Automatic Identification Fingerprint Based on Machine Learning Method

被引:0
|
作者
Long The Nguyen
Huong Thu Nguyen
Alexander Diomidovich Afanasiev
Tao Van Nguyen
机构
[1] Irkutsk National Research Technical University,Laboratory of Artificial Intelligence and Machine Learning, Institute of Information Technology and Data Science
[2] Irkutsk National Research Technical University,Baikal School of BRICS
[3] University of Information and Communication Technology,undefined
[4] Thai Nguyen University,undefined
来源
Journal of the Operations Research Society of China | 2022年 / 10卷
关键词
Fingerprint identification; Feature extraction; Image segmentation; Wavelet transform; Neural network algorithm; Machine learning; 68T45; 68Q32; 68W40;
D O I
暂无
中图分类号
学科分类号
摘要
The fingerprint identification technology has been developed and applied effectively to security systems in financial transactions, personal information security, national security, and other fields. In this paper, we proposed the development of a fingerprint identification system based on image processing methods that clarify fingerprint contours, using machine learning methods to increase processing speed and increase the accuracy of the fingerprint identification process. The identification system consists of the following main steps: improving image quality and image segmentation to identify the fingerprint area, extracting features, and matching the database. The accuracy of the system reached 97.75% on the mixed high- and low-quality fingerprint database.
引用
收藏
页码:849 / 860
页数:11
相关论文
共 50 条
  • [1] Automatic Identification Fingerprint Based on Machine Learning Method
    Nguyen, Long The
    Nguyen, Huong Thu
    Afanasiev, Alexander Diomidovich
    Nguyen, Tao Van
    JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA, 2022, 10 (04) : 849 - 860
  • [2] Deep Learning in Automatic Fingerprint Identification
    Wu, Chunsheng
    Wu, Honghao
    Song Lei
    Li, Xiaojun
    Hui Tong
    2021 IEEE 6TH INTERNATIONAL CONFERENCE ON SMART CLOUD (SMARTCLOUD 2021), 2021, : 111 - 116
  • [3] An Automatic Detection Method for Morse Signal Based on Machine Learning
    Wei, Zhihao
    Jia, Kebin
    Sun, Zhonghua
    ADVANCES IN INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING, PT II, 2018, 82 : 185 - 191
  • [4] Automatic Botnet Attack Identification Based on Machine Learning
    Li P.H.
    Xu J.
    Xu Z.Y.
    Chen S.
    Niu B.W.
    Yin J.
    Sun X.F.
    Lan H.L.
    Chen L.L.
    Computers, Materials and Continua, 2022, 73 (02) : 3847 - 3860
  • [5] A Web Application Fingerprint Recognition Method Based on Machine Learning
    Shi, Yanmei
    Yu, Wei
    Zhao, Yanxia
    Jia, Yungang
    CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 2024, 140 (01): : 887 - 906
  • [6] An Automatic Identification Method for the Blink Artifacts in the Magnetoencephalography with Machine Learning
    Feng, Yulong
    Xiao, Wei
    Wu, Teng
    Zhang, Jianwei
    Xiang, Jing
    Guo, Hong
    APPLIED SCIENCES-BASEL, 2021, 11 (05):
  • [7] A Machine Learning Based Method for Automatic Identification of Disaster Related Information Using Twitter Data
    Christidou, Athina Ntiana
    Drakaki, Maria
    Linardos, Vasileios
    INTELLIGENT AND FUZZY SYSTEMS: DIGITAL ACCELERATION AND THE NEW NORMAL, INFUS 2022, VOL 2, 2022, 505 : 70 - 76
  • [8] Segmentation Techniques through Machine Based Learning for Latent Fingerprint Indexing and Identification
    Singh, Harivans Pratap
    Dimri, Priti
    Tiwari, Shailesh
    Saraswat, Manish
    JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH, 2020, 79 (03): : 201 - 208
  • [9] Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning
    Xie, Lifang
    Luo, Siheng
    Liu, Yangyang
    Ruan, Xuejun
    Gong, Kedong
    Ge, Qiuyue
    Li, Kejian
    Valev, Ventsislav Kolev
    Liu, Guokun
    Zhang, Liwu
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2023, 57 (46) : 18203 - 18214
  • [10] A trimaran structure damage identification method based on machine learning
    Tang, Haoyun
    Ren, Deyuan
    Chen, Baiqiao
    Soares, C. Guedes
    OCEAN ENGINEERING, 2025, 320