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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM18509

Machine Learning Based Non Live Finger Print Detection

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM18509

Volume:

7 |

3 |

Mar 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

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

M

Mrs.S.Subhashini

D

Dr.P.Umamaheswari

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Abstract

In recent days, liveness detection of finger print image has become very essential in finger print recognition systems because fake finger prints are used in lieu of real finger prints. Many machine learning(ML) techniques have been widely used for non live finger print image detection because these techniques provide high accurate identification and also cost effective. These techniques also enhance the accuracy of classification of real and spoof finger print images. In this article , literature review is done about machine learning (ML) and its algorithms used for the detection of non live finger print. The main objective of this article is to compare and analyse various ML techniques used for spoof detection. It also provides an overview of performance merits and limitations of ML algorithms used in non live finger print detection .

KEYWORDS

Non live finger print liveness detection Machine learning anti spoofing spoof detection

Cite This Article

Mrs.S.Subhashini, Dr.P.Umamaheswari, "Machine Learning Based Non Live Finger Print Detection," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 3, Mar 2023. https://doi.org/10.55041/IJSREM18509

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Publication Date

1/3/2023

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Double-Blind Peer Review

Review Rounds: 2

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