Biometric Analysis and Simulation Insight System (BASIS)

Modelling the Effects of Biometric Design Choices

Dr. Ted Dunstone — BixeLab / Biometix

Purpose

• Demonstrate how biometric system design choices affect real-world outcomes.
• Bridge technical metrics (accuracy, thresholds, modalities) with population-level impacts (enrolment success, fraud, inclusion).
• Provide an interactive teaching and decision support tool for governments, standards bodies, and system integrators.

Why It Matters

• Biometric performance depends on more than algorithms — it’s shaped by:
  • Population composition (rural vs. urban, ageing, diversity)
  • System configuration (thresholds, fusion rules)
  • Transaction scale and fraud pressure
• Quantifying these factors clarifies trade-offs between:
  • Security (false accepts, fraud)
  • Usability (false rejects)
  • Inclusion (failure to enrol)

What the Simulator Does Now

Simulation Inputs

• Population size, annual growth, transaction volume
• Segment profiles (urban/rural, fraud rate, FTE rate)
• Biometric modalities: Face, Fingerprint, Iris
• Score distributions modelled with Gaussian or Poisson processes
• Adjustable thresholds and fusion rules

Outputs and Visualisations

• Score Histograms: genuine vs impostor distributions
• ROC Curves: TPR vs FPR across thresholds
• Threshold Tables: matching rates, FRR/FAR per setting
• Annual Metrics:
  • People unable to enrol
  • Fraud attempts and successes
  • Population growth and transaction volume

Example Insights

• Effect of raising the threshold on:
  • Security ↑, Inclusion ↓
• Rural users experience slightly higher error rates → potential for bias visualisation
• Fraud rate sensitivity: high FPR thresholds can amplify real-world exposure

Implementation Overview

• Built with Dash + Mantine Components for interactive web UI
• Modular simulation core in Python (NumPy / pandas)
• Generates reproducible results (seeded random number generator)
• Outputs interactive graphs and CSV tables for export

Potential Enhancements

Analytical Extensions

• Add 1:N Identification mode (FPIR/TPIR modelling)
• Multi-year cohort enrolment tracking
• Correlated multimodal fusion (Gaussian copula)
• Introduce cost functions and optimal operating point detection

User Experience

• Interactive Dash dashboard with live parameter sliders
• Scenario comparison mode for “what-if” analysis
• Integration with real score data for calibration
• Add equity and fairness dashboards by segment and demographic

Research and Policy Applications

• Support ISO/IEC 19795 and 29794 test planning
• Model impacts for digital identity inclusion programs (e.g., MOSIP, ID4D)
• Train policymakers on trade-offs between accuracy, risk, and accessibility

Summary

Next Steps

• Update documentation and planning
• Enhance alpha build
• Validate with empirical datasets
• Package as open educational tool for biometric standards training
• Connect to real operational data for parameter import
• Publish under open-source licence (e.g., MIT)

Thank You

Biometric System Impact Simulator
📍 BixeLab | Biometix
💡 Exploring performance, inclusion & trust in identity systems