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Biometrics: Ear Recognition. Samantha L. Allen Dr. Damon L. Woodard July 31, 2012. OUTLINE. Biometrics: What Is It? Why Biometrics? Ear Biometrics How A Biometric System Works Conclusion. What Is It?. Biometrics The science and technology of measuring and analyzing biological data

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biometrics ear recognition
Biometrics:Ear Recognition

Samantha L. Allen

Dr. Damon L. Woodard

July 31, 2012

  • Biometrics: What Is It?
  • Why Biometrics?
  • Ear Biometrics
  • How A Biometric System Works
  • Conclusion
what is it
What Is It?
  • Biometrics
    • The science and technology of measuring and analyzing biological data
    • Measures and analyzes human body characteristics for authentication
    • Physical or behavioral characteristics
    • Identity access management and access control
behavioral characteristics
Behavioral Characteristics
  • Keystroke
  • Voice patterns
  • Gait
  • Signature
physical characteristics
Physical Characteristics
  • DNA
  • Fingerprints
  • Eye retinas and irises
  • Facial patterns
  • Hand measurements
  • Ear geometry
biometric system operation
Biometric System Operation



  • Identity Claimed
  • One-to-one Comparison
  • Authentication is either approved or denied.
  • No identity claimed
  • One-to-many comparison
  • Identity is determined
  • (OR)
  • User not being enrolled leads to fail of identification.
why biometrics
Why Biometrics
  • Biometrics is a method of *direct* human identification as opposed to identifying humans by their possession of keys or remembering passwords.
  • Preferred method of identification because ID’s and cards can easily be stolen and passwords are likely to be forgotten or shared.
  • Discourages fraud
  • Enhances security
disadvantages to biometrics
Disadvantages to Biometrics
  • Privacy Concerns
  • Irrevocable
  • Functional Creep
  • Output is “matching score” instead of yes/no
biometric selection process
Biometric Selection Process
  • Permanence
  • Performance
  • Acceptability
  • Distinctiveness
  • Circumvention
  • Collectability
  • Universality
ear biometrics background
Ear Biometrics Background
  • Dates back to the 1980’s
  • Shape and features of ear
      • Unique
    • Invariant with age
  • Disadvantages
    • Affected by occlusions, hair,
    • and ear piercings
2d vs 3d ear biometrics
2D vs. 3D Ear Biometrics
  • Contains surface shape information related to anatomical structure
  • Relatively insensitive to illumination
  • Slightly higher performance
  • Performance is greatly affected by pose variation and imaging conditions
  • Images contain less information
ear biometrics approaches
Ear Biometrics Approaches
  • Approaches
    • Global: Whole ear
    • Local: Sections of ear
    • Geometric: Measurements
how a biometric system works
How A Biometric System Works
  • Has this applicant been here before?
  • Is this the person that he/she claims to be?
  • Should this individual be given access to our system?
  • Are the rendered services being accessed by a legitimate user?
how a biometric system works cont1
How A Biometric System Works (Cont.)
  • Identifying features of individual are enrolled into system.
  • During feature extraction, the application is used to identify specific points of data as match points
  • Match points in database are processed using an algorithm that translates the information into numeric values or feature vectors.
  • Feature set is compared against the template set in the system database.
ear recognition detection process
Ear RecognitionDetection Process
  • Human ear detection is a crucial task of a human ear recognition system because its performance significantly affects the overall quality of the system.
    • template matching based detection
    • ear shape model based detection
    • fusion of color and range images and global-to-local registration based detection
performance metrics
Performance Metrics
  • The following are used as performance metrics for biometric systems:
    • False accept rate or false match rate (FAR or FMR)
      • Measures the percent of invalid inputs which are incorrectly accepted.
      • Probability that the system incorrectly matches the input pattern to a non-matching template in the database.
    • False reject rate or false non-match rate (FRR or FNMR)
  • Measures the percent of valid inputs which are incorrectly rejected.
  • Probability that the system fails to detect a match between the input pattern and a matching template in the database.
summer research
  • Research included exploration of ear recognition implementation in Matlab.
  • 100 pre-processed images, 17 subjects
summer research1
  • Enroll images into database with different classes for each person
  • Perform ear recognition or 1:1 verification
  • Ear recognition is still a relatively new area in biometrics research.
  • Potential to be used in real-world applications to identify/authenticate humans by their ears.
  • Can be used in both the low and high security applications and in combination with other biometrics such as face.
  • D. Hurley, B Arbab-Zavar, and M. Nixon, The Ear as a Biometric, In A. Jain, P. Flynn, and A. Ross, Handbook of Biometrics, Chapter 7, Springer US, 131-150, 2007.
  • A. Jain, A. Ross, and S. Prabhakar. An Introduction to Biometric Recognition. In IEE Trans. On Circuits and Systems for Video Technology, Jan. 2004.
  • R. N. Tobias, A Survey of Ear as a Biometric: Methods, Applications, and Databases for Ear Recognition.
  • Carreira-Perpiñán, M. Á. (1995): Compression neural networks for feature extraction: Application to human recognition from ear images (in Spanish). MSc thesis, Faculty of Informatics, Technical University of Madrid, Spain.