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

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

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

Behavioral Characteristics Keystroke Voice patterns Gait Signature

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

Biometric System Components Sensor Feature Extraction Matcher DATABASE

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.

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 Privacy Concerns Irrevocable Functional Creep Output is “matching score” instead of yes/no

Biometric Selection Process Permanence Performance Acceptability Distinctiveness Circumvention Collectability Universality

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

Examples of Bad Images

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

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

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 (Cont.)

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 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

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.

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

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

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.

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%2 0Biometrics09.pdf http://www.security.iitk.ac.in/contents/publications/more/ear.pdf http://www.technovelgy.com/ct/Technology-Article.asp?ArtNum=98

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