Conference paper
Self-Organizing Maps for Fingerprint Image Quality Assessment
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.
Language: | English |
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Publisher: | IEEE |
Year: | 2013 |
Pages: | 138-145 |
Proceedings: | 2013 IEEE Conference on Computer Vision and Pattern RecognitionIEEE Conference on Computer Vision and Pattern Recognition Workshops |
ISBN: | 076954990X , 076954990x , 1479909947 , 9780769549903 and 9781479909940 |
ISSN: | 21607516 and 21607508 |
Types: | Conference paper |
DOI: | 10.1109/CVPRW.2013.28 |
Accuracy Histograms Kohonen self-organizing map NIST NFIQ 2.0 Website Quality assessment SOM Topology Training Vectors Vegetation accuracy tracking biometric biometric enrolment biometric recognition systems computational complexity computational complexity requirement detection probability evaluation finger image high-level representation fingerprint fingerprint identification fingerprint image block clustering fingerprint image quality assessment image representation machine learning military computing military force mobile computing mobile platforms national biometric systems pattern clustering police police forces probability quality quality measurement random forest recognition process self-organising feature maps self-organizing maps spatial information content standard supervised machine learning techniques unsupervised learning unsupervised machine learning techniques