Smudge Noise for Quality Estimation of Fingerprints and its Validation

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Smudge Noise for Quality Estimation of Fingerprints and its Validation

Robin Richter; Carsten Gottschlich; Lucas Mentch; Duy H. Thai; Stephan F. Huckemann

Automated biometric identification systems are inherently challenged to optimize false (non-)match rates. This can be addressed either by directly improving comparison subsystems, or indirectly by allowing only “good quality” biometric queries to be compared. We are interested in the latter, where the challenge lies in relating the “good quality” of a query to its utility with respect to a comparison subsystem. First, we propose a new general robust biometric quality validation scheme (RBQ VS) that, mimicking the use-case, robustly quantifies comparison improvement obtained by employing a specific quality estimator. For this purpose, we robustify an existing validation scheme by repeated random subsampling cross-validation. Second, specifically for the task of fingerprint comparison, we propose a novel biometric feature for quality estimation. Since comparison subsystems based on fingerprint minutiae, which are ridge endings and bifurcations, appear to miss minutiae or detect spurious minutiae, especially in the presence of smudge noise, we propose an algorithm aiming at measuring corruption by smudge. To this end, we employ a recently developed three parts image-decomposition and link our new smudge noise quality estimator (SNoQE) to the structure of the texture part found. At last, using the FVC databases and an NIST database, we compare the SNoQE with the popular NFIQ 2.0 estimator, and its predecessor. Experimental results show that the single-feature SNoQE can compete with the multi-feature NFIQ 2.0 and, in fact, adds new information not sufficiently reproduced by the NFIQ 2.0. Indeed, a simple combination of SNoQE and NFIQ 2.0 tends to outperform on all databases included in the comparison study. An implementation of the RBQ VS and the SNoQE can be found online.

SPS on Twitter

  • now accepting submissions for special sessions, tutorials, and papers! The conference is set for June 2…
  • DEADLINE EXTENDED: The IEEE Journal of Selected Topics in Signal Processing is now accepting papers for a Special I…
  • NEW WEBINAR: Join us on Friday, 14 August at 11:00 AM ET for the 2021 SPS Membership Preview! Society leadership wi…
  • CALL FOR PAPERS: The 2020 IEEE Workshop on Spoken Language Technology is now accepting papers for its January 2021…
  • CALL FOR PAPERS: The 2020 IEEE International Workshop on Information Forensics and Security is now accepting submis…

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar