Computational Intelligence - February 2017 - 20

Table 1 Basic terminology and definitions of watchlist technologies.
Term

DefiniTion

CommenTs

Watchlist

system for the identification of individuals of
interest using biometric traits and related
contextual information. the Watchlist check
is also knoWn as screening and traveler risk
assessment.

it is a dynamical data structure, stored content is varying; the Watchlist check is characterized by errors such as miss-identification,
false detection, or impersonation.

Watchlist social
embedding

mapping of the Watchlist technology onto
social infrastructure, including types of biometric traits, depth of embedding, and privacy
issues.

an example is the system advise that aims at
finding and tracking relationships in data
available about the traveler from the physical
and digital World [5].

redress complaint
disposition (rcd)

metric of the Watchlist check: a complaint that
indicates that the traveler Was adversely affected and seeks relief. the traveler can be in one of
the folloWing states [12]: non-related, positive
match, or misidentified.

the rcd has a strong correlation With the
depth of social embedding. it is implemented by
updating mechanisms from physical and virtual/digital sources of information.

Watchlist architecture

a distributed netWork for: (1) collection (capture of data from the real and virtual World
for the purpose of matching); (2) matching; (3)
storing (enrolling, maintaining, and updating
biometric related contextual data); and (4) sharing (exchanging data among government agencies in accordance With national regulations).

architectural aspects reflect the social infrastructure. for example, fingerprint traits provide a relationship With forensic databases.
but facial traits are preferable because of a
common use (driver's license, various documents, social media, and surveillance systems).

Watchlist inference

a mechanism for traveler risk assessment using
Watchlist components such as biometric traits
and relevant records or meta-data.

the Watchlist check becomes complicated
technology because of potential errors related to the quality of biometric traits.

model of the Watchlist
technology

basic components for: (1) evidence accumulation and updating mechanisms; (2) processing
given the traveler evidence; and (3) a policy for
traveler risk assessment.

additional components can be integrated,
such as doddington's detector (for zeroeffort attacks) and other (non-zero-effort)
attack simulators.

Watchlist performance

quantifiable indicators used to assess hoW Well
the Watchlist is achieving its desired objectives.

examples include: number of transactions per
day, number of rcds, and operational rejection rate.

inference engine

proactive component of Watchlist technology
for dealing With the expected unWanted effects
of biometrics.

it initiates changes rather than respond to the
event after it has happened.

doddington's metric
(biometric menagerie)

four categories of recognition processes [21]: i
('sheep'), recognized normally; ii ('goats'), hard
to recognize; iii ('Wolves'), good at impersonating; and iv ('lambs'), easy to impersonate.

it is an inherent property of any biometric
trait [22]: that it is unstable and depends on
the quality of the biometric traits and features of the recognition algorithm.

doddington's detector

algorithm for the observation of doddington
classes in a biometric trait. there are three
types of detectors: type-i evaluates the deviation
betWeen the scores, type-ii analyzes the order of
the scores [21], and type-iii examines the error
rates [23].

doddington's detector is an essential part of
the Watchlist technology. each type of detector provides different results. the type of
detector can be chosen using the control criteria of the doddington phenomenon.

match score

Watchlist scenario of the comparison of tWo biometric samples belonging to the same traveler
(genuine) and to different travelers (impostor).

the knoWledge of Whether or not a match is
genuine or impostor is referred to as a
ground truth.

and a set of observable evidence (e-passport, introduced biometrics). In automated e-border technology, it is defined as
watchlist inference. Currently, no commonly agreed upon set
of factors exist on which to base an evaluation, regardless of the
watchlist purpose or requirements. We establish the criteria and
taxonomy for watchlists and focus on traveler risk assessment
based on the most vulnerable phenomenon in border crossing
known as impersonation.
The notion of an inference engine is based on the model of
traveler screening as a multi-state system (see definition in Table 1).

20

Ieee ComputatIonal IntellIgenCe magazIne | February 2017

Given a traveler, the inference engine aims at controlling the
risks of the watchlist check such as misidentification and impersonation. For this, we divide the stored biometric data with
respect to the potential risk of their use in recognition processes
using Doddington's categorization (see, for example, [21], [47]).
It defines the inference engine mechanism based on the classic
causal network paradigm with probabilistic (Bayesian) measures,
as well as another interpretation of uncertainty such as interval
(Dempster-Shafer) measures [48]. Updating the watchlist
requires updating the inference engine.



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