Fingerprint Classification Using Deep Convolutional Neural Network

Mohamed Hirsi Mohamed

doi:doi:10.11648/j.jeee.20210905.11

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Fingerprint Classification Using Deep Convolutional Neural Network

Mohamed Hirsi Mohamed

Published in Journal of Electrical and Electronic Engineering (Volume 9, Issue 5)

Received: 5 August 2021 Accepted: 16 August 2021 Published: 6 September 2021

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Abstract

Fingerprint classification is a method of reducing the number of candidates needed by fingerprint recognition systems to determine if a fingerprint picture matches one in the database. Deep learning has gained a lot of attraction in the recent decade including natural language processing, digital image processing, speech recognition, handwritten digit recognition, medical picture assessments, and so on. The subject of this paper is to explore the factors affecting fingerprint classification using a convolutional neural network and to train and test a deep CNN model, The CNN model includes two serial stages, a preprocessing phase which is used to enhance the fingerprint images qualities, and post-processing phase which used to train the classification model. This has been accomplished by designing a new deep convolutional neural network model for this work. The Convolutional neural network model achieved outstanding classification accuracy on the fingerprint. This experiment used the NIST DB4 dataset which contains 4,000 fingerprints images with five labels. Separately, each label of this database comprises almost 800 fingerprint samples with dimension of 512 x 512. To lower the training time required we reduced the fingerprint images up to 200 x 200 dimension. the study achieves 99.2% of classification accuracy with a zero-rejection rate.

Published in	Journal of Electrical and Electronic Engineering (Volume 9, Issue 5)
DOI	10.11648/j.jeee.20210905.11
Page(s)	147-152
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Biometric, Fingerprint, Convolutional Neural Network, Overfitting, Classification

References

[1]	FBI, (2014). Integrated Automated Fingerprint Identification System. Federal Bureau of investigation. Available at: https://www.fbi.gov/services/information management/foipa/privacy-impact-assessments/iafis [Accessed 19 Feb. 2020].
[2]	Wilson, C., Candela, G., and Watson, C. (1994). Neural network fingerprint classification. Journal of Artificial Neural Networks 1.2, pp. 203-228.
[3]	J. M. Shrein, "Fingerprint classification using convolutional neural networks and ridge orientation images," 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, 2017, pp. 1-8.
[4]	W. Jeon and S. Rhee, "Fingerprint Pattern Classification Using Convolution Neural Network", international journal of fuzzy logic and intelligent systems, vol. 17, no. 3, pp. 170-176, 2017. Available: 10.5391/ijfis.2017.17.3.170.
[5]	B. Bakhshi and H. Veisi, "End to End Fingerprint Verification Based on Convolutional Neural Network", in 27th Iranian Conference on Electrical Engineering (ICEE2019), Tehran, 2019, pp. 1994 - 1998.
[6]	C. Watson, "NIST Special Database 4. NIST 8-Bit Gray Scale Images of Fingerprint Image Groups", NIST, 2020. [Online]. Available: https://www.nist.gov/publications/nist-special-database-4-nist-8-bit-gray-scale-images-fingerprint-image-groups. [Accessed: 01- Nov- 2019].
[7]	P. Puneet and N. Garg, "Binarization Techniques used for Grey Scale Images", International Journal of Computer Applications, vol. 71, no. 1, pp. 8-11, 2013. Available: 10.5120/12320-8533 [Accessed 30 June 2020].
[8]	Michelsanti, D., Ene, A-D., Guichi, Y., Stef, R., Nasrollahi, K., & Moeslund, T. B. (2017). Fast Fingerprint Classification with Deep Neural Networks. In F. Imai, A. Tremeau, & J. Braz (Eds.), VISiGRAPP 2017: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Vol. 5, pp. 202-209). SCITEPRESS Digital Library. https://doi.org/10.5220/0006116502020209
[9]	Zhong Z, Zheng L, Kang G, Li S, Yang Y (2017) Random erasing data augmentation. arXiv. Available online at: https://arxiv.org/pdf/ 1708.04896.pdf. [Accessed 16 Feb 2020].
[10]	M. Galar et al., "A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal", Knowledge-Based Systems, vol. 81, pp. 98-116, 2015. Available: 10.1016/j.knosys.2015.02.015 [Accessed 1 March 2020].
[11]	Peralta, D., Triguero, I., Carcia, S., Saeys, Y., Benirez J. and Herrera, F. (2017). Robust classification of different fingerprint copies with deep neural networks for database penetration rate reduction.
[12]	Yaniv Taigman, et al. (2014). DeepFace: Closing the Gap to Human-Level Performance in Face Verification. Conference on Computer Vision and Pattern Recognition (CVPR).
[13]	Guilhem Cheron, Ivan Laptev, Cordelia Schmid. (2015). P-CNN: Pose-Based CNN Features for Action Recognition. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3218-3226.

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Mohamed Hirsi Mohamed. (2021). Fingerprint Classification Using Deep Convolutional Neural Network. Journal of Electrical and Electronic Engineering, 9(5), 147-152. https://doi.org/10.11648/j.jeee.20210905.11

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Mohamed Hirsi Mohamed. Fingerprint Classification Using Deep Convolutional Neural Network. J. Electr. Electron. Eng. 2021, 9(5), 147-152. doi: 10.11648/j.jeee.20210905.11

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AMA Style

Mohamed Hirsi Mohamed. Fingerprint Classification Using Deep Convolutional Neural Network. J Electr Electron Eng. 2021;9(5):147-152. doi: 10.11648/j.jeee.20210905.11

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@article{10.11648/j.jeee.20210905.11,
  author = {Mohamed Hirsi Mohamed},
  title = {Fingerprint Classification Using Deep Convolutional Neural Network},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {9},
  number = {5},
  pages = {147-152},
  doi = {10.11648/j.jeee.20210905.11},
  url = {https://doi.org/10.11648/j.jeee.20210905.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20210905.11},
  abstract = {Fingerprint classification is a method of reducing the number of candidates needed by fingerprint recognition systems to determine if a fingerprint picture matches one in the database. Deep learning has gained a lot of attraction in the recent decade including natural language processing, digital image processing, speech recognition, handwritten digit recognition, medical picture assessments, and so on. The subject of this paper is to explore the factors affecting fingerprint classification using a convolutional neural network and to train and test a deep CNN model, The CNN model includes two serial stages, a preprocessing phase which is used to enhance the fingerprint images qualities, and post-processing phase which used to train the classification model. This has been accomplished by designing a new deep convolutional neural network model for this work. The Convolutional neural network model achieved outstanding classification accuracy on the fingerprint. This experiment used the NIST DB4 dataset which contains 4,000 fingerprints images with five labels. Separately, each label of this database comprises almost 800 fingerprint samples with dimension of 512 x 512. To lower the training time required we reduced the fingerprint images up to 200 x 200 dimension. the study achieves 99.2% of classification accuracy with a zero-rejection rate.},
 year = {2021}
}

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TY  - JOUR
T1  - Fingerprint Classification Using Deep Convolutional Neural Network
AU  - Mohamed Hirsi Mohamed
Y1  - 2021/09/06
PY  - 2021
N1  - https://doi.org/10.11648/j.jeee.20210905.11
DO  - 10.11648/j.jeee.20210905.11
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 147
EP  - 152
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20210905.11
AB  - Fingerprint classification is a method of reducing the number of candidates needed by fingerprint recognition systems to determine if a fingerprint picture matches one in the database. Deep learning has gained a lot of attraction in the recent decade including natural language processing, digital image processing, speech recognition, handwritten digit recognition, medical picture assessments, and so on. The subject of this paper is to explore the factors affecting fingerprint classification using a convolutional neural network and to train and test a deep CNN model, The CNN model includes two serial stages, a preprocessing phase which is used to enhance the fingerprint images qualities, and post-processing phase which used to train the classification model. This has been accomplished by designing a new deep convolutional neural network model for this work. The Convolutional neural network model achieved outstanding classification accuracy on the fingerprint. This experiment used the NIST DB4 dataset which contains 4,000 fingerprints images with five labels. Separately, each label of this database comprises almost 800 fingerprint samples with dimension of 512 x 512. To lower the training time required we reduced the fingerprint images up to 200 x 200 dimension. the study achieves 99.2% of classification accuracy with a zero-rejection rate.
VL  - 9
IS  - 5
ER  -

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Author Information

Mohamed Hirsi Mohamed

Department of Electrical and Electronics Engineering, Cyprus International University, North Cyprus, Turkey

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Plain Text BibTeX RIS

APA Style

Mohamed Hirsi Mohamed. (2021). Fingerprint Classification Using Deep Convolutional Neural Network. Journal of Electrical and Electronic Engineering, 9(5), 147-152. https://doi.org/10.11648/j.jeee.20210905.11

Copy | Download

ACS Style

Mohamed Hirsi Mohamed. Fingerprint Classification Using Deep Convolutional Neural Network. J. Electr. Electron. Eng. 2021, 9(5), 147-152. doi: 10.11648/j.jeee.20210905.11

Copy | Download

AMA Style

Mohamed Hirsi Mohamed. Fingerprint Classification Using Deep Convolutional Neural Network. J Electr Electron Eng. 2021;9(5):147-152. doi: 10.11648/j.jeee.20210905.11

Copy | Download

@article{10.11648/j.jeee.20210905.11,
  author = {Mohamed Hirsi Mohamed},
  title = {Fingerprint Classification Using Deep Convolutional Neural Network},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {9},
  number = {5},
  pages = {147-152},
  doi = {10.11648/j.jeee.20210905.11},
  url = {https://doi.org/10.11648/j.jeee.20210905.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20210905.11},
  abstract = {Fingerprint classification is a method of reducing the number of candidates needed by fingerprint recognition systems to determine if a fingerprint picture matches one in the database. Deep learning has gained a lot of attraction in the recent decade including natural language processing, digital image processing, speech recognition, handwritten digit recognition, medical picture assessments, and so on. The subject of this paper is to explore the factors affecting fingerprint classification using a convolutional neural network and to train and test a deep CNN model, The CNN model includes two serial stages, a preprocessing phase which is used to enhance the fingerprint images qualities, and post-processing phase which used to train the classification model. This has been accomplished by designing a new deep convolutional neural network model for this work. The Convolutional neural network model achieved outstanding classification accuracy on the fingerprint. This experiment used the NIST DB4 dataset which contains 4,000 fingerprints images with five labels. Separately, each label of this database comprises almost 800 fingerprint samples with dimension of 512 x 512. To lower the training time required we reduced the fingerprint images up to 200 x 200 dimension. the study achieves 99.2% of classification accuracy with a zero-rejection rate.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Fingerprint Classification Using Deep Convolutional Neural Network
AU  - Mohamed Hirsi Mohamed
Y1  - 2021/09/06
PY  - 2021
N1  - https://doi.org/10.11648/j.jeee.20210905.11
DO  - 10.11648/j.jeee.20210905.11
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 147
EP  - 152
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20210905.11
AB  - Fingerprint classification is a method of reducing the number of candidates needed by fingerprint recognition systems to determine if a fingerprint picture matches one in the database. Deep learning has gained a lot of attraction in the recent decade including natural language processing, digital image processing, speech recognition, handwritten digit recognition, medical picture assessments, and so on. The subject of this paper is to explore the factors affecting fingerprint classification using a convolutional neural network and to train and test a deep CNN model, The CNN model includes two serial stages, a preprocessing phase which is used to enhance the fingerprint images qualities, and post-processing phase which used to train the classification model. This has been accomplished by designing a new deep convolutional neural network model for this work. The Convolutional neural network model achieved outstanding classification accuracy on the fingerprint. This experiment used the NIST DB4 dataset which contains 4,000 fingerprints images with five labels. Separately, each label of this database comprises almost 800 fingerprint samples with dimension of 512 x 512. To lower the training time required we reduced the fingerprint images up to 200 x 200 dimension. the study achieves 99.2% of classification accuracy with a zero-rejection rate.
VL  - 9
IS  - 5
ER  -

Copy | Download