Multimodal Biometrics (Fusion)
Combining two or more biometric signals (e.g., face + fingerprint) to boost accuracy, resilience, and spoof resistance.
Overview
Multimodal biometrics fuse signals (e.g., face + finger, iris + vein) to improve accuracy and resilience. Fusion can also harden systems against presentation attacks when combined with modality-specific PAD.
How it works
- Feature-level fusion: combine feature vectors before matching.
- Score-level fusion: normalize and combine matcher scores (e.g., sum, weighted, learned).
- Decision-level fusion: combine accept/deny votes (e.g., AND/OR rules).
Common use cases
- Border & national ID deduplication (1:N)
- High-assurance workforce access
- Financial KYC with PAD stacking
Strengths and limitations
Strengths: Higher accuracy; graceful degradation; spoof resistance.
Limitations: Cost/complexity; correlation between signals can cap gains; tuning/maintenance.
Key terms
- Score fusion: Combining matcher scores, often after normalization.
- Decision fusion: Using voting or logic rules on match outcomes.