The problem
Digital services need to assess both whether a face matches an identity and whether a person is physically present, while keeping the integration usable.
Computer vision & identity
IM.ID
Facial verification, liveness analysis, and integration for protected digital services.
↳ Created with my teamAt a glance
IM.ID connects facial verification with liveness analysis for protected digital services. Intended users include people accessing ministry systems and developers integrating identity checks. Our team’s documented work combines face matching, spoof analysis, GPU-backed services, mobile capture, SDK integration, and administrative analysis. Separating mobile capture from server processing helps describe the integration and investigate its behavior. Matching a face and assessing physical presence are distinct parts of the workflow. Results depend on capture conditions and model thresholds; this portfolio does not claim certified biometric accuracy or universal resistance to spoofing.
Created with my team
The projects in this portfolio are a shared effort. Product decisions, architecture, engineering, and delivery belong to the people who build together. My responsibilities vary from project to project.
Who it serves
Users of ministry information systems and developers integrating identity verification.
What we build
Our team’s work brings together facial matching, liveness/spoof analysis, GPU-backed services, mobile applications, and SDK integration.
Engineering decisions
Mobile capture and server-side processing are separate concerns. Administrative controls and verification analysis support investigation of model behavior and integration issues.
Capabilities
Face matching, liveness analysis, obstruction checks, mobile capture, SDK integration, and administrative analytics.
Context & limitations
Verification depends on capture conditions and model thresholds. The interface illustrates the workflow; the portfolio makes no biometric accuracy or certification claim.
Technical foundation