Samantha L. AllenDr. Damon L. Woodard

July 31, 2012

BIOMETRICS:EAR RECOGNITION

I. Biometrics: What Is It?

II.Why Biometrics?

III.Ear Biometrics

IV.How A Biometric System Works

V. Conclusion

OUTLINE

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

WHAT IS IT?

Keystroke Voice patterns Gait Signature

BEHAVIORAL CHARACTERISTICS

DNA Fingerprints Eye retinas and irises Facial patterns Hand measurements Ear geometry

PHYSICAL CHARACTERISTICS

BIOMETRIC SYSTEM COMPONENTS

SensorFeature

ExtractionMatcher DATABASE

• 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.

Verifi cation Identifi cation

BIOMETRIC SYSTEM OPERATION

• 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

WHY BIOMETRICS

Privacy Concerns

Irrevocable

Functional Creep

Output is “matching score” instead of yes/no

DISADVANTAGES TO BIOMETRICS

Permanence Performance Acceptability

Distinctiveness Circumvention Collectability Universality

BIOMETRIC SELECTION PROCESS

• Dates back to the 1980’s• Shape and features of ear

Unique

Invariant with age

• Disadvantages Affected by occlusions, hair,

and ear piercings

EAR BIOMETRICS BACKGROUND

EXAMPLES OF BAD IMAGES

• 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

2D VS. 3D EAR BIOMETRICS

• Approaches Global: Whole ear

Local: Sections of ear

Geometric: Measurements

EAR BIOMETRICS APPROACHES

• 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

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.

HOW A BIOMETRIC SYSTEM WORKS (CONT.)

• 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

EAR RECOGNITIONDETECTION PROCESS

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.

PERFORMANCE METRICS

• Research included exploration of ear recognition implementation in Matlab.

• 100 pre-processed images, 17 subjects

SUMMER RESEARCH

• Enroll images into database with

different classes for each person

• Perform ear recognition or 1:1

verification

SUMMER RESEARCH

• 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.

CONCLUSION

• 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

REFERENCES

QUESTIONS?