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

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

4. The science and technology of measuring and analyzing biological data
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

5. Behavioral Characteristics
Keystroke
Voice patterns
Gait
Signature

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

7. Biometric System Components
Sensor
Feature Extraction
Matcher
DATABASE

8. 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.

9. 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

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

12. Biometric Selection Process
Permanence
Performance
Acceptability
Distinctiveness
Circumvention
Collectability
Universality

13. 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

14. Examples of Bad Images

15. Images contain less information
2D vs. 3D Ear Biometrics
Performance is greatly affected by pose variation and imaging conditions
Images contain less information
Contains surface shape information related to anatomical structure
Relatively insensitive to illumination
Slightly higher performance

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

17. 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?

18. How A Biometric System Works (Cont.)

19. 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.

20. Ear Recognition Detection 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

21. 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.

22. 100 pre-processed images, 17 subjects
SUMMER RESEARCH
Research included exploration of ear recognition implementation in Matlab.
100 pre-processed images, 17 subjects

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

24. Ear recognition is still a relatively new area in biometrics research.
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.

25. 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, , 2007.
A. Jain, A. Ross, and S. Prabhakar. An Introduction to Biometric Recognition. In IEE Trans. On Circuits and Systems for Video Technology, Jan
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.
0Biometrics09.pdf

26. Questions?