Feature extraction of Liquid Drop Fingerprint based on Bezier curve fitting

Author

Song Qing ; Yang Lu ; Du Danqing ; Meng Gaojie ; Mao Xuefei

Author_Institution

Autom. Sch., Beijing Univ. of Posts & Telecommun., Beijing, China

fYear

2013

fDate

20-22 Dec. 2013

Firstpage

1363

Lastpage

1366

Abstract

In order to effectively characterize the Liquid Drop Fingerprint (LDF) of different liquids, a new method based on Bezier curve fitting is put forward. The fitting curve is calculated by the least square method. To decrease the fitting error and the feature dimension, the Bezier curve order should be reasonably chosen, combining the particularity of LDF. Theoretical analysis and experimental results show that the eigenvector can reflect the typical differences among different kinds of liquids, and the recognition rate within current test samples is reached up to 100%.

Keywords

eigenvalues and eigenfunctions; feature extraction; least mean squares methods; Bezier curve fitting; Bezier curve order; LDF; eigenvector; feature extraction; least square method; liquid drop fingerprint; recognition rate; Educational institutions; Equations; Feature extraction; Fitting; Liquids; Neural networks; Standards; BP neural network; Bezier curve; Liquid Drop Fingerprint; feature extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference on

Conference_Location

Shengyang

Print_ISBN

978-1-4799-2564-3

Type

conf

DOI

10.1109/MEC.2013.6885281

Filename

6885281