- Identification Who goes there?
- Compare one-to-many
- Example: FBI fingerprint database
- Authentication Are you who you say you are?
- Compare one-to-one
- Example: Thumbprint mouse
- Identification problem is more difficult
- We are (mostly) interested in authentication
Enrollment vs Recognition - Enrollment phase
- Subject’s biometric info put into database
- Must carefully measure the required info
- OK if slow and repeated measurement needed
- Must be very precise
- May be a weak point in real-world use
- Recognition phase
Cooperative Subjects? - Authentication cooperative subjects
- Identification uncooperative subjects
- For example, facial recognition
- Used in Las Vegas casinos to detect known cheaters (also, terrorists in airports, etc.)
- Often, less than ideal enrollment conditions
- Subject will try to confuse recognition phase
- Cooperative subject makes it much easier
- We are focused on authentication
- So, we can assume subjects are cooperative
Biometric Errors - Fraud rate versus insult rate
- For any biometric, can decrease fraud or insult, but other one will increase
- For example
- 99% voiceprint match low fraud, high insult
- 30% voiceprint match high fraud, low insult
- Equal error rate: rate where fraud == insult
- A way to compare different biometrics
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