Biometric verification (1:1 matching) Using Fully Homomorphic Encryption (FHE)
This demo shows Suraksh.AI's biometric verification solution under FHE.
Scenario 1: Verifying an enrolled subject. For this scenario, the reference and probe should be from the same subject. Expected outcome: ✔️ Match
Scenario 2: Verifying an enrolled subject with high recognition threshold. For this scenario, the reference and probe should be from the same subject and increase the recognition threshold. Expected outcome: ❌ No Match
Scenario 3: Verifying a non-enrolled subject. For this scenario, choose a probe not enrolled. Expected outcome: ❌ No Match
Scenario 4: Verifying a non-enrolled subject with low recognition threshold. For this scenario, choose a probe not enrolled and lower the recognition threshold. Expected outcome: ✔️ Match
Phase 1: Enrollment
Step 1: Upload or select a reference facial image for enrollment.
Step 2: Generate reference embedding.
Choose a face recognition model
Facial embeddings are INVERTIBLE and lead to the RECONSTRUCTION of their raw facial images.
Example:
Facial embeddings protection is a must! At Suraksh.AI, we protect facial embeddings using FHE.
Step 3: 🔐 Generate the FHE public and secret keys.
Choose a security level
Step 4: 🔒 Encrypt reference embedding using FHE.
Phase 2: Authentication
Step 1: Upload or select a probe facial image for authentication.
Step 2: Generate probe facial embedding.
Step 3: 🔀 Generate protected probe embedding.
Step 4: 🔒 Compute biometric recognition decision using the threshold under FHE.
