Abstract
The fingerprint presentation attack is still a major challenge in biometric systems because of its increased applications worldwide. In the past, researchers used fingerprint presentation attack detection (FPAD) for user authentication, but it suffers from reliable authentication due to less focus on reducing the ‘error rate’. In this paper, we proposed an algorithm, based on referential image quality-metrics and minutiae count using neural network, k-NN and SVM for FPAD. We evaluate and validate the error rate reduction with different machine learning models on the public domain, such as LivDet crossmatch dataset 2015 and achieved an accuracy of 88% with a neural network, 88.6% with k-NN and 88.8% using SVM. In addition, the average classification error (ACE) score is 0.1197 for ANN, 0.1138 for k-NN and 0.1117 for SVM. Thus, the results obtained show that it was achieved a reasonable accuracy with a low ACE score with respect to other state-of-the-art methods.
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References
Guennouni, S., Mansouri, A. & Ahaitouf, A. (2020). Biometric systems and their applications. In Visual impairment and blindness - what we know and what we have to know, IntechOpen.
Rai, V., Mehta, K., Jatin, J., Tiwari, D., & Chaurasia, R. (2020). Automated biometric personal identification-techniques and applications. Proceedings of the International Conference on Intelligent Computing and Control Systems, ICICCS, 2020, 1023–1030.
Galbally-Herrero, J., Fierrez-Aguilar, J., Rodriguez-Gonzalez, J. D., Alonso-Fernandez, F., Ortega-Garcia, J. & Tapiador, M. (2014). On the vulnerability of fingerprint verification systems to fake fingerprints attacks. In Proceedings - international carnahan conference on security technology, 2006 (pp. 130–136).
Chugh, T., Cao, K. & Jain, A. K. (2017). Fingerprint spoof buster. arXiv (pp. 1–13).
Verma, A., Gupta, V. K. & Goel, S. (2020). Fingerprint presentation attack detection in open-set scenario using transient liveness factor. Recent Advances in Computer Science and Communications (vol. 13).
Ghiani, L., Marcialis, G. L. & Roli, F. (2012). Fingerprint liveness detection by local phase quantization. In Proceedings - international conference on pattern recognition (pp. 537–540).
Ahmad, A. S., Hassan, R. & Othman, R. M. (2017). An investigation of fake fingerprint detection approaches. In AIP conference proceedings (vol. 1891).
Chugh, T., Cao, K. & Jain, A. K. (2018). Fingerprint spoof detection using minutiae-based local patches. In IEEE international joint conference on biometrics, IJCB 2017, 2018, vol. 2018-Janua (pp. 581–589).
Nogueira, R. F., DeAlencarLotufo, R., & CamposMacHado, R. (2016). Fingerprint liveness detection using convolutional neural networks. IEEE Transactions on Information Forensics and Security, 11(6), 1206–1213.
Rattani, A., Scheirer, W. J., & Ross, A. (2015). Open set fingerprint spoof detection across novel fabrication materials. IEEE Transactions on Information Forensics and Security, 10(11), 2447–2460.
Y. Ding and A. Ross, “An ensemble of one-class SVMs for fingerprint spoof detection across different fabrication materials,” in 8th IEEE International Workshop on Information Forensics and Security, WIFS 2016, 2017, no. December.
KrishnaPrasad, K., & Aithal, P. S. (2017). Literature review on fingerprint level 1 and level 2 features enhancement to improve quality of image. International Journal of Management, Technology, and Social Sciences, 2, 8–19.
Yuan, C., Li, X., Wu, Q. M. J., Li, J., & Sun, X. (2017). Fingerprint liveness detection from different fingerprint materials using convolutional neural network and principal component analysis. Computers, Materials and Continua, 53(4), 357–371.
Park, E., Cui, X., Kim, W., Liu, J. & Kim, H. (2018) Patch-based fake fingerprint detection using a fully convolutional neural network with a small number of parameters and an optimal threshold. arXiv (pp. 1–12).
Gajawada, R, Popli, A., Chugh, T., Namboodiri, A. & Jain, A. K. (2019) Universal material translator: Towards spoof fingerprint generalization. arXiv. (pp. 1–8)
Marasco, E., & Ross, A. (2014). “A survey on antispoofing schemes for fingerprint recognition systems. ACM Computing Surveys., 47(2), 1–36.
Tan, B. (2008). Novel methods for fingerprints image analysis detect fake fingers. SPIE Newsroom.
Gragnaniello, D., Poggi, G., Sansone, C. & Verdoliva, L. (2013). Fingerprint liveness detection based on weber local image descriptor. In 2013 IEEE workshop on biometric measurements and systems for security and medical applications, BioMS 2013 - Proceedings, 2013, no. September (pp. 46–50).
Gragnaniello, D., Poggi, G., Sansone, C., & Verdoliva, L. (2015). Local contrast phase descriptor for fingerprint liveness detection. Pattern Recognition, 48(4), 1050–1058.
Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W. & Feng, J. (2015). Biometric recognition: 10th Chinese conference, CCBR 2015 Tianjin, China, november 13–15, 2015 proceedings. In Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) (vol. 9428, pp. 241–249).
Arunalatha, G., & Ezhilarasan, M. (2015). Fingerprint spoof detection using quality features. International Journal of Security and Its Applications, 9(10), 83–94.
Xia, Z., Lv, R., Zhu, Y., Ji, P., Sun, H., & Shi, Y. Q. (2017). Fingerprint liveness detection using gradient-based texture features. Signal, Image and Video Processing, 11(2), 381–388.
Park, E., Kim, W., Li, Q., Kim, H. & Kim, J. (2016). Fingerprint liveness detection using CNN features of random sample patches: Liveness detection using CNN features. In Lecture notes in informatics (LNI), proceedings - series of the gesellschaft fur informatik (GI) (vol P-260).
Ghiani, L., Hadid, A., Marcialis, G. L., & Roli, F. (2017). Fingerprint liveness detection using local texture features. IET Biometrics, 6(3), 224–231.
Toosi, A., Cumani, S. & Bottino, A. (2017). CNN patch-based voting for fingerprint liveness detection. In IJCCI 2017 - proceedings of the 9th international joint conference on computational intelligence (pp. 158–165).
Bartunek, J. S. (2005). Minutiae extraction from fingerprint with neural network and minutiae based fingerprint verification Josef Str. om Bart ˚ ek,” Master’s Thesis MEE.
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Verma, A., Gupta, A., Akbar, M. et al. Presentation Attack Detection Using Referential Quality Metrics and Minutiae Count. Wireless Pers Commun 127, 3347–3361 (2022). https://doi.org/10.1007/s11277-022-09921-6
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DOI: https://doi.org/10.1007/s11277-022-09921-6