Multimodal face and fingerprint authentication system using fuzzy set exponential water wave optimization (Record no. 22865)

MARC details
000 -LEADER
fixed length control field a
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250515122455.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250515b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 26209
Author Deol, G.Joel Sunny
245 ## - TITLE STATEMENT
Title Multimodal face and fingerprint authentication system using fuzzy set exponential water wave optimization
250 ## - EDITION STATEMENT
Volume, Issue number Vol.105(6), Dec
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Mumbai
Name of publisher, distributor, etc. Springer
Year 2024
300 ## - PHYSICAL DESCRIPTION
Pagination 1743-1756p.
520 ## - SUMMARY, ETC.
Summary, etc. Especially in the financial sector and law enforcement, biometric authentication technologies are becoming crucial. Unimodal systems were utilised in the past for biometrics, but multimodal biometrics is now in vogue. Multimodal biometrics is a crucial part of pattern recognition and has received a lot of attention in the scientific community. Although multimodality is frequently manipulated by unimodal systems, they frequently accomplish numerous tasks. In order to obtain high classification accuracy and low mistake rate, this research looks into the safe authentication procedure of a multimodal system employing fingerprint and facial recognition. However, the developed optimisation method's method for carrying out the fusion process involves the chance selection of two logical operators. The exponentially weighted moving average (EWMA) and WWO (water wave optimisation) are combined in order to create the scale-invariant feature transformation based fuzzy set exponential water wave optimisation (FEWWO). SIFT and FEWWO of face category, fingerprint ridge, and feature extraction are used to optimise and extract features. On the basis of the FVC200 fingerprint and Ollivati face datasets, the suggested solution is assessed. By obtaining low false positive and rejection rates, the suggested method can improve the realism of multimodal systems and reach our desired target of accuracy of about 99.2%.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4619
Topical term or geographic name entry element EXTC Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 26210
Co-Author Priyadarsini, Pullagura Indira
773 0# - HOST ITEM ENTRY
International Standard Serial Number 2250-2106
Title Journal of the institution of engineers (India): Series B
856 ## - ELECTRONIC LOCATION AND ACCESS
URL https://link.springer.com/article/10.1007/s40031-024-01073-4
Link text Click here
942 ## - ADDED ENTRY ELEMENTS (KOHA)
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    Dewey Decimal Classification     School of Engineering & Technology School of Engineering & Technology Archieval Section 15/05/2025   2025-0837 15/05/2025 15/05/2025 Articles Abstract Database
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