Fingerprint Classification Using Improved Directional Field and Fuzzy Wavelet Neural Network

Author

Wang, Wei ; Li, Jianwei ; Chen, Weimin

Author_Institution

Lab. of Pattern Recognition, Optoelectron. Eng. Coll., Chongqing Univ.

Volume

2

fYear

0

fDate

0-0 0

Firstpage

9961

Lastpage

9964

Abstract

A fingerprint classification algorithm is proposed in this paper. It is based on the features extracted from the directional field of the fingerprint image. To improve the accuracy of the directional field, an efficient estimation approach is developed. Based on the improved directional field, the singular points and the relative features are extracted to generate the input features of the fingerprint classifier. After encoding the input features, a fuzzy wavelet neural network-based classifier is applied to classify fingerprints for the five-class problem. Experimental results show an excellent classification performance of the proposed algorithm

Keywords

feature extraction; fingerprint identification; fuzzy neural nets; image classification; wavelet transforms; feature extraction; fingerprint image classification; fuzzy wavelet neural network; Classification algorithms; Databases; Feature extraction; Fingerprint recognition; Fuzzy neural networks; Hidden Markov models; Image matching; Laboratories; Neural networks; Pattern recognition; Directional Field; Fingerprint Classification; Fuzzy Wavelet Neural Network; Singular Point;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713945

Filename

1713945