Title: A secure finger vein recognition system using WS-progressive GAN and C4 classifier

Authors: R. Sreemol; M.B. Santosh Kumar; A. Sreekumar

Addresses: Department of Computer Applications, Cochin University of Science and Technology, South Kalamassery, Kochi, Kerala, India ' Division of IT, School of Engineering, Cochin University of Science and Technology, South Kalamassery, Kochi, Kerala, India ' Department of Computer Applications, Cochin University of Science and Technology, South Kalamassery, Kochi, Kerala, India

Abstract: This paper proposes a secure finger vein reconstruction and recognition system utilising a novel weight standardisation-based progressive generative adversarial networks (WS-progressive GAN) as well as 'he' initialised chimp optimisation-based convolutions neural network (he-ChOA-CNN) classifier for overcoming security issues. Initially, the input images are pre-processed, and the reflection-based contrast limited adaptive histograms equalisation (RCLAHE) enhanced the pre-processed images. Next, bias locality-sensitive hashing (BLSH) generates hash values, through which the ameliorated images are secured. Next, the secured images are augmented and applied for WS-progressive GAN, which encodes and decodes the image for reconstructing the synthetic images. Then, the he-ChOA-CNN accepts the imperative features extracted as of the synthetic images as input for training. Amid testing, the identity of the person is recognised utilising the classifier output and the query image by detecting the gaps. Analogised to the prevailing methods, more accurate outcomes are attained by the proposed model, which is illustrated through the experimental outcomes.

Keywords: reflection-based contrast limited adaptive histogram equalisation; finger vein; bias locality-sensitive hashing; BLSH; he initialisation; chimp optimisation-based CNN; generative adversarial network; progressive GAN.

DOI: 10.1504/IJBM.2024.135159

International Journal of Biometrics, 2024 Vol.16 No.1, pp.44 - 67

Received: 30 Dec 2021
Accepted: 23 Dec 2022
Published online: 01 Dec 2023 *

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