FINGERPRINT IDENTIFICATION A Support Vector Machine Approach

被引：0
|
作者：
Kristensen, Terje [1 ]
机构：
[1] Bergen Univ Coll, Dept Comp Engn, Nygardsgaten 112, N-5020 Bergen, Norway
来源：
ICAART 2010: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1: ARTIFICIAL INTELLIGENCE | 2010年
关键词：
AFIS; SVM; Classification rate;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this work a hybrid technique for classification of fingerprint identification has been developed to decrease the matching time. For classification a Support Vector Machine is described and used. Automatic Fingerprint Identification Systems are widely used today, and it is therefore necessary to find a classification system that is less time-consuming. The given fingerprint database is decomposed into four different subclasses and a SVM algorithm is used to train the system to do correct classification. The classification rate has been estimated to about 87.0 % of unseen fingerprints. The average matching time is decreased with a factor of about 3.5 compared to brute force search applied.
引用
收藏
页码：451 / 458
页数：8
相关论文
共 50 条
  • [21] A novel method of process dead-time identification: Support vector machine approach
    Zhu, HD
    Shao, HH
    PROCEEDINGS OF THE 2004 AMERICAN CONTROL CONFERENCE, VOLS 1-6, 2004, : 880 - 884
  • [22] A SUPPORT VECTOR MACHINE APPROACH TO THE AUTOMATIC IDENTIFICATION OF FLUORESCENCE SPECTRA EMITTED BY BIOLOGICAL AGENTS
    Gelfusa, M.
    Murari, A.
    Lungaroni, M.
    Malizia, A.
    Parracino, S.
    Peluso, E.
    Cenciarelli, O.
    Carestia, M.
    Pizzoferrato, R.
    Vega, J.
    Gaudio, P.
    OPTICS AND PHOTONICS FOR COUNTERTERRORISM, CRIME FIGHTING, AND DEFENCE XII, 2016, 9995
  • [23] A support vector machine approach for detection of microcalcifications
    El-Naqa, I
    Yang, YY
    Wernick, MN
    Galatsanos, NP
    Nishikawa, RM
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2002, 21 (12) : 1552 - 1563
  • [24] A minimum seeking approach for support vector machine
    Peng, PY
    Zhang, YP
    Soft Computing with Industrial Applications, Vol 17, 2004, 17 : 143 - 148
  • [25] New approach to training support vector machine
    Tang Faming
    Journal of Systems Engineering and Electronics, 2006, (01) : 200 - 205
  • [26] A support vector machine approach to decision trees
    Bennett, KP
    Blue, JA
    IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE, 1998, : 2396 - 2401
  • [27] Support vector machine approach for fast classification
    Kianmehr, Keivan
    Alhajj, Reda
    DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS, 2006, 4081 : 534 - 543
  • [28] Local Binary Pattern with Hyperparameter Tuned Support Vector Machine for Fingerprint Classification
    Chougule, Abhijeet
    Shah, Medha
    PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICCS), 2019, : 1084 - 1087
  • [29] Partial discharge identification using a support vector machine
    Hao, L
    Lewin, PL
    Tian, Y
    Dodd, SJ
    2005 ANNUAL REPORT CONFERENCE ON ELECTRICAL INSULATION AND DIELECTRIC PHENOMENA, 2005, : 414 - 417
  • [30] Fever Identification of Pigs Based on Support Vector Machine
    Zhu Weixing
    Wang Wei
    2011 INTERNATIONAL CONFERENCE ON COMPUTERS, COMMUNICATIONS, CONTROL AND AUTOMATION (CCCA 2011), VOL II, 2010, : 177 - 180
12345