Fingerprint recognition using model-based density map

Authors:
Dingrui Wan;Jie Zhou
Affiliations:
Dept. of Autom., Tsinghua Univ., Beijing, China;-
Venue:
IEEE Transactions on Image Processing
Year:
2006

Citing 0
Cited 5

A comparative study of combining multiple enrolled samples for fingerprint verification

Pattern Recognition
Local relative location error descriptor-based fingerprint minutiae matching

Pattern Recognition Letters
Driver identification based on voice signal using continuous wavelet transform and artificial neural network techniques

Expert Systems with Applications: An International Journal
Consensus fingerprint matching with genetically optimised approach

Pattern Recognition
A Novel Fingerprint Matching Algorithm Using Ridge Curvature Feature

ICB '09 Proceedings of the Third International Conference on Advances in Biometrics

Quantified Score

Hi-index	0.02

Visualization

Abstract

Utilizing more information other than minutiae is much helpful for large-scale fingerprint recognition applications. In this paper, we proposed a polynomial model to approximate the density map of fingerprints and used the model's parameters as a novel kind of feature for fingerprint representation. Thus, the density information can be utilized into the matching stage with a low additional storage cost. A decision-level fusion scheme is further used to combine the density map matching with conventional minutiae-based matching and experimental results showed a much better performance than using single minutiae-based matching.