Open AccessResearch on fingerprint classification based on twin support vector machine
Author(s) -
Ding Shifei,
Shi Songhui,
Jia Weikuan
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5977
Subject(s) - fingerprint (computing) , pattern recognition (psychology) , artificial intelligence , support vector machine , computer science , gabor filter , nist , binary tree , fingerprint recognition , feature extraction , speech recognition , algorithm
Fingerprint classification is one of the core steps of fingerprint recognition and directly relates to the accuracy of recognition. For this reason, a fingerprint classification method based on Twin Support Vector Machine (TWSVM) is studied. First, the Gabor filter is used to extract texture features from fingerprint images. Second, a multi‐class model based on TWSVM is constructed by using the ‘one‐versus‐all’ strategy and the binary tree method, respectively. The quantum particle swarm optimisation algorithm is used to optimise the parameters in the model. Then the fingerprints are divided into five categories using the optimised model. Finally, the classification model is evaluated using fingerprint images from the NIST‐4 database. The experimental results show that applying the TWSVM to fingerprint classification can get good classification results.