Security and Accuracy
Understanding the security features and accuracy metrics of Face Match
Security
| Risk | Security Functionality |
|---|---|
| 2D Images, Print Outs | 3D FaceScan detects points in three-dimensions, meaning 2D images and prints are quickly dismissed |
| 3D Masks, Ultra-realistic wax sculptures | A) 3D Liveness detection checks for liveness context throughout user’s face over time, meaning static or partially static faces (partially altered with wax/mask) will be detected |
| Video injection & DeepFakes | Technology detects when camera feed is being altered or user is trying to inject video |
| FaceScan alteration | FaceScans are encrypted to prevent alteration on the client side |
| Client-side device risk | A) SDK checking for risk signals on the device that would indicate likely fraud |
Accuracy
Definitions
Imagine User A is already enrolled…
False Acceptance Rate (FAR) - this is the probability that a given user B can pretend to be User A
- This is the value that matters for authentication (getting into an existing account)
False Rejection Rate (FRR) - this is the probability that User A will be rejected when trying to authenticate again
- This is the value that matters for uniqueness (preventing an existing user from falsely creating a duplicate / new account)
Accuracy:
- FAR: 1 / 125,000,000 chance (Apple’s touch ID is 1/50K, and FaceID is 1/1M)
- FRR: < 3 / 100,000
- Works with beards, transparent glasses (not sunglasses), and makeup
- 3-Dimensional modeling based on facial features results in skin-tone agnostic accuracy