Open Access   Article Go Back

Unique Finger Correctness Detection Using CNN

Sneha Sadula1 , N V Sailaja2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 1229-1234, Jul-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i7.12291234

Online published on Jul 31, 2018

Copyright © Sneha Sadula, N V Sailaja . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Sneha Sadula, N V Sailaja, “Unique Finger Correctness Detection Using CNN,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1229-1234, 2018.

MLA Style Citation: Sneha Sadula, N V Sailaja "Unique Finger Correctness Detection Using CNN." International Journal of Computer Sciences and Engineering 6.7 (2018): 1229-1234.

APA Style Citation: Sneha Sadula, N V Sailaja, (2018). Unique Finger Correctness Detection Using CNN. International Journal of Computer Sciences and Engineering, 6(7), 1229-1234.

BibTex Style Citation:
@article{Sadula_2018,
author = {Sneha Sadula, N V Sailaja},
title = {Unique Finger Correctness Detection Using CNN},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2018},
volume = {6},
Issue = {7},
month = {7},
year = {2018},
issn = {2347-2693},
pages = {1229-1234},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2589},
doi = {https://doi.org/10.26438/ijcse/v6i7.12291234}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i7.12291234}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2589
TI - Unique Finger Correctness Detection Using CNN
T2 - International Journal of Computer Sciences and Engineering
AU - Sneha Sadula, N V Sailaja
PY - 2018
DA - 2018/07/31
PB - IJCSE, Indore, INDIA
SP - 1229-1234
IS - 7
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
466 406 downloads 208 downloads
  
  
           

Abstract

Motivated by increasing in the usage of statistics systems from few years, spoof fingerprint detection has aging regularly. This uses CNN for the detection of thumbprint vitality. It compares 4 different models: Convolutional neural networks fine-tuned with thumbprint images and CNN pretrained on natural images, CNN with erratic weights, and LBP. Offensive thumbprint-based biometry organizations through awarding mock thumbs next to the radar can stand a thoughtful hazard intended for abandoned submission. Dataset expansion stood cast-off towards growth classifier’s recital besides a variability of preprocessing practice stayed confirmed, aforesaid as occurrence riddling, distinction mathematical besides county appertaining to curiosity.

Key-Words / Index Term

Thumbprint acknowledgement, SVM, convolutional neural networks, appliance erudition

References

[1] A. Antonelli, R. Cappelli, D. Maio, and D. Maltoni, “Fake finger detection by skin distortion analysis,” IEEE Trans. Inf. Forensics Security, vol. 1, no. 3, pp. 360–373, Sep. 2006.
[2] D. Gragnaniello, G. Poggi, C. Sansone, and L. Verdoliva, “Fingerprint liveness detection based on weber local image descriptor,” in Proc. IEEE Workshop Biometric Meas. Syst. Secur. Med. Appl. (BIOMS), Sep. 2013, pp. 46–50.
[3] D. Menotti et al., “Deep representations for iris, face, and fingerprint spoofing detection,” IEEE Trans. Inf. Forensics Security, vol. 10, no. 4, pp. 864–879, Apr. 2015.
[4] X. Jia et al., “Multi-scale local binary pattern with filters for spoof fingerprint detection,” Inf. Sci., vol. 268, pp. 91–102, Jun. 2014.
[5] L. Ghiani, G. L. Marcialis, and F. Roli, “Fingerprint liveness detection by local phase quantization,” in Proc. 21st Int. Conf. Pattern Recognit. (ICPR), 2012, pp. 537–540.
[6] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1097–1105.
[7] T. Ahonen, A. Hadid, and M. Pietikäinen, “Face recognition with local binary patterns,” in Computer Vision. Heidelberg, Germsany: Springer, 2004, pp. 469–481.
[8] A. K. Jain, Y. Chen, and M. Demirkus, “Pores and ridges: Highresolution fingerprint matching using level 3 features,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 1, pp. 15–27, Jan. 2007
[9] L. Ghiani, G. L. Marcialis, and F. Roli, “Fingerprint liveness detection by local phase quantization,” in Proc. 21st Int. Conf. Pattern Recognit. (ICPR), 2012.