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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM11836

Fingerprint Detection using CNN

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM11836

Volume:

6 |

3 |

Mar 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM11836

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KAS

K. Abhijith Saralaya

TPL

T Prasanna Lakshmi

SD

Sudeepthi Devasari

RR

R Rajini

SSP

Srigiri Shree Puruhoothi

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Abstract

. For biometric recognition, fingerprints are often used. Spoofing attacks based on a synthetic fingerprint, on the other hand, are frequent. We present in this research a method for detecting fingerprints that employ guided filtering and hybrid image analysis. When examining a denoised image, the problem of neglecting the contribution of sharp features is addressed in this study. The method described in this project uses the increased sharp features as well as the denoised features from the hybrid images to get better outcomes.

KEYWORDS

Hybrid image analysis Guided filtering Convolutional Neural Networks.

Cite This Article

K. Abhijith Saralaya, T Prasanna Lakshmi, Sudeepthi Devasari, R Rajini, Srigiri Shree Puruhoothi, "Fingerprint Detection using CNN," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 3, Mar 2022. https://doi.org/10.55041/IJSREM11836

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Publication Date

3/14/2022

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Double-Blind Peer Review

Review Rounds: 2

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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