Title: Improved deep learning-based multiple biometric authentication system for ATM transaction solving cost and ambiguity problems of multiple card usage

Authors: M. Ravi Prasad; N. Thillaiarasu

Addresses: School of Computing and Information Technology, REVA University, Bengaluru, Karnataka, India ' School of Computing and Information Technology, REVA University, Bengaluru, Karnataka, India

Abstract: When considering the ATM machine, the multimodal authentication is performed. Here, the authentication using biometric information is achieved by applying the multi-channel adaptive EfficientNetV2 with attention mechanism (MAEv2-AM) each channel carries information of each image from retina, fingerprint, face and spectrogram), where the hyper-parameters is tuned by using the improved fitness condition-based Archimedes optimisation algorithm (FCAOA). After the initial authentication, a single PIN entry would be required to select the desired bank account, streamlining the process of withdrawing money. This approach enhances convenience while maintaining security. Therefore, wastage of cards can be reduced, cost of card manufacturing can be reduced, and the ambiguity of using multiple passwords in multiple cards can also be reduced. Finally, the performance of the method is analysed using diverse metrics and compared with other baseline models. Hence, the proposed work outperforms with the higher results to do the biometric-based authentication for ATM transaction.

Keywords: multiple biometric authentication system; multiple card usage; fitness condition; Archimedes optimisation algorithm; short time Fourier transform; STFT; single card; multiple bank.

DOI: 10.1504/IJICS.2025.146616

International Journal of Information and Computer Security, 2025 Vol.27 No.1, pp.1 - 35

Received: 30 Sep 2023
Accepted: 30 Jun 2024
Published online: 06 Jun 2025 *

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