Self-Organizing Maps for Fingerprint Image Quality Assessment

Martin Aastrup Olsen, Elham Tabassi, Anton Makarov, Christoph Busch

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Fingerprint quality assessment is a crucial task which needs to be conducted accurately in various phases in the biometric enrolment and recognition processes. Neglecting quality measurement will adversely impact accuracy and efficiency of biometric recognition systems (e.g. verification and identification of individuals). Measuring and reporting quality allows processing enhancements to increase probability of detection and track accuracy while decreasing probability of false alarms. Aside from predictive capabilities with respect to the recognition performance, another important design criteria for a quality assessment algorithm is to meet the low computational complexity requirement of mobile platforms used in national biometric systems, by military and police forces. We propose a computationally efficient means of predicting biometric performance based on a combination of unsupervised and supervised machine learning techniques. We train a self-organizing map (SOM) to cluster blocks of fingerprint images based on their spatial information content. The output of the SOM is a high-level representation of the finger image, which forms the input to a Random Forest trained to learn the relationship between the SOM output and biometric performance. The quantitative evaluation performed demonstrates that our proposed quality assessment algorithm is a reasonable predictor of performance. The open source code of our algorithm will be posted at NIST NFIQ 2.0 website.
Original languageEnglish
Title of host publication2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
PublisherIEEE
Publication date2013
Pages138-145
DOIs
Publication statusPublished - 2013
Event2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Portland, OR, United States
Duration: 23 Jun 201328 Jun 2013
http://www.pamitc.org/cvpr13/

Conference

Conference2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CountryUnited States
CityPortland, OR
Period23/06/201328/06/2013
Internet address

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