Fingerprint recognition based on combined features

Authors:
Yangyang Zhang;Xin Yang,;Qi Su;Jie Tian
Affiliations:
Center for Biometrics and Security Research, Key Laboratory of Complex Systems and Intelligence Science, P.O.Box 2728 Beijing, China;Institute of Automation, P.O.Box 2728 Beijing, China;Chinese Academy of Sciences, P.O.Box 2728 Beijing, China;Graduate School of the Chinese Academy of Sciences, P.O.Box 2728 Beijing, China
Venue:
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Year:
2007

Citing 8
Cited 0

Decision Combination in Multiple Classifier Systems

IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Fingerprint Verification

IEEE Transactions on Pattern Analysis and Machine Intelligence
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fingerprint Verification System Based on Triangular Matching and Dynamic Time Warping

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Matching Using an Orientation-Based Minutia Descriptor

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint matching using ridges

Pattern Recognition
Fingerprint quality indices for predicting authentication performance

AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Fingerprint recognition by combining global structure and local cues

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we represent the fingerprint with a novel local feature descriptor, which is composed of minutia, the sample points on associated ridge and the adjacent orientation distribution. Then a novel fingerprint recognition method is proposed combining the orientation field and the local feature descriptor. We compare two descriptor lists from the input and template fingerprints to calculate a set of transformation vectors for fingerprint alignment. The similarity score is evaluated by fusing the orientation field and the local feature descriptor. The experiments have been conducted on three large-scale databases. The comparison results approve that our algorithm is more accurate and robust than previous methods based on the minutiae or ridge features, especially for those poor-quality and partial fingerprints.