Research on fingerprint classification based on twin support vector machine

被引：11
|
作者：
Ding, Shifei [1 ,2 ]
Shi, Songhui [1 ,2 ]
Jia, Weikuan [3 ]
机构：
[1] China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Jiangsu, Peoples R China
[2] Minist Educ Peoples Republ China, Mine Digitizat Engn Res Ctr, Xuzhou 221116, Jiangsu, Peoples R China
[3] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China
来源：
IET IMAGE PROCESSING | 2020年 / 14卷 / 02期
关键词：
particle swarm optimisation; image classification; fingerprint identification; Gabor filters; trees (mathematics); feature extraction; pattern classification; support vector machines; image texture; optimised model; classification model; fingerprint images; TWSVM; twin support vector machine; fingerprint recognition; fingerprint classification method; multiclass model; quantum particle swarm optimisation algorithm; fingerprints;
D O I：
10.1049/iet-ipr.2018.5977
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：231 / 235
页数：5
相关论文
共 50 条
  • [1] Research on automatic fingerprint classification based on support vector machine
    Guo, Lei
    Wu, Youxi
    Wu, Qing
    Yan, Weili
    Shen, Xueqin
    [J]. WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 4093 - +
  • [2] Fingerprint Classification Based on Support Vector Machine
    Lue, Chaohui
    Wang, Hui
    Liu, Yingying
    [J]. INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL SCIENCES AND OPTIMIZATION, VOL 1, PROCEEDINGS, 2009, : 859 - 862
  • [3] Fingerprint Classification using Support Vector Machine
    Alias, Nurul Ain
    Radzi, Nor Haizan Mohamed
    [J]. 2016 FIFTH ICT INTERNATIONAL STUDENT PROJECT CONFERENCE (ICT-ISPC), 2016, : 105 - 108
  • [4] Quality classification method for fingerprint image based on support vector machine
    Zhang, Yu
    Yin, Yi-Long
    Luo, Gong-Qing
    [J]. Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, 2009, 22 (01): : 129 - 135
  • [5] Structural twin support vector machine for classification
    Qi, Zhiquan
    Tian, Yingjie
    Shi, Yong
    [J]. KNOWLEDGE-BASED SYSTEMS, 2013, 43 : 74 - 81
  • [6] The research of twin support vector machine based on radius interval
    Jin, Feng
    Zhang, De
    [J]. 2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2017, : 3619 - 3623
  • [7] An Updated Projection Twin Support Vector Machine for Classification
    Hua, Xiaopeng
    Xu, Sen
    [J]. 2017 INTERNATIONAL CONFERENCE ON ELECTRONIC INFORMATION TECHNOLOGY AND COMPUTER ENGINEERING (EITCE 2017), 2017, 128
  • [8] ν-twin support vector machine with Universum data for classification
    Yitian Xu
    Mei Chen
    Zhiji Yang
    Guohui Li
    [J]. Applied Intelligence, 2016, 44 : 956 - 968
  • [9] ν-twin support vector machine with Universum data for classification
    Xu, Yitian
    Chen, Mei
    Yang, Zhiji
    Li, Guohui
    [J]. APPLIED INTELLIGENCE, 2016, 44 (04) : 956 - 968
  • [10] Robust twin support vector machine for pattern classification
    Qi, Zhiquan
    Tian, Yingjie
    Shi, Yong
    [J]. PATTERN RECOGNITION, 2013, 46 (01) : 305 - 316
12345