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Automated Teller Machine Authentication Using Biometric

Shumukh M. Aljuaid*, Arshiya S. Ansari

Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al Majma’ah 11952, Saudi Arabia

* Corresponding Author: Shumukh M. Aljuaid. Email: email

Computer Systems Science and Engineering 2022, 41(3), 1009-1025. https://doi.org/10.32604/csse.2022.020785

Abstract

This paper presents a novel method of a secured card-less Automated Teller Machine (ATM) authentication based on the three bio-metrics measures. It would help in the identification and authorization of individuals and would provide robust security enhancement. Moreover, it would assist in providing identification in ways that cannot be impersonated. To the best of our knowledge, this method of Biometric_ fusion way is the first ATM security algorithm that utilizes a fusion of three biometric features of an individual such as Fingerprint, Face, and Retina simultaneously for recognition and authentication. These biometric images have been collected as input data for each module in this system, like a fingerprint, a face, and a retina module. A database is created by converting these images to YIQ color space, which is helpful in normalizing the brightness levels of the image hence mainly (Y component’s) luminance. Then, it attempt to enhance Cellular Automata Segmentation has been carried out to segment the particular regions of interest from these database images. After obtaining segmentation results, the featured extraction method is carried out from these critical segments of biometric photos. The Enhanced Discrete Wavelet Transform technique (DWT Mexican Hat Wavelet) was used to extract the features. Fusion of extracted features of all three biometrics features have been used to bring in the multimodal classification approach to get fusion vectors. Once fusion vectors ware formulated, the feature level fusion technique is incorporated based on the extracted feature vectors. These features have been applied to the machine learning algorithm to identify and authorization of multimodal biometrics for ATM security. In the proposed approach, we attempt at useing an enhanced Deep Convolutional Neural Network (DCNN). A hybrid optimization algorithm has been selected based on the effectiveness of the features. The proposed approach results were compared with existing algorithms based on the classification accuracy to prove the effectiveness of our algorithm. Moreover, comparative results of the proposed method stand as a proof of more promising outcomes by combining the three biometric features.

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Cite This Article

APA Style
Aljuaid, S.M., Ansari, A.S. (2022). Automated teller machine authentication using biometric. Computer Systems Science and Engineering, 41(3), 1009-1025. https://doi.org/10.32604/csse.2022.020785
Vancouver Style
Aljuaid SM, Ansari AS. Automated teller machine authentication using biometric. Comput Syst Sci Eng. 2022;41(3):1009-1025 https://doi.org/10.32604/csse.2022.020785
IEEE Style
S.M. Aljuaid and A.S. Ansari, “Automated Teller Machine Authentication Using Biometric,” Comput. Syst. Sci. Eng., vol. 41, no. 3, pp. 1009-1025, 2022. https://doi.org/10.32604/csse.2022.020785



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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