Improvement of Waveform Analysis Method Based on Polynomial Fitting for Liquid Drop Fingerprint

Abstract

Waveform analysis method is widely used for feature extraction of liquid drop fingerprint, but it is easily affected by noise. To solve this problem, waveform fitting and analysis method based on polynomial fitting is proposed, though which the waveform of liquid drop fingerprint is fitted to be a smooth curve. Experimental results show that waveform fitting and analysis method is able to reduce the standard deviation and maximum difference of eigenvectors from the same kinds of liquid, and thus increase the recognition accuracy rate of 10 kinds of water based on BP neural network from 86.5% to 100%.