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Tour Presentation

  1. 1. Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />1<br />
  2. 2. Biometrics and Pattern Recognition Lab<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />2<br />Human Centered Computing Division<br />Clemson University, Spring 2010<br />
  3. 3. About Us<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />3<br />
  4. 4. Biometrics and Pattern Recognition Lab<br />Established in Summer 2006<br />Formerly the Image and Video Analysis Lab (IVAL)‏<br />In 2008, became part of the Center of Advanced Studies in Identity Sciences (CASIS) with CMU, UNCW, and NC A&T University.<br />4<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />
  5. 5. Biometrics?<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />5<br />
  6. 6. (Bio)(Metrics)‏<br />Bio<br />Life<br />Metrics<br />To measure<br />Biometrics:<br />The science of identifying or authenticating an individual’s identity based on behavioural or physiological characteristics.<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />6<br />
  7. 7. Biometric Characteristics<br />Physical Characteristics<br />Iris<br />Retina<br />Vein Pattern<br />Hand Geometry<br />Face<br />Fingerprint<br />Behavioural Characteristics<br />Keystroke dynamics<br />Signature dynamics<br />Voice<br />Gait<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />7<br />
  8. 8. Why Biometrics?<br />Eliminate memorization<br />Users don’t have to memorize features of their voice, face, eyes, or fingerprints<br />Eliminate misplaced tokens<br />Users won’t forget to bring fingerprints to work<br />Can’t be delegated<br />Users can’t lend fingers or faces to someone else<br />Often unique<br />Save money and maintain database integrity by eliminating duplicate enrollments<br />8<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />
  9. 9. Biometric System<br />Verification (1:1)<br />Identification (1:N)<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />9<br />
  10. 10. Purpose<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />10<br />
  11. 11. Two main research goals<br />To produce:<br /> Usable Biometrics<br /><ul><li>It might have 100% performance, but if it isn’t feasible in the real world, who cares?</li></ul> Unconstrained Biometrics<br /><ul><li>At present, good recognition rates depend on a lot of variables being just right, or at least consistent
  12. 12. We would like to reduce the dependency or get rid of it altogether</li></ul>11<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />
  13. 13. Constraints<br />Some common constraints are lighting, non-uniform distance, pose, expression, time lapse, occlusion<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />12<br />Typical image used in facial recognition<br />Unconstrained image<br />
  14. 14. Periocular Region Recognition<br />Feature Reduction using Computational Intelligence<br />Aging Effects on Facial Recognition<br />Effects of Demographics on Facial Recognition<br />Soft Biometrics<br />Projects<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />13<br />
  15. 15. Periocular Region Recognition<br />Relaxes image quality (location of iris, focus, blurring) on iris images<br />Could be used if more of the face is occluded<br />Currently looking at texture, color, and eye shape<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />14<br />
  16. 16. Feature Reduction using Computational Intelligence<br />General Regression Neural Network (GRNN)<br />Reduce the size of the features to enable faster, more portable biometric applications<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />15<br />
  17. 17. Aging Effects on Facial Recognition<br />Looking at an image of a person, can we reliably predict<br />what age they are?<br />what they will look like in so many years?<br />or what they looked like in the past?<br />Relaxes time lapse constraint<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />16<br />
  18. 18. Demographics<br />How do demographics affect recognition?<br />Older easier to recognize than younger<br />Males easier than females<br />Why do some algorithms work better on certain populations than others?<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />17<br />
  19. 19. Soft Biometrics<br />What if we don’t have enough information to identify the person?<br />We would like to know as much about them as possible: age, gender, ...<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />18<br />
  20. 20. Questions?<br />Clemson University <br />School of Computing <br />Biometrics and Pattern Recognition Lab Director: Damon Woodard, PhD<br />19<br />

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