Ear biometrics
Advisor:
Wei-Yang Lin Professor
Group Member:
陳致豪
黃笙慈
695410070
695410128
OUTLINE
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Biometric in general
Three kinds of ear biometrics
– Burge and Burger
– Victor, Chang, Bowyer, Sarkar
– Hurley, Nixon and Carter
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Related news
Reference
Ideal biometric
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Universal : each person should possess
the characteristics
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Unique
: no two persons should share
the characteristics
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Permanent : the characteristics should not
change
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Collectable: easily presentable to a sensor
and quantifiable
Biometric suitability for
authentication purpose
[1]
Ideal biometric (cont.)
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Why do we must have ear biometric?
– Many problems in face recognition remain
largely unsolved.
A
wide variety of imaging problem.
 Face is the most changing part of the
body.
– Facial expression, cosmetics , anaplasty.
Before and after
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The magic of cosmetic
Before and after (cont.)
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Anaplasty
Before and after (cont.)
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Anaplasty and cosmetic
Ear shape
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Physical biometric is characterized by the
shape of the outer ear, lobes and bone
structure
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Unique enough?
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New biometric, not widely used yet
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No applications available yet
Alfred Iannarelli
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Compared over 10,000 ears drawn from
a randomly selected sample in California
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Another study was among identical and
non-identical twins
– Using Iannarelli’s measurements
– Result: ears are not identical. Even
identical twins had similar but not
identical ears.
Alfred Iannarelli (cont.)
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The structure of the ear does not change
radically over time.
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The rate of stretching is about five times
greater than normal during the period
from four months to the age of eight, after
which it is constant until around 70 when
it again increases.[2]
Permanence of biometrics
[1]
Iannarelli’s measurements
(a) Anatomy, (b) Measurements. (a) 1 Helix Rim, 2 Lobule, 3 Antihelix, 4
Concha, 5 Tragus, 6 Antitragus, 7 Crus of Helix, 8 Triangular Fossa, 9
Incisure Intertragica. (b) The locations of the anthropometric measurements
used in the “Iannarelli System”. (Burge et al., 1998) [2]
Iannarelli’s system weaknesses
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If the first point is incorrect, all
measurements are incorrect
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Localizing the anatomical points is not
very well suitable for machine vision
– some other methods had to be found
Methods using pictures
(1/3)
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Burge and Burger (1998, 2000)
– automating ear biometrics with Voronoi
diagram of its curve segments.
– a novel graph matching based algorithm
forauthentication, which takes into
account the possible error curves, which
can be caused by e.g. lightning,
shadowing and occlusion.[3]
System step
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Acquisition
– 300*500 image using CCD camera
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Localization
– Locate the ear
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Edge extraction
– Compute large curve segments
System step (cont.)
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Curve extraction
– Form large curve segment, remove small
ones
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Graph model
– Build Voronoi diagram and neighborhood
graph
Error correct group matching
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Compute distance between graph model,
if it less than a threshold, identification is
verified.
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For high FRR due to graph model, we
can remove the noise curve and use ear
curve width.
Removal of noise curves in
the inner ear
Graph model (Burge et al.) and false curves because
of e.g. oil and wax of the ear.
Improving the FRR with ear
curve widths, an example
width of an ear curve corresponding to the
upper Helix rim  better results
Methods using pictures (2/3)
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Victor, Chang, Bowyer, Sarkar (at least 2
publications in 2002 and 2003)
– principal component analysis approach
– comparison between ears and faces
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This method is presented later with 2
cases.[4][5]
Case 1: an evaluation of face
and ear biometrics
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The used method is principal component
analysis (PCA) and the design principle
is adopted from the FERET methodology
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Null hypothesis: there is no significant
performance difference between using
the ear or face as a biometric[4]
PCA Method
RAW IMAGE
jpeg format
ear image 400x500
TRAINING
generate eigen space
record eigenvectors
PREPROCESSING
cropping with ear
centered set
landmark points
TESTING
project gallery &
probe eigenvectors
nearest neighbor
NORMALIZATION
geometric normaliz.
masking
illumination normal.
RESULTS
generate cumulative
match score
Points for normalization
Tests of research
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For faces:
– Same day, different expression
– Different day, similar expression
– Different day, different expression
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For ears:
– Same day, opposite ear
– Different day, same ear
– Different day, opposite ear
Same day, different
expression or opposite ear
ear
Different day, similar
expression or same ear
ear
Different day, different
expression or opposite ear
ear
Victor et al. research result
Experiment #
Face/Ear compared
1 Same day,
different
Expected Result
Result
Same day,
Greater variation in
Face performs
opposite ear
expressions than ears; ears better
expression
2 Different day,
similar
perform better
Different day,
Greater variation in
same ear
expression across days; ears better
expression
3 Differet day,
different
expression
Face performs
perform better
Different day,
Greater variation in face
Face performs
opposite ear
expression than ear; ears
better
perform better
Case 2: Ear and Face images
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Hypothesis:
– ear provide better biometric performance
than images of the face
– exploring whether a combination of ear and
face images may provide better performance
than either one individually[5]
Images used in research
Same kinds of sets for faces, too.
PCA, FERET
Tests for the research
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Day variation
– other conditions constant
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Different lightning condition
– taken in the same day in the same
session
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Pose variation
– 22.5 degree rotation, other conditions
constant, taken in the same day
Day variation test
Different lightning conditions
Pose variation
(22.5 degree rotation)
Results
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In this research face biometrics seem to
be better in constant conditions, ear
biometrics in changing conditions
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Multimodal biometrics face plus ear gives
the best results, why not use them?
Methods using pictures (3/3)
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Hurley, Nixon and Carter (2000, 2005)
– force field transformations for ear
recognition.
– the image is treated as an array of
Gaussian attractors that act as the source
of the force field
– according to the researchers this feature
extraction technique is robust and reliable
and it possesses good noise tolerance.
Error possibilities in ear
recognition
Possibilities to enhance ear
biometrics
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Using accurate measurements, e.g. ear curve
and upper helix rim
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Removing noise curves
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Thermograms  removal of obstacles
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Better quality cameras  more accurate
pictures
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Combined biometrics
Ear shape applications
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currently there are no applications, which
use ear identification or authentication
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crime investigation is interested in using
ear identification
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active ear authentication could be
possible in different scenarios
Related news
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A new type of ear-shape analysis could
see ear biometrics surpass face
recognition as a way of automatically
identifying people, claim the UK
researchers developing the system. [6]
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University of Leicester working with a
Northampton company have made a
breakthrough in developing a
computerized system for ear image and
ear print identification.[7]
Reference
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[1] http://www.bromba.com/faq/biofaqe.htm
[2] A. Iannarelli, Ear Identification. Forensic Identification Series.
Paramont Publishing Company, Fremont, California, 1989.
[3] Biometrics Personal Identification in Networked Society,
chapter13, Mark Burge and Wilhelm Burger
[4] Victor, B., Bowyer, K., Sarkar, S. An evaluation of face and ear
biometrics in Proceedings of International Conference on Pattern
Recognition, pp. 429-432, August 2002.
[5] Chang, K., Bowyer. K.W., Sarkar, S., Victor, B. Comparison and
Combination of Ear and Face Images in Appearance-Based
Biometrics. IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 25, no. 9, September 2003, pp. 1160-1165.
[6] http://www.newscientist.com/article.ns?id=dn7672
[7]http://www.findbiometrics.com/Pages/feature%20articles/earprint
.html