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.