Effective Fingerprint Quality Estimation for Diverse Capture Sensors

Clicks: 315
ID: 110331
2010
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Abstract
Recognizing the quality of fingerprints in advance can be beneficial for improving the performance of fingerprint recognition systems. The representative features to assess the quality of fingerprint images from different types of capture sensors are known to vary. In this paper, an effective quality estimation system that can be adapted for different types of capture sensors is designed by modifying and combining a set of features including orientation certainty, local orientation quality and consistency. The proposed system extracts basic features, and generates next level features which are applicable for various types of capture sensors. The system then uses the Support Vector Machine (SVM) classifier to determine whether or not an image should be accepted as input to the recognition system. The experimental results show that the proposed method can perform better than previous methods in terms of accuracy. In the meanwhile, the proposed method has an ability to eliminate residue images from the optical and capacitive sensors, and the coarse images from thermal sensors.
Reference Key
xie2010sensorseffective Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shan Juan Xie;Sook Yoon;Jinwook Shin;Dong Sun Park;Xie, Shan Juan;Yoon, Sook;Shin, Jinwook;Park, Dong Sun;
Journal sensors
Year 2010
DOI
10.3390/s100907896
URL
https://www.mdpi.com/1424-8220/10/9/7896/htm
Keywords
recognition sensor svm fingerprints quality estimation

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