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Biometrics: Ear Recognition

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

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  1. Biometrics:Ear Recognition Samantha L. Allen Dr. Damon L. Woodard July 31, 2012

  2. OUTLINE • Biometrics: What Is It? • Why Biometrics? • Ear Biometrics • How A Biometric System Works • Conclusion

  3. 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

  4. Behavioral Characteristics • Keystroke • Voice patterns • Gait • Signature

  5. Physical Characteristics • DNA • Fingerprints • Eye retinas and irises • Facial patterns • Hand measurements • Ear geometry

  6. Biometric System Components

  7. Biometric System Operation Verification Identification • 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.

  8. 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

  9. Disadvantages to Biometrics • Privacy Concerns • Irrevocable • Functional Creep • Output is “matching score” instead of yes/no

  10. Biometric Selection Process • Permanence • Performance • Acceptability • Distinctiveness • Circumvention • Collectability • Universality

  11. 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

  12. Examples of Bad Images

  13. 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

  14. Ear Biometrics Approaches • Approaches • Global: Whole ear • Local: Sections of ear • Geometric: Measurements

  15. 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?

  16. How A Biometric System Works (Cont.)

  17. 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.

  18. 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

  19. 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.

  20. SUMMER RESEARCH • Research included exploration of ear recognition implementation in Matlab. • 100 pre-processed images, 17 subjects

  21. SUMMER RESEARCH • Enroll images into database with different classes for each person • Perform ear recognition or 1:1 verification

  22. Conclusion • 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.

  23. References • 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. • http://www.advancedsourcecode.com/earrecognition.asp • http://vislab.ucr.edu/PUBLICATIONS/pubs/Chapters/2009/3D%20Ear%20Biometrics09.pdf • http://www.security.iitk.ac.in/contents/publications/more/ear.pdf • http://www.technovelgy.com/ct/Technology-Article.asp?ArtNum=98

  24. Questions?

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