The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
Source abstract
Abstract
Fingerprint recognition systems have been widely deployed in authentication and verification applications, ranging from personal smartphones to border control systems. Recently, the biometric society has raised concerns about presentation attacks that aim to manipulate the biometric system’s final decision by presenting artificial fingerprint traits to the sensor. In this paper, we propose a presentation attack detection scheme that exploits the natural fingerprint phenomena, and analyzes the dynamic variation of a fingerprint’s impression when the user applies additional pressure during the presentation. For that purpose, we collected a novel dynamic dataset with an instructed acquisition scenario. Two sensing technologies are used in the data collection, thermal and optical. Additionally, we collected attack presentations using seven presentation attack instrument species considering the same acquisition circumstances. The proposed mechanism is evaluated following the directives of the standard ISO/IEC 30107. The comparison between ordinary and pressure presentations shows higher accuracy and generalizability for the latter. The proposed approach demonstrates efficient capability of detecting presentation attacks with low bona fide presentation classification error rate (BPCER) where BPCER is 0% for an optical sensor and 1.66% for a thermal sensor at 5% attack presentation classification error rate (APCER) for both.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Biometric Identification and Security · User Authentication and Security Systems · Face recognition and analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
University Group for ID Technologies (GUTI), University Carlos III of Madrid (UC3M), Av. de la Universidad 30, Leganés, 28911 Madrid, Spain
Funding
Horizon 2020
References (40)
Show source-provided references
1. Nelson, J. (2013). Biometrics Characteristics. Effective Physical Security, Elsevier Inc.10.1016/B978-0-12-415892-4.00012-2 ↗
2. Goicoechea-Telleria, I., Garcia-Peral, A., Husseis, A., and Sanchez-Reillo, R. (2018, January 22–25). Presentation Attack Detection Evaluation on Mobile Devices: Simplest Approach for Capturing and Lifting a Latent Fingerprint. Proceedings of the 2018 International Carnahan Conference on Security Technology (ICCST), Montreal, QC, Canada.10.1109/CCST.2018.8585605 ↗
3. Casula, R., Orrù, G., Angioni, D., Feng, X., Marcialis, G.L., and Roli, F. (2020). Are spoofs from latent fingerprints a real threat for the best state-of-art liveness detectors?. arXiv.
4. Blanco Gonzalo, R., Corsetti, B., Goicoechea-Telleria, I., Husseis, A., Liu-Jimenez, J., Sanchez-Reillo, R., Eglitis, T., Ellavarason, E., Guest, R., and Lunerti, C. (2018, January 22–25). Attacking a Smartphone Biometric Fingerprint System: A Novice’s Approach. Proceedings of the 2018 International Carnahan Conference on Security Technology (ICCST), Montreal, QC, Canada.10.1109/CCST.2018.8585726 ↗
5. (2016). ISO/IEC 30107-1:2016—Information Technology—Biometric Presentation Attack Detection—Part 1: Framework, ISO.
6. Nixon, M.S. (2019). Handbook of Biometric Anti-Spoofing, Springer.
7. A Survey on Antispoofing Schemes for Fingerprint Recognition Systems10.1145/2617756 ↗
8. Husseis, A., Liu-Jimenez, J., Goicoechea-Telleria, I., and Sanchez-Reillo, R. (2019, January 1–3). A survey in presentation attack and presentation attack detection. Proceedings of the International Carnahan Conference on Security Technology, Chennai, India.10.1109/CCST.2019.8888436 ↗
9. Goicoechea Telleria, I. (2019). Evaluation of Presentation Attack Detection under the Context of Common Criteria. [Ph.D. Thesis, Universidad Carlos III de Madrid].
10. Hara, M. (2007). Thoughts on Fingerprint Image Quality and Its Evaluation Thoughts on Fingerprint Image Quality and Its Evaluation Fingerprint Image Quality and Its Evaluation Fingerprint Image Quality and Its Evaluation, NIST. NIST Biometric Quality Workshop II.
11. Hicklin, R.A. (2017). Improving the Rigor of the Latent Print Examination Process. [Ph.D. Thesis, University of Lausanne].
12. Spoofing protection for fingerprint scanner by fusing ridge signal and valley noise10.1016/j.patcog.2010.01.023 ↗
13. Chang, S., Larin, K., Mao, Y., Flueraru, C., and Almuhtadi, W. (2011). Fingerprint Spoof Detection Using Near Infrared Optical Analysis, Books on Demand.10.5772/19453 ↗
14. Presentation attack detection methods for fingerprint recognition systems: A survey10.1049/iet-bmt.2013.0020 ↗
15. Tolosana, R., Gomez-Barrero, M., Kolberg, J., Morales, A., Busch, C., and Ortega-Garcia, J. (2018, January 26–28). Towards Fingerprint Presentation Attack Detection Based on Convolutional Neural Networks and Short Wave Infrared Imaging. Proceedings of the 2018 International Conference of the Biometrics Special Interest Group, BIOSIG, Darmstadt, Germany.10.23919/BIOSIG.2018.8553413 ↗
16. Low-Cost and Efficient Hardware Solution for Presentation Attack Detection in Fingerprint Biometrics Using Special Lighting Microscopes10.1109/ACCESS.2018.2888905 ↗
17. Pałka, N., and Kowalski, M. (2020). Towards Fingerprint Spoofing Detection in the Terahertz Range. Sensors, 20.10.3390/s20123379 ↗
18. Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition10.1109/TIP.2013.2292332 ↗
19. Mendoza, M., and Velastín, S. (2018). Fingerprint Presentation Attack Detection Method Based on a Bag-of-Words Approach. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Springer International Publishing.
20. Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks10.1109/ACCESS.2020.3048756 ↗
21. Open set fingerprint spoof detection across novel fabrication materials10.1109/TIFS.2015.2464772 ↗
22. Dynamic Fingerprint Statistics: Application in Presentation Attack Detection10.1109/ACCESS.2020.2995829 ↗
23. Husseis, A., Liu-Jimenez, J., and Sanchez-Reillo, R. (2021). Fingerprint Presentation Attack Detection Utilizing Spatio-Temporal Features. Sensors, 21.10.3390/s21062059 ↗
24. Fingerprint Liveness Detection Using Convolutional Neural Networks10.1109/TIFS.2016.2520880 ↗
25. Fingerprint Spoof Buster: Use of Minutiae-Centered Patches10.1109/TIFS.2018.2812193 ↗
26. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv.
27. Chugh, T., and Jain, A.K. (2019, January 4–7). Fingerprint Presentation Attack Detection: Generalization and Efficiency. Proceedings of the 2019 International Conference on Biometrics, ICB 2019, Crete, Greece.10.1109/ICB45273.2019.8987374 ↗
28. Anti-spoofing method for fingerprint recognition using patch based deep learning machine
29. A comparative study on handcrafted features v/s deep features for open-set fingerprint liveness detection10.1016/j.patrec.2021.03.032 ↗
30. Touch DNA: The effect of the deposition pressure on the quality of latent fingermarks and STR profiles10.1016/j.fsigen.2018.10.016 ↗
31. Efficient Rectification of Distorted Fingerprints10.1109/TIFS.2017.2745685 ↗
32. Transparent and flexible fingerprint sensor array with multiplexed detection of tactile pressure and skin temperature10.1038/s41467-018-04906-1 ↗
33. Cappelli, R., Maio, D., and Maltoni, D. (2001). Modelling Plastic Distortion in Fingerprint Images, Springer.10.1007/3-540-44732-6_38 ↗
34. Fake Finger Detection by Skin Distortion Analysis10.1109/TIFS.2006.879289 ↗
35. Zhang, Y., Tian, J., Chen, X., Yang, X., and Shi, P. (2007). Fake Finger Detection Based on Thin-Plate Spline Distortion Model. Advances in Biometrics, Springer.10.1007/978-3-540-74549-5_78 ↗
36. Jia, J., Cai, L., Zhang, K., and Chen, D. (2007). A New Approach to Fake Finger Detection Based on Skin Elasticity Analysis. Advances in Biometrics, Springer.10.1007/978-3-540-74549-5_33 ↗
37. Classifying web videos using a global video descriptor10.1007/s00138-012-0449-x ↗
38. Dynamic texture recognition using local binary patterns with an application to facial expressions10.1109/TPAMI.2007.1110 ↗
39. Volume local phase quantization for blur-insensitive dynamic texture classification
40. (2017). ISO/IEC 30107-3:2017—Information Technology—Biometric Presentation Attack Detection—Part 3: Testing and Reporting, ISO.