CBP Biometric Fusion and Multimodal Systems

Question 1

Which level of biometric fusion combines raw data or feature vectors from multiple sensors before matching occurs?

Accepted Answer

Feature-level fusion

Answer

Feature-level fusion operates on the feature vectors extracted from biometric samples prior to matching, merging them into a single combined feature set.

Question 2

What is the primary advantage of using a multimodal biometric system over a unimodal system?

Accepted Answer

Improved accuracy and resistance to spoofing

Answer

Multimodal systems combine multiple biometric sources, improving overall accuracy, reducing false accept/reject rates, and making spoofing significantly more difficult.

Question 3

In decision-level fusion, the AND rule requires that:

Accepted Answer

All matchers accept the identity claim

Answer

The AND rule in decision-level fusion grants access only when every individual matcher independently accepts the claim, maximizing security at the cost of higher false rejection.

Question 4

Which fusion strategy is typically most difficult to implement due to incompatible feature spaces across different biometric modalities?

Accepted Answer

Feature-level fusion

Answer

Feature-level fusion is the most challenging because feature vectors from different modalities (e.g., fingerprint minutiae and iris codes) often have different dimensionalities and statistical properties.

Question 5

Score normalization is most commonly applied before which type of fusion?

Accepted Answer

Score-level fusion

Answer

Score-level fusion requires normalization to map match scores from different matchers onto a common scale before combining them, since raw scores from different systems are not directly comparable.

(CBP) Certified Biometrics Professional Practice Test

(CBP) Certified Biometrics Professional Practice Test

CBP Biometric Fusion and Multimodal Systems