The Biometrics and Pattern Recognition Lab at Clemson University, directed by Damon Woodard, conducts research on biometrics and pattern recognition. The lab seeks to develop usable and unconstrained biometrics by reducing dependencies on strict constraints like lighting and pose. Current projects include periocular region recognition, feature reduction using computational intelligence, analyzing aging effects and demographics in facial recognition, and extracting soft biometrics.

1. Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 1

2. Biometrics and Pattern Recognition Lab Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 2 Human Centered Computing Division Clemson University, Spring 2010

3. About Us Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 3

4. Biometrics and Pattern Recognition Lab Established in Summer 2006 Formerly the Image and Video Analysis Lab (IVAL)‏ In 2008, became part of the Center of Advanced Studies in Identity Sciences (CASIS) with CMU, UNCW, and NC A&T University. 4 Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD

5. Biometrics? Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 5

6. (Bio)(Metrics)‏ Bio Life Metrics To measure Biometrics: The science of identifying or authenticating an individual’s identity based on behavioural or physiological characteristics. Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 6

7. Biometric Characteristics Physical Characteristics Iris Retina Vein Pattern Hand Geometry Face Fingerprint Behavioural Characteristics Keystroke dynamics Signature dynamics Voice Gait Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 7

8. Why Biometrics? Eliminate memorization Users don’t have to memorize features of their voice, face, eyes, or fingerprints Eliminate misplaced tokens Users won’t forget to bring fingerprints to work Can’t be delegated Users can’t lend fingers or faces to someone else Often unique Save money and maintain database integrity by eliminating duplicate enrollments 8 Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD

9. Biometric System Verification (1:1) Identification (1:N) Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 9

10. Purpose Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 10

12. We would like to reduce the dependency or get rid of it altogether11 Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD

13. Constraints Some common constraints are lighting, non-uniform distance, pose, expression, time lapse, occlusion Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 12 Typical image used in facial recognition Unconstrained image

14. Periocular Region Recognition Feature Reduction using Computational Intelligence Aging Effects on Facial Recognition Effects of Demographics on Facial Recognition Soft Biometrics Projects Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 13

15. Periocular Region Recognition Relaxes image quality (location of iris, focus, blurring) on iris images Could be used if more of the face is occluded Currently looking at texture, color, and eye shape Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 14

16. Feature Reduction using Computational Intelligence General Regression Neural Network (GRNN) Reduce the size of the features to enable faster, more portable biometric applications Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 15

17. Aging Effects on Facial Recognition Looking at an image of a person, can we reliably predict what age they are? what they will look like in so many years? or what they looked like in the past? Relaxes time lapse constraint Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 16

18. Demographics How do demographics affect recognition? Older easier to recognize than younger Males easier than females Why do some algorithms work better on certain populations than others? Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 17

19. Soft Biometrics What if we don’t have enough information to identify the person? We would like to know as much about them as possible: age, gender, ... Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 18

20. Questions? Clemson University School of Computing Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD 19