Fingerprint alignment using similarity histogram

Authors:
Tanghui Zhang;Jie Tian;Yuliang He;Jiangang Cheng;Xin Yang
Affiliations:
Biometrics Research Group, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Biometrics Research Group, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Biometrics Research Group, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Biometrics Research Group, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Biometrics Research Group, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Year:
2003

Citing 6
Cited 3

On-Line Fingerprint Verification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Image Enhancement: Algorithm and Performance Evaluation

IEEE Transactions on Pattern Analysis and Machine Intelligence
FVC2000: Fingerprint Verification Competition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image enhancement and minutiae matching in fingerprint verification

Pattern Recognition Letters
A Robust Fingerprint Matching Algorithm Using Local Alignment

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
A Minutia Matching Algorithm in Fingerprint Verification

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4

Fingerprint Matching Based on Global Comprehensive Similarity

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Fingerprint Matching Algorithm Using Ridge Curvature Feature

ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
A method for fingerprint alignment and matching

Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

The performance of fingerprint matching algorithm relies heavily on the accuracy of fingerprint alignment. Falsely aligning two feature sets extracted from two finger images of a fingerprint will increase the false rejection rate (FRR). In order to improve the performance of fingerprint matching algorithm, we present a new fingerprint alignment algorithm called similarity histogram approach (SHA). First, we calculate the local similarity matrix based on minutiae and associate ridges between two fingerprints. Then, similarity histograms of transformation parameters are constructed from local similarity matrix. In the end, the optimal transformation parameters are obtained using a statistical method. Experimental results on FVC databases show that our method is effective and reliable.