Computational Intelligence - February 2017 - 22

Category I ('Sheep')

Category II ('Goat')

Category III ('Wolf') and Category IV ('Lamp')

Control

Traveler Who is an ImpersonatorMean and the Standard
Type-I
Traveler Who is Not a Traveler Whose Genuine Score
Impersonated Pair: An Imposter Score
Deviation of the Scores.
Detector 'Goat' or a 'Wolf/Lamb'. is Below the 2.5th Percentile.
that is Above the 97.5th Percentile.
Type-II
Traveler Who is Not a Traveler with the Lowest
Detector 'Goat' or a 'Wolf/Lamb'. Genuine Scores.

Traveler Who is an ImpersonatorImpersonated Pair. They're Members
with the Highest Imposter Scores.

Percentile Quantity and
the Order of Scores.

If the Percentage of Population Being
Impersonated ≥ T, then Traveler is
Threshold Value (T =
If
the
FRR
at
Zero-FAR
≥
T,
Type-III
Traveler Who is Not a
Considered a 'Wolf'.
0.5, 0.4 or 0.3 Values
then
the
Traveler
is
Considered
Detector 'Goat', 'Wolf, or' Lamb'.
Depending on Choice).
a 'Goat'.
If the FAR at Zero-FRR ≥ T, then
the Traveler is Considered a 'Lamb'.
figure 2 three types of doddington detectors for watchlist technology.

Doddington's detectors are based on the following metrics
[57], [60]: error counts (using FRR and FAR, useful for the
fixed match score threshold); score level (sensitive to dynamical
changes of the watchlist); ranks (how often a traveler is
returned among the top ranked results); and mean scores.
Given the watchlist, the stored data dynamically changes
via updating mechanisms (Figure 1), and therefore, Doddington's landscape changes too. With regards to the high variability of the data structure and biometric traits of both the
unknown traveler and the watchlist, Figure 2 provides some
general recommendations regarding the choice of the detector
type using the criteria of the control parameters of Doddington's landscape.
Consider three states of the watchlist life cycle modeled by
three different databases, FRGC v2.0 (Face Recognition Grand
Challenge; version 2.0, a total of 50000 recordings with 4007
subject sessions, in this set we used 39328 images of 487 subjects taken under both controlled and uncontrolled illumination) [61], ORL (Cambridge University database; 400

grey-scale images of 40 subjects) [62], and LFW (Labeled Faces
in the Wild; 13233 images of unconstrained web photos with
two or more distinct photos of 1680 subjects) [63]. Let us use
the Type-II Doddington's detector and compute the genuine
and imposter mean score for these states. The results are reported in Table 3 using the mean (μ) and deviation ( v ) metrics.
The key conclusion is that the performance of the watchlist
varies widely due to the high variability of genuine and
imposter scores.
D. Analysis and Comparisons

In our experiment, we used both the FRGC V2.0 [61], ORL
[62], and the LFW [63] databases. Five different classifications
of Doddington's categories were performed using the commercial package VERILOOK based on a neural network platform.
The results for each strategy are shown in Table 4:
Using the Type-I detector (first lines in Table 4), the 'Goats'
are not detected. As well, every traveler is placed in one of the
categories, regardless of whether he/she is on/off the watchlist.

Table 3 Dynamic properties of the watchlist: The genuine and imposter mean (μ) and standard deviation ( v ) scores for
Doddington's categories using different databases: FRGC Database (Top), ORL Database (Center), and LFW Database (Bottom).
CaTegories

frgc

orl

lfW

22

genuine sCore

imPosTer sCore

ng

vg

ni

vi

'sheep'

193.9329

115.0477

1.4103

0.5000

'goat'

92.0977

38.2766

1.1553

0.3089

'Wolf/lamb'

228.2154

101.0328

2.4586

0.7439

total

190.7298

114.3759

1.4383

0.5456

'sheep'

318.1907

159.0213

11.2094

4.8071

'goat'

111.7111

54.5101

10.1205

3.7548

'Wolf/lamb'

411.7667

118.3775

21.4121

3.4903

total

315.3468

160.4357

11.4390

5.0206

'sheep'

29.2691

21.0511

6.8675

1.5895

'goat'

0.0000

0.0000

4.8292

2.3482

'Wolf/lamb'

27.7350

20.0015

10.7873

0.3865

total

28.6454

26.5067

6.8799

1.7803

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