Title: Leveraging bio-maximum inverse rank method for iris and palm recognition

Authors: Mallikarjuna A. Reddy; Sudheer K. Reddy; Santhosh C.N. Kumar; Srinivasa K. Reddy

Addresses: Computer Science and Engineering, Anurag University, Hyderabad, India ' Information Technology, Anurag University, Telangana 500088, India ' Computer Science and Engineering, Anurag Engineering College, Kodad, India ' Computer Science and Engineering, BVRIT Hyderabad College of Engineering for Women, Hyderabad, India

Abstract: Biometrics is vital to recognise and confirm the identity of human beings by estimating and distinguishing the natural qualities that include iris, retinal, face recognition, fingerprint, palm detection and others. Numerous biometric designs and frameworks are most successful in distinguishing human identities by employing several techniques. In this article, the authors present bi-modular biometric frameworks. For iris and ribbon print, a bi-modular biometric is employed. Wavelet and Gabor-edge channels are employed to separate highlights in various balances. This article aims to present the BMIR (bio maximum inverse rank) model that is vigorous regarding varieties in scores and other factors of a module. Category support and choice-based strategies are employed to join the magnitudes of these modules. The authors have employed three data sets to carry out the investigation effectively. The investigation shows the accuracy, sufficiency and appropriateness of the proposed hybrid model when compared with the existing frameworks.

Keywords: ranking; iris recognition; biometrics; knowledge acquisition; neural network.

DOI: 10.1504/IJBM.2022.124681

International Journal of Biometrics, 2022 Vol.14 No.3/4, pp.421 - 438

Received: 02 Jan 2021
Accepted: 14 Feb 2021

Published online: 05 Aug 2022 *

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