Ear Biometrics for Human Identification Based on Image Analysis

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Ear Biometrics for Human Identification Based on
Image Analysis

• Michal Choras
• Image Processing Group
• Institute of Telecommunication
• ATR Bydgoszcz, Poland
• Presentation for ELCVIA Journal

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INTRODUCTION TO HUMAN IDENTIFICATION

• Disadvantages of the traditional methods
• hard to remember
• easy to loose
• lack of security
• cards and keys are often stolen
• passwords can be cracked
• invasiveness

• Traditional methods
• PINs
• Logins Passwords
• Identification Cards
• Specific Keys

• Identification by something that people know or
  possess.

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INTRODUCTION TO BIOMETRICS

• Definition automatic identification of a living
  person based on physiological or behavioural
  characteristics.
• Identification by who people are!
• All the biometrics methods can be divided into

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INTRODUCTION TO BIOMETRICS
PHYSIOLOGICAL
BEHAVIOURAL

• Hand
• hand geometry
• hand veins geometry
• fingerprints
• palmprints
• Head
• eye
• iris
• retina
• face recognition
• ear

• Most popular methods
• voice identification
• signature dynamics
• keystroke dynamics
• motion recognition

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GENERAL MOTIVATION FOR EAR BIOMETRICS

• WHERE DO WE HEAD ?

passive physiological biometrics
FACE AND EAR BIOMETRICS MIGHT BE THE ANSWER
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FACE BIOMETRICS GENERAL OVERVIEW

• Passive physiological method.
• Natural humans recognize people by looking at
  their faces.
• Fast development of new algorithms.
• Still many unsolved problems including
  compensation of illumination changes and pose
  invariance.
• Some popular methods
• 2D geometry,
• 3D models,
• PCA, ICA, LDA,
• Gabor Wavelets,
• Hidden Markov Models.

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EAR BIOMETRICS

• Human ears have been used as major feature in the
  forensic science for many years.
• Earprints found on the crime scene have been used
  as a proof in over few hundreds cases in the
  Netherlands and the United States.
• Human ear contains large amount of specific and
  unique features that allows for human
  identification.
• Ear images can be easily taken from a distance
  and without knowledge of the examined person.
• Therefore suitable for security, surveillance,
  access control and monitoring applications.

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PASSIVE BIOMETRICS EAR vs. FACE

• Ear does not change during human life, and face
  changes more significantly with age than any
  other part of human body.
• cosmetics, facial hair and hair styling, emotions
  express different states of mind like sadness,
  happiness, fear or surprise.
• Colour distribution is more uniform in ear than
  in human face, iris or retina.
• not much information is lost while working with
  the greyscale or binarized images.
• Ear is also smaller than face, which means that
  it is possible to work faster and more
  efficiently with the images with the lower
  resolution.
• Ear images cannot be disturbed by glasses, beard
  nor make-up. However, occlusion by hair or
  earrings is possible.

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SAMPLE EAR IMAGES FROM OUR DATABASE
Ears differ at a first glance. We lack in
vocabulary - humans just dont look at ears.
easy ear images
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SAMPLE EAR IMAGES FROM OUR DATABASE
difficult ear images
Removing hair for access control is simple and
takes just single seconds.
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EAR BIOMETRICS OBVIOUS APPROACH
The method based on geometrical distances.
How to find specific points?
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IANNARELLIS MANUAL MEASUREMENTS

• The first, manual method, used by Iannarelli in
  the research in which he examined over 10000 ears
  and proved their uniqueness, was based on
  measuring the distances between specific points
  of the ear.
• Iannarelli proved that even twins ears are
  different.
• The major problem in ear identification systems
  is discovering automated method to extract those
  specific, key points.

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EAR BIOMETRICS KNOWN METHODS

• Neighborhood graphs based on Voronoi diagrams.
• Burge M., Burger W., Ear Recognition, in
  Biometrics Personal Identification in Networked
  Society (eds. Jain A.K., Bolle R., Pankanti S.),
  273-286, Kluwer Academic Publishing, 1998.
• Burge M., Burger W., Ear Biometrics for Machine
  Vision, Proc. Of 21st Workshop of the Austrian
  Association for Pattern Recognition, Hallstatt,
  Austria, 1997.
• Burge M., Burger W., Ear Biometrics in Computer
  Vision, IEEE ICPR 2000.

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EAR BIOMETRICS KNOWN METHODS

• Ear Biometrics based on Force Field
  Transformation
• Hurley D.J., Nixon M.S., Carter J.N., Automatic
  Ear Recognition by Force Field Transformations,
  IEE Colloquium on Biometrics, 2000.
• Hurley D.J., Nixon M.S., Carter J.N., Force Field
  Energy Functionals for Image Feature Extraction,
  Image and Vision Computing Journal, vol. 20, no.
  5-6, 311-318, 2002.

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EAR BIOMETRICS KNOWN METHODS

• Ear Biometrics based on Force Field
  Transformation
• Application of force field transformation in
  order to find energy lines, wells and channels as
  ear features.

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EAR BIOMETRICS KNOWN METHODS

• Ear Biometrics based on PCA and eigenears
• Chang K., Victor B., Bowyer K.W., Sarkar S.,
  Comparison and Combination of Ear and Face Images
  for Biometric Recognition, 2003.
• Victor B., Bowyer K.W., Sarkar S., An Evaluation
  of Face and Ear Biometrics, Proc. of Intl. Conf.
  on Pattern Recognition, I 429-432, 2002.
• Chang K., Victor B., Bowyer K.W., Sarkar S.,
  Comparison and Combination of Ear and Face Images
  in Appereance-Based Biometrics, IEEE Trans. on
  PAMI, vol. 25, no. 9, 2003.
• Ear Biometrics based on compression networks
• Moreno B., Sanchez A., Velez J.F., On the Use of
  Outer Ear Images for Personal Identification in
  Security Applications, IEEE 1999.

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EAR BIOMETRICS OUR APPROACH

• Ear Biometrics Based on Geometrical Feature
  Extraction
• Choras Michal, Feature Extraction Based on
  Contour Processing in Ear Biometrics, IEEE
  Workshop on Multimedia Communications and
  Services, MCS04, 15-19, Cracow, 2004.
• Choras Michal, Human Ear Identification Based on
  Image Anlysis, in L. Rutkowski et al. (Eds)
  Artificial Intelligence and Soft Computing,
  ICAISC 2004, Springer-Verlag LNAI 3070, 688-693,
  2004.
• Choras Michal, Ear Biometrics Based on
  Geometrical Method of Feature Extraction, in F.J
  Perales and B.A. Draper (Eds.) Articulated
  Motion and Deformable Objects, AMDO 2004,
  Springer-Verlag LNCS 3179, 51-61, 2004.

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GEOMETRICAL FEATURE EXTRACTION

• General Overview
• Contour Detection, Normalization
• Centroid Calculation
• 1st Algorithm Based on Concentric Circles
• 2nd Algorithm Based on Contour Tracing
• Feature Vectors Comparison and Classification

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CONCLUSIONS WORK-IN-PROGRESS

• Aim Developement of the automatic algorithm
  based on geometrical features for ear
  identification
• So far Algorithm calculating properties of
  concentic circles originated in the ear contour
  image centriod
• So far Algoritm based on contour tracing and
  extracting of the characteristic points
• Results Good for easy ear images.
• Remarks Heavily dependent on contour detection.
  
• Now additional segmentation is used to avoid
  hair, glasses and earrings contours.
• New algorithm of selecting only 8-10 longest
  contours is proposed.

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CONCLUSIONS WORK-IN-PROGRESS

• Work in progress
• Algorithm calculating standard geometrical
  curve-features applied to 10 longest ear
  contours,
• New algorithm calculating triangle ratio of the
  longest contour,
• Classification to left and right ears based on
  longest contour direction,
• New algorithm calculating modified shape ratios
  of the 10 longest contours,
• Further developement of ear database 20 views
  for a person (5 orientations, 2 scales, 2
  illuminations).