UNIVERSITY OF SOUTHAMPTON COMP6211W1
SEMESTER 2 EXAMINATIONS
2014/15 BIOMETRICS
Duration 120 MINS (2 Hours)
This paper contains 6 questions
Answer THREE questions
Each question carries 33 marks.
Only University approved calculators may be used.
A foreign language word to word® translation dictionary (paper version) is
permitted provided it contains no notes, additions or annotations.
5 page examination paper
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Q1
a) Discuss the motivation for feature extraction in image processing
and give examples of features that could be useful in an image
classification.
[8 marks]
b) Describe three different metrics that could be used to measure the
distance of image examples in feature space.
[9 marks]
c) Describe the k-nearest neighbour rule by giving a pseudo code
implementation [9 marks]
d) Describe how you could build a face recognition system using
ideas in (a), (b) and (c). [7 marks]


Q2
a) Explain how shapes can be described and then classified via
Cartesian moment descriptions given by
∑∑=
x y
qp
pq yxIyxm ),(
where all symbols have their usual meanings. [11 marks]
b) Given that the centre of mass ),( yx is given by
00
10
m
mx = ,
00
01
m
my = ,
show that the following relationships hold for centralised moments:
i) 010 =µ
ii)
00
2
10
2020 m
m
m −=µ [16 marks]
c) Describe how 20µ can be used to discriminate between the image
of a ring and the image of a disc. [6 marks]


Q3
a) Describe what mean, median, and mode filters are and which one
is a linear filter. [10 marks]
b) Explain which one of the filters in Q3a performs the best in the
process of background subtraction in videos containing Gait
biometric and why? [9 marks]
c) Describe what the wavelet transform is and explain what are
advantages and disadvantages of wavelet transform over Fourier
transform. Provide two examples in biometric where wavelet
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transform is used and explain what task the wavelet transform
performs in these examples. [14 marks]
Q4

a) Describe the basic assumptions concerning a biometric including
that it should be accessible, revealable and unique.
[9 Marks]

b) Given an image of a subject as shown in Figure 1, describe
suitable approaches that could be used to recognise a subject by
information derived from the region of the nose (perhaps including
its shape). Describe a complete system which can first detect this
region and then extract features that can be used for recognition
purposes.

[24 Marks]



Figure 1 Face Image (A Moorhouse, AN Evans, GA Atkinson, The nose
on your face may not be so plain: Using the nose as a biometric, Proc.
ICDP 2009)










TURN OVER


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Q5

a) Discuss the likely performance advantages and disadvantages of
distance measures, and why statistics might be included in their
formulation.
[9 Marks]

b) The Mahalanobis distance measure is given by
( ) ( )μpμp −∑−= −1TMAHd
where the covariance matrix is formed of elements which express the
variance as
( )( )[ ]jjiiij μpμp −−Ε=∑
where Ε denotes the expected (average) value.

Describe what is meant by each term in these expressions.

Given two dimensional measurements of subjects which are in
clusters 1 and 2 (with means µ1 and µ2, standard deviations σ1
and σ2). For the three cases in Table 1, discuss whether
recognition will be possible, and the structure of the covariance
matrix in each case.

µ1 µ2 σ1 σ2
Case 1 [2,2] [4,4] 1 1
Case 2 [2,2] [4,4] 4 4
Case 3 [2,2] [2.5,2.5] 0.1 0.1
Table 1

[18 Marks]

c) For each of Cases 1,2 and 3 describe the likely outcome of using
a more conventional distance measure, such as Euclidean or
Manhattan distance.

[6 Marks]



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Q6

a) Discuss how a walking subject can be recognised from a
sequence of images
[9 Marks]

b) Describe two methods by which a feature description can be
derived, giving equations where appropriate. Describe the
advantages and disadvantages of your chosen feature description.
[18 Marks]

c) If the subject walks past a bright lamp, describe the effect on one
of your feature vectors and on your recognition process.

[6 Marks]


END OF PAPER

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