Volume 10, Issue 1 (January 2023), Pages: 69-76
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Original Research Paper
Fingerprint classification combined with Gabor filter and convolutional neural network
Author(s): Zhengfang He 1, 2, *, Ivy Kim D. Machica 1, Jan Carlo T. Arroyo 3, 4, Ma. Luche P. Sabayle 5, Weibin Su 1, 2, Gang Xu 1, 2, Yikai Wang 2, Mingbo Pan 2, Allemar Jhone P. Delima 3
Affiliation(s):
1College of Information and Computing, University of Southeastern Philippines, Davao City, Davao del Sur, Philippines
2School of Intelligent Science and Engineering, Yunnan Technology and Business University, Kunming, China
3College of Information and Computing Studies, Northern Iloilo State University, Estancia, Iloilo, Philippines
4College of Computing Education, University of Mindanao, Davao City, Davao del Sur, Philippines
5College of Information and Communications Technology, West Visayas State University, Iloilo City, Philippines
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* Corresponding Author.
Corresponding author's ORCID profile: https://orcid.org/0000-0002-8075-4122
Digital Object Identifier:
https://doi.org/10.21833/ijaas.2023.01.010
Abstract:
A fingerprint is an impression left by the friction ridges of a human finger. A fingerprint classification system groups fingerprint according to their characteristics and therefore helps to match a fingerprint against an extensive database of fingerprints. The Henry classification system is widely used among fingerprint classification systems. Some researchers have used traditional machine learning or deep learning for fingerprint classification. Nevertheless, traditional algorithms cannot extract the depth features of the fingerprint, and most deep learning algorithms lack fingerprint image enhancement. So, this paper combined the Gabor Filter and Convolutional Neural Network to extract fingerprint features. The model has two channels, one is a Deep Convolutional Neural Network (DCNN), and the other is a Shallow Convolutional Neural Network (SCNN). The DCNN consists of a neural network with eight layers, which can extract deep features of the fingerprint. The SCNN consists of Gabor Filter and a neural network with two layers that can extract features from clear fingerprint images. This paper uses NIST Special Database 4 for experiments. Experimental results show that the model proposed in this paper has achieved 91.4% accuracy. Compared with other algorithms, this model has higher accuracy than others. It shows that combined with the Gabor Filter and Convolutional Neural Network can better extract the ridge features of fingerprint images.
© 2022 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Convolutional neural network, Fingerprint classification, Gabor filter, Image enhancement
Article History: Received 25 May 2022, Received in revised form 2 September 2022, Accepted 24 September 2022
Acknowledgment
No Acknowledgment.
Compliance with ethical standards
Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Citation:
He Z, Machica IKD, Arroyo JCT, Sabayle MLP, Su W, Xu G, Wang Y, Pan M, and Delima AJP (2023). Fingerprint classification combined with Gabor filter and convolutional neural network. International Journal of Advanced and Applied Sciences, 10(1): 69-76
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Figures
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Tables
Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7
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