businesses to deliver secure, effortless cross-platform (iOS, Android, WEB) remote customer onboarding
Streamline a swift and dependable user registration and authorization process with anti-fraud checks using BAF. It's built upon 3DiVi's NIST FRVT-validated software components: Liveness, Verification, and Matching. To further bolster the reliability of checks, session analysis is employed. This involves analyzing data from the user's environment to generate risk alerts.
Advantages
Zero trust: each user session has unique parameters that cannot be reused by fraudsters to fake
Unique face recognition: recognition algorithm based on image quality, source, designed specifically for the remote identification, top NIST rankings
Comprehensive liveness check: highly reliable protection against malicious actions using printed face images, masks, video playback, based on several algorithms
Easy embedding in browsers and apps: SDK for Web, Android and iOS
Optimized algorithms perform checks and comparisons in seconds
Scalability: configurable load variation depending on scenarios used and performance requirements
Quick start: simple API for integration into the other information systems
On-premise: 100% data-sovereign, customer-run software, no user data sent to 3DiVi
Numbers
Quality of the face recognition algorithm (biometric template extraction and comparison)
Quality of Liveness algorithm according to APCER - Attack Presentation Classification Error Rate(proportion of images containing an attack that were erroneously classified as real face images)
Quality of Liveness algorithm according to BPCER - Bona Fide Presentation Classification Error Rate (proportion of real face images that were erroneously classified as images containing an attack)
Ask an Expert
3DiVi computer vision experts are always ready to help you to find a solution matching your requirements
Ask an Expert
Just fill in the form below and we will contact you shortly to answer your questions.
Error get alias