The Problem

Biometric exclusion is real.
Millions have identity issues due to poor biometric quality — especially the elderly, manual laborers, and rural populations.

If the machine doesn’t recognize me, what good is this ID?

The Cost of Poor Quality

• 🌍 Nigeria: Over 10% of face images unusable
• 🇮🇳 India: Authentication failures = missed pensions, rations — some fatal
• 🇸🇴 Somila fingerprint quality is a significant issue

📉 Poor quality =

• Exclusion
• Higher fraud risk
• Costly re-enrollments
• System mistrust

Enter BQAT

BQAT – Biometric Quality Assessment Tool
🛠️ Open-source, modular platform to:

• Assess biometric sample quality
• Standardize across fingerprint, face, iris
• Flag poor captures before they’re stored
• Integrate with national systems like MOSIP

Why BQAT Matters Now

🔓 Enables inclusion for hard-to-enroll populations
📊 Drives cost savings and accuracy across ID systems
🧪 Tested in Nigeria, Philippines, Morocco
🤝 Embedded into MOSIP - discussions with UNHCR  CMU

What We’ll Deliver (Year 1)

• ✅ Refactor for scale (20k+ templates/hour)
• ✅ Launch Quality Sim Lab in Canberra
• ✅ Publish real-world biometric quality baselines
• ✅ Deliver multilingual training & quick-start kits
• ✅ Begin country integrations (2 planned)

Impact by Year 3

• 🧍 >10M people more reliably enrolled & verified
• 💸 Reduced re-enrollment & fraud costs
• 🌍 Adoption in 8+ countries
• 🤝 Governance transfer to nonprofit
• 🔓 Fully open-source (Apache 2.0), globally usable

Why Gates

Your support helps:

• Build inclusive, resilient identity systems
• Replace opaque, costly tools with open ones
• Embed quality and fairness into global ID platforms

Move from ad hoc fixes to systemic change.

Who Benefits?

👵 Elderly, farmers, rural and low-literacy communities
🧕 Women & individuals excluded by current biometric capture
🛂 Governments, NGOs, UN agencies
🧑‍🔬 Researchers and policymakers focused on inclusion