An Innovative Method for Detecting Double Identity Fingerprints from Non-Contiguous Minutiae-Based Attack Technique

Sufyan, Muhammad; Khalid Butt, Khushbu; Alyas, Tahir; Irshad, Omer; Iqbal, Umer

doi:10.22042/isecure.2026.550492.1257

An Innovative Method for Detecting Double Identity Fingerprints from Non-Contiguous Minutiae-Based Attack Technique

Document Type : Research Article

Authors

Muhammad Sufyan ¹

Khushbu Khalid Butt ²

Tahir Alyas ¹

Omer Irshad ³

Umer Iqbal ²

¹ Lahore Garrison University, Computer Science, Lahore, 54920, Pakistan

² Lahore Garrison University, Information Technology, Lahore, 54920, Pakistan

³ Lahore Garrison University, Software Engineering, Lahore, 54920, Pakistan

https://doi.org/10.22042/isecure.2026.550492.1257

Abstract

Advanced fingerprint spoofing attacks often threaten biometric authentication systems, such as the presence of non-contiguous minutiae to form a double-identity fingerprint. This research paper offers a deep learning system based on MobileNet to differentiate between authentic and anomalous fingerprints. The SOCOFing dataset (in the public domain) with real fingerprints and synthetically deformed samples at three levels of difficulty (easy, medium, and hard) was experimented with, which is used as a reference point in assessing the performance of spoof detection. In order to enhance the generalization, data augmentation and transfer learning were utilized, which helped in increasing the resilience of the model to different fingerprint modifications. The overall test accuracy of the MobileNet-based model was 88%, which declined with the case of medium and hard alterations, which suggests the inability to detect extremely complicated spoofing patterns. However, the model is efficient and has low computational expense, thus it can form a viable yet lightweight real-time biometric verification architecture. The future research ought to cover more sophisticated architectures, cross-dataset testing, and feature-level analysis in order to enhance the detection of difficult forms of spoofing.

Keywords

Biometric authentication

Fingerprint spoofing

Non-contiguous minutiae

MobileNet

Deep learning

SOCOFing dataset

Lightweight neural networks

Real-time verification

Synthetic fingerprint alterations

[1] A. Jain, Lin Hong, and R. Bolle. On-line fingerprint verification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4): 302–314, 1997.

[2] Davide Maltoni, Dario Maio, Anil K. Jain, and Salil Prabhakar. Handbook of Fingerprint Recognition. Springer London, 2 edition, 2009. ISBN 9781848822535. URL https://doi.org/10. 1007/978-1-84882-254-2.

[3] S. Pankanti, Salil Prabhakar, and Anil Jain. On the individuality of fingerprints. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24:1010– 1025, 09 2002.

[4] Carsten Gottschlich and Stephan F. Huckemann. Separating the real from the synthetic: Minutiae histograms as fingerprints of fingerprints. IET Biometrics, 3(4):291–301, April 2013. . URL https://doi.org/10.1049/ietbmt.2013.0004.

[5] Arun Ross, Jidnya Shah, and Anil K. Jain. From template to image: Reconstructing fingerprints from minutiae points. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (4):544–560, May 2007. URL https://doi. org/10.1109/TPAMI.2007.1004.

[6] Patrizio Campisi. Security and Privacy in Biometrics. Springer London, 2015. ISBN 9781447167215. URL https://doi.org/10. 1007/978-1-4471-6722-2.

[7] Cai Li and Jiankun Hu. Attacks via record multiplicity on cancelable biometrics templates. Concurrency and Computation: Practice and Experience, 26, 06 2014.

[8] Jun Feng and Anil K. Jain. Fingerprint reconstruction: From minutiae to phase. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(2):209–223, February 2011. . URL https://doi.org/10.1109/TPAMI.2010.59.

[9] Kai Cao and Anil K. Jain. Learning fingerprint reconstruction: From minutiae to image. IEEE Transactions on Information Forensics and Security, 10(1):104–117, January 2015. URL https: //doi.org/10.1109/TIFS.2014.2368356.

[10] V. S. Baghel and S. Prakash. Digital Image Security. CRC Press, 2024.

[11] Soweon Yoon and Anil K. Jain. Longitudinal study of fingerprint recognition. Proceedings of the National Academy of Sciences, 112(28):8555– 8560, 2015. URL https://doi.org/10.1073/ pnas.1417220112.

[12] Shi Xuanbin, Jun Feng, Jie Zhou, and Yilong Luo. Detection and rectification of distorted fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3):555– 568, March 2015. URL https://doi.org/10. 1109/TPAMI.2014.2353647.

[13] Nalini K. Ratha, Jonathan H. Connell, and Ruud M. Bolle. Enhancing security and privacy in biometrics-based authentication systems. IBM Systems Journal, 40(3):614–634, January 2001. URL https://doi.org/10.1147/sj. 403.0614.

[14] Emanuele Marasco and Arun Ross. A survey on antispoofing schemes for fingerprint recognition systems. ACM Computing Surveys, 47(2):1–36, November 2014. URL https://doi.org/10. 1145/2656334.

[15] Konstantinos Karampidis, Minas Rousouliotis, Euangelos Linardos, and Ergina Kavallieratou. A comprehensive survey of fingerprint presentation attack detection. Journal of Surveillance, Security and Safety, 2:117–161, 2021.

[16] Anuj Agarwal et al. Deep learning based fingerprint presentation attack detection: A comprehensive survey. arXiv preprint arXiv:2305.17522, 2023.

[17] Abdulaziz A. Alshdadi et al. Enhancing fingerprint liveness detection accuracy using deep learning: A comprehensive study and novel approach. Journal of Imaging, 9(8):158, 2023.

[18] Hailin Li and Raghavendra Ramachandra. A survey on deep learning techniques for fingerprint presentation attack detection. Sensors, 26(4), 2026. ISSN 1424-8220. URL https://www. mdpi.com/1424-8220/26/4/1283.

[19] Sai Venkatesh, Raghavendra Ramachandra, Karthik N. Raja, and Christoph Busch. Face morphing attack generation & detection: A comprehensive survey. IEEE Transactions on Technology and Society, 2(3), September 2021. URL https://doi.org/10.1109/TTS.2021. 3110099.

[20] Michele Ferrara, Riccardo Cappelli, and Davide Maltoni. Detecting double-identity fingerprint attacks. IEEE Transactions on Biometrics, Behavior, and Identity Science, 5(4):476– 485, 2023. URL https://doi.org/10.1109/ TBIOM.2023.3258442.

[21] Kai Cao and Anil K. Jain. Automated latent fingerprint recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (4):788–800, April 2019. URL https://doi. org/10.1109/TPAMI.2018.2855026.

[22] Haoxiang Zhang, Raghavendra Ramachandra, Karthik N. Raja, and Christoph Busch. Generalized single-image-based morphing attack detection using deep representations from vision transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 1510–1518. IEEE, 2024.

[23] A. H. Aloweiwi. Fingerprint classification using transfer learning technique. Master’s thesis, Montclair State University, New Jersey, 2021. Available at DIGITAL COMMONS.

[24] F. M. Jasem, I. T. Ahmed, and B. T. Hammad. A comprehensive method for fingerprint classification based on gabor filters and machine learning. International Information and Engineering Technology Association, 4(6):1775–1782, December 2024.

[25] A. Spanier, D. Steiner, N. Sahalo, and Y. Abecassis. Enhancing fingerprint forensics: A comprehensive study of gender classification based on advanced data-centric ai approaches and multi-database analysis. Applied Sciences, 14(1): 417, 2024. URL https://doi.org/10.3390/ app14010417.

[26] Debanjan Roy, Abhishek Bhowmick, Faria Ahmed, Md Jahid Hossain, and Md Mahfuzur Rahaman. Dual-model synergy for fingerprint spoof detection using vgg16 and resnet50. Journal of Imaging, 11(2):42, 2025.

[27] Ricardo Rodrigues, Jason Jermyn, and Stephanie Schuckers. Dyffpad: Dynamic fusion of convolutional and handcrafted features for fingerprint presentation attack detection. IEEE Transactions on Information Forensics and Security, 18: 3505–3518, 2023.

[28] Yifan Xu, Shiqi Liu, Yan Fang, and Zheng Wang. Gru-aunet: A domain adaptation framework for contactless fingerprint presentation attack detection. arXiv preprint arXiv:2504.01213, 2025.

[29] A.Kumar,D.Dembla,S.Tinker,andS.B.Khan. Handbook of Deep Learning Models for Healthcare Data Processing-Disease Prediction, Analysis, and Applications. CRC Press, 2025.

[30] Metin Akay, Yong Du, Cheryl Sershen, Minghua Wu, Ting Chen, Shervin Assassi, Chandra Mohan, and Yasemin Akay. Deep learning classification of systemic sclerosis skin using the mobilenetv2 model. IEEE Open Journal of Engineering in Medicine and Biology, PP:1–1, 03 2021.

[31] S. Belaqziz, S. E. Hajjami, H. Amellal, R. Lahmyed, L. Koutti, B. Abdellah, and I. U. Khan. Smart Applications of Artificial Intelligence and Big Data. CRC Press, 2025.

[32] H. L. Gururaj, F. Flammini, V. R. Kumar, and N. S. Prema. Recent Trends in Healthcare Innovation. CRC Press, 2025.

[33] Z. Li, B. Gong, and T. Yang. Improved dropout for shallow and deep learning. arXiv preprint arXiv:1607.01799, 2016. URL https://arxiv. org/abs/1607.01799.

[34] A. M. Javid, S. Das, M. Skoglund, and S. Chatterjee. A relu dense layer to improve the performance of neural networks. In ICASSP 2021 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, June 2021.

[35] Z. Zhang. Improved adam optimizer for deep neural networks. In 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS). IEEE, June 2018.

[36] A. Arias-Duart, E. Mariotti, D. Garcia-Gasulla, and J. M. Alonso-Moral. A confusion matrix for evaluating feature attribution methods. In Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 2023. URL https: //www.researchgate.net/publication/373134015_A_Confusion_Matrix_for_Evaluating_Feature_Attribution_Methods.

[37] P. Nandal, M. Dahiya, M. Singh, A. Dagur, and B. Kumar. Progressive Computational Intelligence, Information Technology and Networking. CRC Press, 2025.

[38] H. Dong. Data Analytics in Finance. CRC Press, 2025.

[39] Andrew Howard et al. Searching for mobilenetv3. Proceedings of ICCV, 2019.

[40] Ming Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. ICML, 2019.

[41] Pavan Kumar Vasu et al. Mobileone: An improved one millisecond mobile backbone. CVPR, 2023.