The main approach to obtaining the true biometric information from a
collected sample of an individual’s physiological or behavioral
characteristics is:

A.
False rejection

B.
Enrollment

C.
Digraphs

D.
Feature extraction

Explanation:
Feature extraction algorithms are a subset of signal/image processing
and are used to extract the key biometric information from a
sample that has been taken from an individual. Usually, the sample is
taken in an environment that may have noise and other conditions
that may affect the raw sample image. Neural networks are an example
of a feature extraction approach.
Answer “enrollment” refers to the process of collecting samples that are averaged and then stored
to

use as a reference base against which future samples are compared.
Answer “False rejection” refers to the false rejection in biometrics.
False rejection is the rejection of an authorized user because of a mismatch
between the sample and the reference template. Conversely,
false acceptance is the acceptance of an unauthorized user because of
an incorrect match to the template of an authorized user. The corresponding
measures in percentage are the False Rejection Rate (FRR)
and False Acceptance Rate (FAR).
For answer diagraphs refer to sets of average values compiled in the biometrics area of keystroke
dynamics. Keystroke dynamics involves analyzing the characteristics
of a user typing on a keyboard. Keystroke duration samples as well
as measures of the latency between keystrokes are taken and averaged.
These averages for all pairs of keys are called diagraphs. Trigraphs, sample sets for all key triples, can also be used as biometric
samples.