Computational Intelligence - February 2017 - 24

called the revised probability, or belief for evidence A) is calculated using the Bayesian profile:

0.25
0.20
Likelihood

F
P (D i)
P (D i | A G , A I ) = P (A G | D i ) P (A I | D i )
.
1444
24443 1444442444443 P (A)
Posterior
Doddingtonl s likelihood
;
Evidence

Genuine

Prior

Imposter
+2σ

0.15

Wolves/Lambs

Given an unknown traveler which can be on the watchlist
(AG) or not (AI), the Bayesian profile (Equation 2) states that
by observing the Doddington's evidence A (in the watchlist)
we are able to evaluate the risks that this traveler belongs to
one of the Doddington's categories, Di. The Bayesian profile
addresses the updating of degrees of belief upon receiving new
evidence (updating the watchlist).

0.10
7

9

11 13

0.05
Goats
0.00
-200

0

200

400
Score

600

800

1,000

figure 3 operating principle of the type-i Doddington detector for
the watchlist check. the scenario of a cooperative traveler represented by a face image.

Phase II: Risk Assessment

Camera

Cooperative

Traveler
Verification

Traveler

Watchlist
IN

Doddington
Detector

NonCooperative

Camera

Watchlist
Check

OUT

Traveler
Identification

Phase I: Risk Pre-Assessment
figure 4 integration of a doddington's detector into the two-phase
traveler risk assessment procedure of an automated border control
machine.

subject AG and imposter subject AI with respect to category
Di, where D 1 = 'Sheep,' D 2 = 'Goat,' and D 3 = 'Wolf/Lamb':
Likelihood = P (A G | D i) P (A I | D i). Doddington's evidence is
defined as the total probability:
3

P (A) = / P (A G | D i) P (A I | D i) P (D i) .
i = 1 1444442444443

(1)

Doddingtonl s likelihood

The probability P(A) is called the evidence factor; it can be
viewed as merely a scale factor that guarantees that the posterior probabilities sum to one, as all good probabilities must. The
posterior probability of the Doddington's phenomenon (also

24

Ieee ComputatIonal IntellIgenCe magazIne | February 2017

(2)

C. Computing the Doddington's Likelihood

For computation of the Doddington's likelihood we need
the probability density function (PDF) of a genuine score,
P (A G | D i) , and imposter score, P (A I | D i) , respectively,
belonging to each individual Doddington's category. Note
that the low genuine match scores indicate a risk of false
rejects (person of interest is not detected) and the high
impostor match scores indicate a risk of false accepts
(innocent person is detected as a person of interest). In this
section, the rank distribution (Type-II detector) is used as
an example because this method shows multiple Doddington's categories.
Figure 5 illustrates the PDF of each Doddington's class
using our experimental design. We divided the photos into two
groups (represented by the green and red vertical lines): highquality (a uniform background and a close proximity frontal
view), and low-quality (a complex background or a distant
frontal view). In addition, the solid-gray curves in Figure 5
bounds the extent of the probability interval for the point
probabilities (dashed-black curve). In this experiment, the
probability interval can be heuristically calculated in such a way
that the interval can be used to quantify the possible errors
within the prior and likelihood probabilities.
Let us choose subject 04882 as an example to demonstrate the calculation of the Doddington's evidence
(Figure 6) using the Type-II Doddington's detector. Calculations are given in Figure 7. By applying inference on the
causal network, it is possible to produce the conditional
probabilities of a traveler's Doddington's category. Calculation of the Doddington's evidence is performed by using
Equation 2 and the prior probabilities. In this scenario, the
traveler 04882 has an 87.6% probability of being a 'Sheep,'
an 11.7% probability of being a 'Goat,' and a 0.7% probability of being a 'Wolf/Lamb.'
Continuing with subject 04882 as an example to demonstrate the calculation of the Doddington's evidence (Figure 6)
using the Type-II Doddington's detector, this time probability
intervals will be used. The calculated results are shown in
Figure 8. Through inference, it is possible to produce the conditional probabilities of a traveler's Doddington's category. The



Table of Contents for the Digital Edition of Computational Intelligence - February 2017

Computational Intelligence - February 2017 - Cover1
Computational Intelligence - February 2017 - Cover2
Computational Intelligence - February 2017 - 1
Computational Intelligence - February 2017 - 2
Computational Intelligence - February 2017 - 3
Computational Intelligence - February 2017 - 4
Computational Intelligence - February 2017 - 5
Computational Intelligence - February 2017 - 6
Computational Intelligence - February 2017 - 7
Computational Intelligence - February 2017 - 8
Computational Intelligence - February 2017 - 9
Computational Intelligence - February 2017 - 10
Computational Intelligence - February 2017 - 11
Computational Intelligence - February 2017 - 12
Computational Intelligence - February 2017 - 13
Computational Intelligence - February 2017 - 14
Computational Intelligence - February 2017 - 15
Computational Intelligence - February 2017 - 16
Computational Intelligence - February 2017 - 17
Computational Intelligence - February 2017 - 18
Computational Intelligence - February 2017 - 19
Computational Intelligence - February 2017 - 20
Computational Intelligence - February 2017 - 21
Computational Intelligence - February 2017 - 22
Computational Intelligence - February 2017 - 23
Computational Intelligence - February 2017 - 24
Computational Intelligence - February 2017 - 25
Computational Intelligence - February 2017 - 26
Computational Intelligence - February 2017 - 27
Computational Intelligence - February 2017 - 28
Computational Intelligence - February 2017 - 29
Computational Intelligence - February 2017 - 30
Computational Intelligence - February 2017 - 31
Computational Intelligence - February 2017 - 32
Computational Intelligence - February 2017 - 33
Computational Intelligence - February 2017 - 34
Computational Intelligence - February 2017 - 35
Computational Intelligence - February 2017 - 36
Computational Intelligence - February 2017 - 37
Computational Intelligence - February 2017 - 38
Computational Intelligence - February 2017 - 39
Computational Intelligence - February 2017 - 40
Computational Intelligence - February 2017 - 41
Computational Intelligence - February 2017 - 42
Computational Intelligence - February 2017 - 43
Computational Intelligence - February 2017 - 44
Computational Intelligence - February 2017 - 45
Computational Intelligence - February 2017 - 46
Computational Intelligence - February 2017 - 47
Computational Intelligence - February 2017 - 48
Computational Intelligence - February 2017 - 49
Computational Intelligence - February 2017 - 50
Computational Intelligence - February 2017 - 51
Computational Intelligence - February 2017 - 52
Computational Intelligence - February 2017 - 53
Computational Intelligence - February 2017 - 54
Computational Intelligence - February 2017 - 55
Computational Intelligence - February 2017 - 56
Computational Intelligence - February 2017 - Cover3
Computational Intelligence - February 2017 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202311
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202308
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202305
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202302
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202211
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202208
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202205
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202202
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202111
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202108
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202105
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202102
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202011
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202008
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202005
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202002
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201911
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201908
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201905
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201902
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201811
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201808
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201805
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201802
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter12
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall12
https://www.nxtbookmedia.com