Proceedings of the 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019)

Research on the Finger Knuckle Print Recognition Method Based on Improved Canny Operator

Authors
Xiufeng Zhang, Wei Wang, Qiang Ding, Tianyi Ma, Chunyang Hao
Corresponding Author
Wei Wang
Available Online 24 December 2019.
DOI
https://doi.org/10.2991/acsr.k.191223.037How to use a DOI?
Keywords
canny, the finger knuckle print, ROI, daptive median filtering
Abstract

In order to improve the accuracy of the classification and classification of the finger knuckle print, a method based on the improved Canny operator for the edge detection of the Finger Knuckle Print image is proposed. Firstly, the region of interest (ROI) of the Finger Knuckle Print is positioned. Secondly, The method optimized the edge detection of the finger knuckle print images from two aspects: traditional Canny algorithm filtering, gradient direction. For the poor performance of Gauss filter used by traditional Canny algorithm in limitation of removing gauss noise and loss of edge details, Adaptive median filtering is used instead of Gaussian filtering for filtering. For the problem that the Canny algorithm is easy to detect the false edge, the edge was refined by adding the direction gradient template in the process of calculating the gradient direction of the image. Finally, information entropy is used to illustrate the effectiveness of the algorithm. Compared with traditional operators, this method has better stability and robustness.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019)
Series
Advances in Computer Science Research
Publication Date
24 December 2019
ISBN
978-94-6252-873-4
ISSN
2352-538X
DOI
https://doi.org/10.2991/acsr.k.191223.037How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xiufeng Zhang
AU  - Wei Wang
AU  - Qiang Ding
AU  - Tianyi Ma
AU  - Chunyang Hao
PY  - 2019
DA  - 2019/12/24
TI  - Research on the Finger Knuckle Print Recognition Method Based on Improved Canny Operator
BT  - Proceedings of the 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019)
PB  - Atlantis Press
SP  - 160
EP  - 163
SN  - 2352-538X
UR  - https://doi.org/10.2991/acsr.k.191223.037
DO  - https://doi.org/10.2991/acsr.k.191223.037
ID  - Zhang2019
ER  -