Implementation of Fingerprint Biometrics for Secure Contactless Banking Card Transactions

Authors

DOI:

https://doi.org/10.14500/aro.12311

Keywords:

Biometric authentication, Capacitive fingerprint sensors, Contactless payment, Fingerprint recognition, SURF features

Abstract

A contactless card is an easy and straightforward way to make a purchase that takes only a few seconds without requiring a Personal Identification Number (PIN) or signature. However, not requiring a PIN or signature on a contactless bank card makes it vulnerable to fraud attacks when losing or stealing the card. This article delivers a new model which is developed for securing a bank contactless card with fingerprint authentication. The suggested model is the creation of a new fingerprinting algorithm that combines with the virtual contactless card. The fingerprint recognition algorithm employs image processing methods to enhance and extract features in order to compare fingerprint impression images. The performance of the proposed model is evaluated based on two metrics: false acceptance rate and false rejection rate. There are five scenarios to test and evaluate the proposed model. The findings establish that the developed system enhances the process of embedding biometrics (fingerprints) to non-contact smart card and a user-friendly experience.

Downloads

Download data is not yet available.

Author Biographies

Soleen J. Ibrahim, Department of Computer Science, College of Science, University of Duhok, Duhok 42001, Kurdistan Region – F.R. Iraq

Soleen Ja. Ibrahim is an Assistant Lecturer at College of Nursing, University of Duhok. She got the B.Sc. Degree in Computer Science (University of Duhok, Iraq), the M.Sc. degree in Information Security (University of Duhok, Iraq). Her research interests are in cybersecurity and data protection.

Shaheen A. Abdulkareem, Department of Computer Science, College of Science, University of Duhok, Duhok 42001, Kurdistan Region – Iraq

Shaheen A. Abdulkareem is a Senior Lecturer at the Department of Computer Science, College of Science, University of Duhok. She got the B.Sc. degree in Computer Science (University of Duhok, Iraq), the M.Sc. degree in Geoinformatics (University of Twente, The Netherlands) and the Ph.D. degree in Artificial Intelligence (University of Twente, The Netherlands). Her research interests are in agent-based modeling, artificial intelligence and complex decision making. Dr. Shaheen is a member of IEEE Computer Society.

Ahmad B. Al-Khalil, Department of Computer Science, College of Science, University of Duhok, Duhok 42001, Kurdistan Region – F.R. Iraq

Ahmad B. Al-Khalil is an Associate Professor at the Department of Computer Science, College of Science, University of Duhok. He got the B.Sc. degree in Computer Science (University of Mosul, Iraq), the M.Sc. degree in Innovative Computing (University of Buckingham, UK) and the Ph.D. degree in Computer Communications (University of Northampton, UK). His research interests are in computer communications, computer security, and signal processing. Dr. Al-Khalil is a professional member of British Computer Society BCS, Association for Computing Machinery ACM. A member of Institute of Electrical and Electronics Engineers IEEE, Institution of Engineering and Technology IET, and Iraqi Academics Syndicate.

References

Akintunde, O.A., Adetunji, A.B., Fenwa, O.D., Oguntoye, J.P., Olayiwola, D.S., and Adeleke, A.J., 2025. Comparative analysis of score level fusion techniques in multi-biometric system. Journal of Engineering and Technology, 19(1), pp.128-141. DOI: https://doi.org/10.36108/laujet/5202.91.0121

Al-Maliki, O., and Al-Assam, H., 2021. Challenge-response mutual authentication protocol for EMV contactless cards. Computers and Security, 103, p.102186. DOI: https://doi.org/10.1016/j.cose.2021.102186

Al-Maliki, O., and Al-Assam, H., 2022. A tokenization technique for improving the security of EMV contactless cards. Information Security Journal a Global Perspective, 31(5), pp.511-526. DOI: https://doi.org/10.1080/19393555.2021.2001120

Alshehri, H., Hussain, M., Aboalsamh, H.A., and Al Zuair, M.A., 2018. Crosssensor fingerprint matching method based on orientation, gradient, and gabor-hog descriptors with score level fusion. IEEE Access, 6, pp.28951-28968. DOI: https://doi.org/10.1109/ACCESS.2018.2840330

Andress, J., (2011). Identification and authentication. In: Andress, J., Eds. The Basics of Information Security. Syngress, London, pp. 17-31. DOI: https://doi.org/10.1016/B978-1-59749-653-7.00002-5

Ayeswarya, S., and Singh, K.J., 2024. A comprehensive review on secure biometric-based continuous authentication and user profiling. IEEE Access, 12, pp.82996-83021. DOI: https://doi.org/10.1109/ACCESS.2024.3411783

Bae, G., Lee, H., Son, S., Hwang, D., and Kim, J., 2018. Secure and Robust user Authentication using Partial Fingerprint Matching. In: 2018 IEEE International Conference on Consumer Electronics (ICCE). IEEE, pp. 1-6. DOI: https://doi.org/10.1109/ICCE.2018.8326078

Bakheet, S., Alsubai, S., Alqahtani, A., and Binbusayyis, A., 2022. Robust fingerprint minutiae extraction and matching based on improved SIFT features. Applied Sciences, 12(12), p.6122. DOI: https://doi.org/10.3390/app12126122

Bojjagani, S., Seelam, N.R., Sharma, N.K., Uyyala, R., Akuri, S.R.C.M., and Maurya, A.K., 2023. The use of IoT-based wearable devices to ensure secure lightweight payments in FinTech applications. Journal of King Saud UniversityComputer and Information Sciences, 35(9), p.101785. DOI: https://doi.org/10.1016/j.jksuci.2023.101785

Castillo-Rosado, K., and Hernández-Palancar, J., 2019. Latent fingerprint matching using distinctive ridge points. Informatica, 30(3), pp.431-454. DOI: https://doi.org/10.15388/Informatica.2019.213

Chen, C.L., Aymanns, F., Minegishi, R., Matsuda, V.D., Talabot, N., Günel, S., Dickson, B.J., and Ramdya, P., 2023. Ascending neurons convey behavioral state to integrative sensory and action selection brain regions. Nature Neuroscience, 26(4), pp.682-695. DOI: https://doi.org/10.1038/s41593-023-01281-z

Daas, S., Yahi, A., Bakir, T., Sedhane, M., Boughazi, M., and Bourennane, E.B., 2020. Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion. IET Image Processing, 14(15), pp.3859-3868. DOI: https://doi.org/10.1049/iet-ipr.2020.0491

Ding, Y., and Nan, X., 2023. On edge detection algorithms for water-repellent images of insulators taking into account efficient approaches. Symmetry, 15(7), p.1418. DOI: https://doi.org/10.3390/sym15071418

Dong, X., Cho, S., Kim, Y., Kim, S., and Teoh, A.B.J., 2022. Deep rank hashing network for cancellable face identification. Pattern Recognition, 131, p.108886. DOI: https://doi.org/10.1016/j.patcog.2022.108886

Estrela, P.M.A.B., Albuquerque, R.D.O., Amaral, D.M., Giozza, W.F., and Júnior, R.T.D.S., 2021. A framework for continuous authentication based on touch dynamics biometrics for mobile banking applications. Sensors (Basel), 21(12), p.4212. DOI: https://doi.org/10.3390/s21124212

Galbally, J., Beslay, L., and Böstrom, G., 2020. 3D-FLARE: A touchless full3D fingerprint recognition system based on laser sensing. IEEE Access, 8, pp.145513-145534. DOI: https://doi.org/10.1109/ACCESS.2020.3014796

Gupta, R., Khari, M., Gupta, D., and Crespo, R.G., 2020. Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction. Information Sciences, 530, pp.201-218. DOI: https://doi.org/10.1016/j.ins.2020.01.031

Hendre, M., Patil, S., and Abhyankar, A., 2022. Biometric recognition robust to partial and poor quality fingerprints using distinctive region adaptive SIFT keypoint fusion. Multimedia Tools and Applications, 81(12), pp.17483-17507. DOI: https://doi.org/10.1007/s11042-021-11686-2

Huang, S., Sun, G., and Li, M., 2021. FAST and FLANN for Feature Matching Based on SURF. In: 2021 33rd Chinese Control and Decision Conference (CCDC), IEEE, pp. 1584-1589. DOI: https://doi.org/10.1109/CCDC52312.2021.9601366

Ibrahim, S.J., and Al-Khalil, A.B., 2023. Fingerprints to authenticate transactions in contactless cards. Science Journal of University of Zakho, 11(4), pp.481-491. DOI: https://doi.org/10.25271/sjuoz.2023.11.4.1165

Keerthana, N.V., and Devi, M.P., 2024. A Comprehensive Analysis of Minutiae Point Extraction in Biometric FingerPrint. In: 2024 13th International Conference on System Modeling and Advancement in Research Trends (SMART), IEEE, pp. 319-322. DOI: https://doi.org/10.1109/SMART63812.2024.10882188

Lee, W., Cho, S., Choi, H., and Kim, J., 2017. Partial fingerprint matching using minutiae and ridge shape features for small fingerprint scanners. Expert Systems with Applications, 87, pp.183-198. DOI: https://doi.org/10.1016/j.eswa.2017.06.019

Liao, C.C., and Chiu, C.T., 2016. Fingerprint Recognition with Ridge Features and Minutiae on Distortion. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 2109-2113. DOI: https://doi.org/10.1109/ICASSP.2016.7472049

Magdum, A., Sivaraman, E., and Honnavalli, P.B., 2021. Contactless transaction using wearable ring with biometric fingerprint security feature. In: Computer Networks and Inventive Communication Technologies: Proceedings of Third ICCNCT 2020. Springer Nature, Singapore, pp. 653-666. DOI: https://doi.org/10.1007/978-981-15-9647-6_51

Mathur, S., Vjay, A., Shah, J., Das, S., and Malla, A., 2016. Methodology for Partial Fingerprint Enrollment and Authentication on Mobile Devices. In: 2016 International Conference on Biometrics (ICB). IEEE, pp. 1-8. DOI: https://doi.org/10.1109/ICB.2016.7550093

Mogaji, E., and Nguyen, N.P., 2024. Evaluating the emergence of contactless digital payment technology for transportation. Technological Forecasting and Social Change, 203, p.123378. DOI: https://doi.org/10.1016/j.techfore.2024.123378

Mohamed Abdul Cader, A.J., Banks, J., and Chandran, V., (2023). Fingerprint Systems: Sensors, image acquisition, interoperability and challenges. Sensors (Basel), 23(14), p.6591. DOI: https://doi.org/10.3390/s23146591

Nedjah, N., Wyant, R.S., Mourelle, L.M., and Gupta, B.B., (2017). Efficient yet robust biometric iris matching on smart cards for data high security and privacy. Future Generation Computer Systems, 76, pp.18-32. DOI: https://doi.org/10.1016/j.future.2017.05.008

Nilsson, H., 2021. Trust issues? The need to secure contactless biometric payment cards. Biometric Technology Today, 2021(1), pp.5-8. DOI: https://doi.org/10.1016/S0969-4765(21)00009-6

Qi, Y., Yang, Z., Sun, W., Lou, M., Lian, J., Zhao, W., Deng, X., and Ma, Y., 2022. A comprehensive overview of image enhancement techniques. Archives of Computational Methods in Engineering, 29(1), pp.583-607. DOI: https://doi.org/10.1007/s11831-021-09587-6

Routray, S., Ray, A.K., and Mishra, C., 2017. Analysis of Various Image Feature Extraction Methods Against Noisy Image: SIFT, SURF and HOG. In: 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, pp. 1-5. DOI: https://doi.org/10.1109/ICECCT.2017.8117846

Shepherd, C., and Markantonakis, K., 2024. Isolated hardware execution platforms. In: Trusted Execution Environments. Cham: Springer International Publishing, pp.55-78. DOI: https://doi.org/10.1007/978-3-031-55561-9_4

Suwarno, S., and Santosa, P.I., 2019. Simple verification of low-resolution f ingerprint using non-minutiae feature. Journal of Physics Conference Series, 1196(1), p. 012062. DOI: https://doi.org/10.1088/1742-6596/1196/1/012062

Wang, J., Zhou, Y., and Yang, Y., 2020. A novel and fast three-dimensional measurement technology for the objects surface with non-uniform reflection. Results in Physics, 16, p.102878. DOI: https://doi.org/10.1016/j.rinp.2019.102878

Wani, M.A., Bhat, F.A., Afzal, S., and Khan, A.I., 2019. Supervised deep learning in fingerprint recognition. In: Advances in Deep Learning. Springer Singapore, Singapore, pp. 111-132. DOI: https://doi.org/10.1007/978-981-13-6794-6_7

Yu, J., Niu, L., Gao, C., Cao, Z., and Zhao, H., 2024. Partial Fingerprint Matching Via Feature Similarity and Pre-Training. In: 2024 IEEE International Joint Conference on Biometrics (IJCB). IEEE, pp. 1-9. DOI: https://doi.org/10.1109/IJCB62174.2024.10744474

Zhang, F., Xin, S., and Feng, J., 2019. Combining global and minutia deep features for partial high-resolution fingerprint matching. Pattern Recognition Letters, 119, pp.139-147. DOI: https://doi.org/10.1016/j.patrec.2017.09.014

Zhang, H., and Yang, Z., 2023. Biometric authentication and correlation analysis based on CNN‐SRU hybrid neural network model. Computational Intelligence and Neuroscience, 2023(1), p.8389193. DOI: https://doi.org/10.1155/2023/8389193

Downloads

Published

2026-01-29

How to Cite

Ibrahim, S. J., Abdulkareem, S. A. and Al-Khalil, A. B. (2026) “Implementation of Fingerprint Biometrics for Secure Contactless Banking Card Transactions”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 14(1), pp. 31–42. doi: 10.14500/aro.12311.

Issue

Vol. 14 No. 1 (2026): Edition Twenty Six

Section

Articles

License

Copyright (c) 2026 Soleen J. Ibrahim, Shaheen A. Abdulkareem, Ahmad B. Al-Khalil

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Authors who choose to publish their work with Aro agree to the following terms:

  • Authors retain the copyright to their work and grant the journal the right of first publication. The work is simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-SA 4.0]. This license allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  • Authors have the freedom to enter into separate agreements for the non-exclusive distribution of the journal's published version of the work. This includes options such as posting it to an institutional repository or publishing it in a book, as long as proper acknowledgement is given to its initial publication in this journal.

  • Authors are encouraged to share and post their work online, including in institutional repositories or on their personal websites, both prior to and during the submission process. This practice can lead to productive exchanges and increase the visibility and citation of the published work.

By agreeing to these terms, authors acknowledge the importance of open access and the benefits it brings to the scholarly community.

Crossref
Scopus
Google Scholar
Europe PMC
Received 2025-05-30
Accepted 2025-12-06
Published 2026-01-29

Similar Articles

1 2 3 4 5 6 > >> 

You may also start an advanced similarity search for this article.