Proof of Two Kinds of Fingerprint Feature Extraction CNN

Author

Jie, LongMei ; Wang, Hui ; Li, DeRong ; Shao, GuoQiang

Author_Institution

Comput. Sci. & Inf. Technol. Dept., DaQing Normal Univ., DaQing, China

Volume

1

fYear

2009

fDate

11-14 Dec. 2009

Firstpage

307

Lastpage

310

Abstract

The researches and applications of image processing based on the cellular neural network (CNN) have made great progress. The fingerprint feature extraction CNN are two kinds of CNN, which are able to extract the endings and bifurcations, two important features in a fingerprint image. This paper makes a further contribution to this topic. We first present the global tasks and the local rules of these two kinds of CNN. Then, we proof the correctness of those local rules mathematically.

Keywords

cellular neural nets; feature extraction; fingerprint identification; bifurcations extraction; cellular neural network; endings extraction; fingerprint feature extraction CNN; image processing; Cellular neural networks; Computational intelligence; Computer science; Feature extraction; Fingerprint recognition; Image matching; Image processing; Information technology; Input variables; Output feedback; Cellular Neural Networks; fingerprint feature extraction; von neumann neighborhood;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security, 2009. CIS '09. International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-5411-2

Type

conf

DOI

10.1109/CIS.2009.69

Filename

5376575