By Topic

Fingerprint classification through self-organizing feature maps modified to treat uncertainties

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

2 Author(s)
Halici, U. ; Dept. of Electr. Eng., Middle East Tech. Univ., Ankara, Turkey ; Ongun, G.

In this paper, a neural network structure based on self organizing feature maps (SOFM) is proposed for fingerprint classification. In order to be able to deal with fingerprint images having distorted regions, the SOFM learning and classification algorithms are modified. For this purpose, the concept of “certainty” is introduced and used in the modified algorithms. This fingerprint classifier together with a fingerprint identifier, constitute subsystems of an automated fingerprint identification system, named HALafis. Our results show that a network that is trained with a sufficiently large and representative set of samples can be used as an indexing mechanism for a fingerprint database, so that it does not need to be retrained for each fingerprint added to the database

Published in:

Proceedings of the IEEE  (Volume:84 ,  Issue: 10 )