Ear biometrics

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Ear biometrics

• Advisor
• Wei-Yang Lin Professor
• Group Member
• ??? 695410070
• ??? 695410128

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OUTLINE

• Biometric in general
• Three kinds of ear biometrics
• Burge and Burger
• Victor, Chang, Bowyer, Sarkar
• Hurley, Nixon and Carter
• Related news
• Reference

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Ideal biometric

• Universal each person should possess
  
• the characteristics
• Unique no two persons should share
  
• the characteristics
• Permanent the characteristics should not
• change
• Collectable easily presentable to a sensor
• and quantifiable

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Biometric suitability for authentication purpose
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Ideal biometric (cont.)

• 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.

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Before and after

• The magic of cosmetic

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Before and after (cont.)

• Anaplasty

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Before and after (cont.)

• Anaplasty and cosmetic

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Ear shape

• Physical biometric is characterized by the shape
  of the outer ear, lobes and bone structure
• Unique enough?
• New biometric, not widely used yet
• No applications available yet

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Alfred Iannarelli

• Compared over 10,000 ears drawn from a randomly
  selected sample in California
• Another study was among identical and
  non-identical twins
• Using Iannarellis measurements
• Result ears are not identical. Even
• identical twins had similar but
  not
• identical ears.

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Alfred Iannarelli (cont.)

• The structure of the ear does not change
  radically over time.
• 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

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Permanence of biometrics
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Iannarellis 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
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Iannarellis system - weaknesses

• If the first point is incorrect, all measurements
  are incorrect
• Localizing the anatomical points is not very well
  suitable for machine vision
• some other methods had to be found

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Methods using pictures (1/3)

• Burge and Burger (1998, 2000)
• automating ear biometrics with Voronoi diagram of
  its curve segments.
• a novel graph matching based algorithm for
  authentication, which takes into account the
  possible error curves, which can be caused by
  e.g. lightning, shadowing and occlusion.3

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System step

• Acquisition
• 300500 image using CCD camera
• Localization
• Locate the ear
• Edge extraction
• Compute large curve segments

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System step (cont.)

• Curve extraction
• Form large curve segment, remove small ones
• Graph model
• Build Voronoi diagram and neighborhood graph

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Error correct group matching

• Compute distance between graph model, if it less
  than a threshold, identification is verified.
• For high FRR due to graph model, we can remove
  the noise curve and use ear curve width.

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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.
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Improving the FRR with ear curve widths, an
example
width of an ear curve corresponding to the upper
Helix rim ? better results
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Methods using pictures (2/3)

• Victor, Chang, Bowyer, Sarkar (at least 2
  publications in 2002 and 2003)
• principal component analysis approach
• comparison between ears and faces
• This method is presented later with 2 cases.45

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Case 1 an evaluation of face and ear biometrics

• The used method is principal component analysis
  (PCA) and the design principle is adopted from
  the FERET methodology
• Null hypothesis there is no significant
  performance difference between using the ear or
  face as a biometric4

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PCA Method
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Points for normalization
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Tests of research

• For faces
• Same day, different expression
• Different day, similar expression
• Different day, different expression
• For ears
• Same day, opposite ear
• Different day, same ear
• Different day, opposite ear

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Same day, different expression or opposite ear
ear
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Different day, similar expression or same ear
ear
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Different day, different expression or opposite
ear
ear
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Victor et al. research result
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Case 2 Ear and Face images

• 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 individually5

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Images used in research
Same kinds of sets for faces, too. PCA, FERET
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Tests for the research

• Day variation
• other conditions constant
• Different lightning condition
• taken in the same day in the same session
• Pose variation
• 22.5 degree rotation, other conditions constant,
  taken in the same day

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Day variation test
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Different lightning conditions
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Pose variation (22.5 degree rotation)
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Results

• In this research face biometrics seem to be
  better in constant conditions, ear biometrics in
  changing conditions
• Multimodal biometrics face plus ear gives the
  best results, why not use them?

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Methods using pictures (3/3)

• 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.

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Error possibilities in ear recognition
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Possibilities to enhance ear biometrics

• Using accurate measurements, e.g. ear curve and
  upper helix rim
• Removing noise curves
• Thermograms ? removal of obstacles
• Better quality cameras ? more accurate pictures
• Combined biometrics

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Ear shape applications

• currently there are no applications, which use
  ear identification or authentication
• crime investigation is interested in using ear
  identification
• active ear authentication could be possible in
  different scenarios

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Related news

• 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
• 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

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Reference

• 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?iddn76
  72
• 7http//www.findbiometrics.com/Pages/feature20a
  rticles/earprint .html