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Adaptive fuzzy genetic algorithm for multi biometric authentication

  • N. MalarvizhiEmail author
  • P. Selvarani
  • Pethuru Raj
Article
  • 8 Downloads

Abstract

Biometric Authentication (BA) has turn out to be presently as key problem in privacy and security. Multimodal biometric system specializes in enhancing verification overall performance of the users for authentication. On this direction, biometrics which is the computer-based validation of an individuals’ identification is turning into increasingly more vital, particularly for high security systems. The spirit of biometrics is the size of character’s behavioral or physiological characteristics; it allows authentication of someone’s identity. Biometric-based totally authentication is likewise turning into increasingly more essential in computer based applications because the quantity of touchy records saved in such structures is growing. The latest needs of biometric systems are robustness, high reputation quotes, capability to handle imprecision, uncertainties of non-statistical kind and magnanimous flexibility. Its miles precisely right here that, the role of soft computing techniques involves vital play. The primary aim of this write-up is to offer a practical view on applications of soft computing strategies in biometrics and to analyze its impact. It is found that soft computing has already made inroads in phrases of man or woman techniques or in combination. This paper additionally proposes as hybrid soft computing based optimization device named “Adaptive Fuzzy Genetic Algorithm (AFGA)” which is adaptable to all of the unimodal and multimodal biometric authentication system. The results acquired by means of this device insure high standard of verification via multi-modal biometrics fusion by means of powerful functionality of fuzzy logic. Experimental investigation under various biometric data conditions exhibits notable effects over current strategies.

Keywords

Multimodal biometric system Unimodal biometric system Fuzzy system Genetic algorithm Adaptive fuzzy genetic algorithm (AFGA) Fingerprint recognition Iris recognition 

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, AvadiChennaiIndia
  2. 2.Site Reliability Engineering (SRE) DivisionReliance Jio Infocomm. Ltd. (RJIL), SARGOD ImperialBangaloreIndia

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