(2010) Mobile ID and Biometrics


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(2010) Mobile ID and Biometrics

  1. 1. Fingerprint Recognition Performance Evaluation for Mobile ID ApplicationsCarnahan Conference| San Jose, CA| October 7th, 2010<br />Biometric Standards, Performance, and Assurance Laboratory | <br />Purdue University<br /> www.bspalabs.org<br />www.twitter.com/bspalabs<br />www.slideshare.net/bspalabs<br />www.linkedin.com/companies/bspa-labs<br />
  2. 2. Agenda<br />Motivation <br />What are we doing?<br />Why are we doing this?<br />Data Collection<br />Results<br />Questions and Further Research<br />Comments / Questions<br />
  3. 3. What are we doing?<br />Evaluating the impact of fingerprint size and fingerprint sensor interoperability on recognition performance.<br />Image quality<br />Minutiae count<br />Matching error rates (FNMR & FMR)<br />
  4. 4. Why are we doing this?<br />To provide performance related information on feasibility of using mobile fingerprint devices in the law enforcement environment.<br />To improve best practices document for mobile fingerprint devices.<br />
  5. 5. Data Methodology<br />Six images from index finger of subject’s dominant hand using two different fingerprint sensors (large area capacitive and optical sensor – FIPS 201 certified)<br />Dataset Summary<br />
  6. 6. Data Collection – Fingerprint Sizes<br />Image sizes are chosen from Mobile ID Device Best Practice Recommendation<br />Seven Levels of Image Size<br />
  7. 7. Dataset - Cropping<br />Cropping<br />In-house development using Matlab™ was used by using the core values as the center of the cropping region.<br />If fingerprint image had two cores, then the core with the higher y-axis was chosen<br />
  8. 8. Dataset – Cropping Sample<br />Note: these sample images are not the original size images<br />
  9. 9. Results – Minutiae Count<br />Minutiae counts were generated using Neurotechnology’s VeriFinger 6.0 extractor<br />Average Minutiae Count<br />
  10. 10. Results – Minutiae Distribution for Optical Dataset<br />Optical Sensor Dataset Histogram<br />
  11. 11. Results – Minutiae Distribution for Capacitance Dataset<br />Capacitance Sensor Dataset Histogram<br />
  12. 12. Results – NFIQ Image Quality<br />NFIQ scores were run as performance prediction<br />NFIQ Score Descriptive Statistics<br />
  13. 13. Results – Optical Detection Error Tradeoff (DET)<br />Optical Sensor DET Curves<br />
  14. 14. Results – Capacitance Detection Error Tradeoff (DET)<br />Capacitance Sensor DET Curves<br />
  15. 15. Results – FNMR & FMR at 0.1% FMR<br />Verifinger FNMR (%)<br />Verifinger FMR (%)<br />
  16. 16. Results – Verifinger FMR<br />VeriFinger FMR<br />
  17. 17. Results – Performance of Interoperable Datasets<br />FNMR (in %) at FMR of 0.1%<br />
  18. 18. Results – Matching Performance for Different Sized Images<br />FNMR (in %) at FMR of 0.1%<br />
  19. 19. Results - Conclusions<br />Single fingerprint images of sizes at or below level 3 are unsuitable for matching purposes.<br />The number of minutiae extracted from the image is crucial as capturing high quality fingerprint images.<br />Interoperability FNMR reduces as the size of the image was increased.<br />Level 7 showed the best results, but level 5 and 6 showed performance that would be acceptable in law enforcement applications.<br />
  20. 20. Any Questions?<br />Follow the discussion on the research blog after the conference<br />www.bspalabs.org/<br />
  21. 21. Authors and Primary Contact Information<br />Authors<br />Shimon Modi<br />Visiting Scholar at C-DAC Mumbai<br />shimonmodi@gmail.com<br />Ashwin Mohan, M.S.<br />Developer and Database Analyst, Morningstar, Inc<br />Ashwin.mohan@morningstar.com<br />Benny Senjaya<br />Graduate Researcher at BSPA Lab<br />bennysenjaya@gmail.com<br />Stephen Elliott, Ph.D.<br />BSPA Lab Director & Associate Professor<br />elliott@purdue.edu<br />Contact Information<br />Stephen Elliott, Ph.D.<br />Associate Professor<br />Director of BSPA Labs<br />elliott@purdue.edu<br />