Self-Organizing Maps for Fingerprint Image Quality Assessment

Martin Aastrup Olsen, Elham Tabassi, Anton Makarov, Christoph Busch; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2013, pp. 138-145


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.

Related Material

author = {Olsen, Martin Aastrup and Tabassi, Elham and Makarov, Anton and Busch, Christoph},
title = {Self-Organizing Maps for Fingerprint Image Quality Assessment},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2013}