Title :
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
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;
Conference_Titel :
Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference on
Conference_Location :
Shengyang
Print_ISBN :
978-1-4799-2564-3
DOI :
10.1109/MEC.2013.6885281
