DocumentCode :
510231
Title :
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
Link To Document :
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