International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 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

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7

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 References (27)

  1. Agarap AF (2018). Deep learning using rectified linear units (ReLU). ArXiv Preprint ArXiv: 1803.08375. https://doi.org/10.48550/arXiv.1803.08375   [Google Scholar]
  2. Cao K, Pang L, Liang J, and Tian J (2013). Fingerprint classification by a hierarchical classifier. Pattern Recognition, 46(12): 3186-3197. https://doi.org/10.1016/j.patcog.2013.05.008   [Google Scholar]
  3. Chang JH and Fan KC (2002). A new model for fingerprint classification by ridge distribution sequences. Pattern Recognition, 35(6): 1209-1223. https://doi.org/10.1016/S0031-3203(01)00121-2   [Google Scholar]
  4. Chen G, Jiang Z, and Kamruzzaman MM (2020). Radar remote sensing image retrieval algorithm based on improved Sobel operator. Journal of Visual Communication and Image Representation, 71: 102720. https://doi.org/10.1016/j.jvcir.2019.102720   [Google Scholar]
  5. Ding S, Shi S, and Jia W (2020). Research on fingerprint classification based on twin support vector machine. IET Image Processing, 14(2): 231-235. https://doi.org/10.1049/iet-ipr.2018.5977   [Google Scholar]
  6. Faulds H (1880). On the skin-furrows of the hand. Nature, 22(574): 605-605. https://doi.org/10.1038/022605a0   [Google Scholar]
  7. Gabor D (1946). Theory of communication. Part 1: The analysis of information. Journal of the Institution of Electrical Engineers-Part III: Radio and Communication Engineering, 93(26): 429-441. https://doi.org/10.1049/ji-3-2.1946.0074   [Google Scholar]
  8. Guo JM, Liu YF, Chang JY, and Lee JD (2014). Fingerprint classification based on decision tree from singular points and orientation field. Expert Systems with Applications, 41(2): 752-764. https://doi.org/10.1016/j.eswa.2013.07.099   [Google Scholar]
  9. Ibrahim AM, Eesee AK, and Al-Nima RRO (2021). Deep fingerprint classification network. TELKOMNIKA (Telecommunication Computing Electronics and Control), 19(3): 893-901. https://doi.org/10.12928/telkomnika.v19i3.18771   [Google Scholar]
  10. Jian W, Zhou Y, and Liu H (2020). Lightweight convolutional neural network based on singularity ROI for fingerprint classification. IEEE Access, 8: 54554-54563. https://doi.org/10.1109/ACCESS.2020.2981515   [Google Scholar]
  11. Jiang X (2015). Fingerprint classification. In: Li SZ and Jain AK (Eds.), Encyclopedia of biometrics: 584-592. Springer Science and Business Media, Berlin, Germany. https://doi.org/10.1007/978-1-4899-7488-4_56   [Google Scholar]
  12. Kanopoulos N, Vasanthavada N, and Baker RL (1988). Design of an image edge detection filter using the Sobel operator. IEEE Journal of Solid-State Circuits, 23(2): 358-367. https://doi.org/10.1109/4.996   [Google Scholar]
  13. Karu K and Jain AK (1996). Fingerprint classification. Pattern Recognition, 29(3): 389-404. https://doi.org/10.1016/0031-3203(95)00106-9   [Google Scholar]
  14. Leung KC and Leung CH (2010). Improvement of fingerprint retrieval by a statistical classifier. IEEE Transactions on Information Forensics and Security, 6(1): 59-69. https://doi.org/10.1109/TIFS.2010.2100382   [Google Scholar]
  15. Li SZ and Jain AK (2015). Encyclopedia of biometrics. Springer Science and Business Media, Berlin, Germany. https://doi.org/10.1007/978-1-4899-7488-4   [Google Scholar]
  16. Moenssens AA (1971). Fingerprint techniques. Chilton Book Company, Boston, USA.   [Google Scholar]
  17. Rao K and Balck K (1980). Type classification of fingerprints: A syntactic approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-2(3): 223-231. https://doi.org/10.1109/TPAMI.1980.4767009   [Google Scholar] PMid:21868895
  18. Rim B, Kim J, and Hong M (2020). Gender classification from fingerprint-images using deep learning approach. In The International Conference on Research in Adaptive and Convergent Systems, Association for Computing Machinery, Gwangju, Korea: 7-12. https://doi.org/10.1145/3400286.3418237   [Google Scholar]
  19. Rim B, Kim J, and Hong M (2021). Fingerprint classification using deep learning approach. Multimedia Tools and Applications, 80(28): 35809-35825. https://doi.org/10.1007/s11042-020-09314-6   [Google Scholar]
  20. Valueva MV, Nagornov NN, Lyakhov PA, Valuev GV, and Chervyakov NI (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation, 177: 232-243. https://doi.org/10.1016/j.matcom.2020.04.031   [Google Scholar]
  21. Wang X, Wang F, Fan J, and Wang J (2009). Fingerprint classification based on continuous orientation field and singular points. In the 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, IEEE, Shanghai, China: 189-193. https://doi.org/10.1109/ICICISYS.2009.5357702   [Google Scholar]
  22. Watson CI and Wilson CL (1992). NIST special database 4. Fingerprint Database, National Institute of Standards and Technology, 17(77): 5.   [Google Scholar]
  23. Wu F, Zhu J, and Guo X (2020). Fingerprint pattern identification and classification approach based on convolutional neural networks. Neural Computing and Applications, 32(10): 5725-5734. https://doi.org/10.1007/s00521-019-04499-w   [Google Scholar]
  24. Yager N and Amin A (2004). Fingerprint verification based on minutiae features: A review. Pattern Analysis and Applications, 7(1): 94-113. https://doi.org/10.1007/s10044-003-0201-2   [Google Scholar]
  25. Zhang W, Doi K, Giger ML, Nishikawa RM, and Schmidt RA (1996). An improved shift‐invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms. Medical Physics, 23(4): 595-601. https://doi.org/10.1118/1.597891   [Google Scholar] PMid:8860907
  26. Zhengfang H, Delima AJP, Machica IKD, Arroyo JCT, Weibin S, and Gang X (2022). Fingerprint identification based on novel siamese rectangular convolutional neural networks. International Journal of Emerging Technology and Advanced Engineering, 12(5): 28-37. https://doi.org/10.46338/ijetae0522_04   [Google Scholar]
  27. Zia T, Ghafoor M, Tariq SA, and Taj IA (2019). Robust fingerprint classification with Bayesian convolutional networks. IET Image Processing, 13(8): 1280-1288. https://doi.org/10.1049/iet-ipr.2018.5466   [Google Scholar]