The document summarizes a study evaluating the impact of fingerprint size and sensor interoperability on recognition performance for mobile ID applications. The study collected fingerprint images from multiple sensors at different sizes, analyzed minutiae extraction and matching error rates. The results showed that sizes at or below a certain level were unsuitable for matching. Recognition performance improved with larger sizes, though some intermediate sizes showed acceptable performance for law enforcement. The study aimed to provide guidance on feasibility and best practices for mobile fingerprint devices.

1. Fingerprint Recognition Performance Evaluation for Mobile ID ApplicationsCarnahan Conference| San Jose, CA| October 7th, 2010 Biometric Standards, Performance, and Assurance Laboratory | Purdue University www.bspalabs.org www.twitter.com/bspalabs www.slideshare.net/bspalabs www.linkedin.com/companies/bspa-labs

2. Agenda Motivation What are we doing? Why are we doing this? Data Collection Results Questions and Further Research Comments / Questions

3. What are we doing? Evaluating the impact of fingerprint size and fingerprint sensor interoperability on recognition performance. Image quality Minutiae count Matching error rates (FNMR & FMR)

4. Why are we doing this? To provide performance related information on feasibility of using mobile fingerprint devices in the law enforcement environment. To improve best practices document for mobile fingerprint devices.

5. Data Methodology Six images from index finger of subject’s dominant hand using two different fingerprint sensors (large area capacitive and optical sensor – FIPS 201 certified) Dataset Summary

6. Data Collection – Fingerprint Sizes Image sizes are chosen from Mobile ID Device Best Practice Recommendation Seven Levels of Image Size

7. Dataset - Cropping Cropping In-house development using Matlab™ was used by using the core values as the center of the cropping region. If fingerprint image had two cores, then the core with the higher y-axis was chosen

8. Dataset – Cropping Sample Note: these sample images are not the original size images

9. Results – Minutiae Count Minutiae counts were generated using Neurotechnology’s VeriFinger 6.0 extractor Average Minutiae Count

10. Results – Minutiae Distribution for Optical Dataset Optical Sensor Dataset Histogram

11. Results – Minutiae Distribution for Capacitance Dataset Capacitance Sensor Dataset Histogram

12. Results – NFIQ Image Quality NFIQ scores were run as performance prediction NFIQ Score Descriptive Statistics

13. Results – Optical Detection Error Tradeoff (DET) Optical Sensor DET Curves

14. Results – Capacitance Detection Error Tradeoff (DET) Capacitance Sensor DET Curves

15. Results – FNMR & FMR at 0.1% FMR Verifinger FNMR (%) Verifinger FMR (%)

16. Results – Verifinger FMR VeriFinger FMR

17. Results – Performance of Interoperable Datasets FNMR (in %) at FMR of 0.1%

18. Results – Matching Performance for Different Sized Images FNMR (in %) at FMR of 0.1%

19. Results - Conclusions Single fingerprint images of sizes at or below level 3 are unsuitable for matching purposes. The number of minutiae extracted from the image is crucial as capturing high quality fingerprint images. Interoperability FNMR reduces as the size of the image was increased. Level 7 showed the best results, but level 5 and 6 showed performance that would be acceptable in law enforcement applications.

20. Any Questions? Follow the discussion on the research blog after the conference www.bspalabs.org/

21. Authors and Primary Contact Information Authors Shimon Modi Visiting Scholar at C-DAC Mumbai shimonmodi@gmail.com Ashwin Mohan, M.S. Developer and Database Analyst, Morningstar, Inc Ashwin.mohan@morningstar.com Benny Senjaya Graduate Researcher at BSPA Lab bennysenjaya@gmail.com Stephen Elliott, Ph.D. BSPA Lab Director & Associate Professor elliott@purdue.edu Contact Information Stephen Elliott, Ph.D. Associate Professor Director of BSPA Labs elliott@purdue.edu