Feature selection and extraction using decomposition techniques in biometric authentication

Y SURESH; PAVAN KUMAR K; PESN KRISHNA PRASAD; AKKINENI HARITHA

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Feature selection and extraction using decomposition techniques in biometric authentication

Author(s)

Y SURESH Department of IT, Prasad V. Potluri Siddhartha Institute of Technology, India

PAVAN KUMAR K Department of IT, Prasad V. Potluri Siddhartha Institute of Technology, India

PESN KRISHNA PRASAD Department of CSE, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India.

AKKINENI HARITHA Department of IT, Prasad V. Potluri Siddhartha Institute of Technology, India

Abstract

Pattern recognition is a very significant and the most prominent emerging technology which aims at analysis or investigation and construction of pattern. It is highly complex phenomena. Vector logic would provide better strategies and yield results for this problem. Generally, in several phases of human beings life, accurate uniqueness validation seems to be critical. Before the emergence of computing revolution, the issue security is for individual ensured after checking the someone in person and also with the help of the signature. It is observed that conventional methods of authentication are unproductive because any person can impress as a real person with the help of therapeutic procedure and through spoofing. At present, authentication is made through offline and or online mode taking the distinctive features like biometrics of a person. The main use of the distinctive or unique features is that no one can duplicate the features of original human being. In the event of processing the biometric qualities, it is a complex process to derive authentication. In order to enhance accuracy, researchers have proposed diverse types of algorithms. During this process, finger and face traits of a person are considered and also , and applications of Kronecker Product(KP) such as Khatri Rao Product are used. And then, two multimodal authentication systems using AT&T, FERET and Yale data sets are implemented in MATLAB, Python.

Publication Details

Page(s) 3686-3695

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 11, Year: 2022

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Keywords

SVD, LU factorization, LU factorization, LU factorization, LU factorization, LU factorization, LU factorization, LU factorization, LU factorization, LU factorization, LU factorization, LU factorization, Kronecker Product, Khatri Rao Product, PyParSVD, Biometric

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