Olsen, Martin Aastrup4; Tabassi, Elham4; Makarov, Anton3; Busch, Christoph4
1 Department of Applied Mathematics and Computer Science, Technical University of Denmark2 National Institute of Standards and Technology3 Technical University of Denmark4 National Institute of Standards and Technology
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.
2013 Ieee Conference on Computer Vision and Pattern Recognition Workshops, 2013, p. 138-145
Main Research Area:
2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Computer Vision and Pattern Recognition, 2013