Feb 23, 2016
Samantha L. AllenDr. Damon L. Woodard
July 31, 2012
BIOMETRICS:EAR RECOGNITION
I. Biometrics: What Is It?II.Why Biometrics?III.Ear BiometricsIV.How A Biometric System WorksV. 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
Sensor Feature Extraction Matcher 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.
Verification Identification
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?