3DiVi Inc., founded in 2011, is one of the leading developers of AI and machine learning (ML) technologies for computer vision.
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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
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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)
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Developers Guide: Face Recognition Integration
For Software and Mobile App Projects