By Topic

Automatic fingerprint identification using cluster algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Ren Qun ; Inst. of Autom., Acad. Sinica, Beijing, China ; Tian Jie ; He Yuliang ; Cheng Jiangang

A fingerprint identification technique is presented, which mainly consists of three modules: enrolment module, identification module and feedback module. In the identification module, a clustering algorithm is used to detect similar minutiae groups from multiple template images generated from the same finger and create the cluster core set. An algorithm compares the similarity level between the minutiae of the test fingerprint and the cluster core set and returns a likely list of candidates. In feedback module, we propose a path to learn and train the cluster core vector based on the assessment of cluster solution. The experiment results demonstrate that this similarity-searching approach proves suitable for one-too-many matching of fingerprints on large-scale databases. With the feedback module the proposed fingerprint identification scheme has inspiring identification performance.

Published in:

Pattern Recognition, 2002. Proceedings. 16th International Conference on  (Volume:2 )

Date of Conference:

2002