$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

Fingerprint Presentation Attack Detection Utilizing Spatio-Temporal Features 원문보기

Sensors, v.21 no.6, 2021년, pp.2059 -   

Husseis, Anas ,  Liu-Jimenez, Judith ,  Sanchez-Reillo, Raul

Abstract AI-Helper 아이콘AI-Helper

This paper presents a novel mechanism for fingerprint dynamic presentation attack detection. We utilize five spatio-temporal feature extractors to efficiently eliminate and mitigate different presentation attack species. The feature extractors are selected such that the fingerprint ridge/valley patt...

주제어

참고문헌 (27)

  1. 1. Future Smartphone Payments to Rely on Software Security 2018 Available online: https://www.juniperresearch.com/press/press-releases/future-smartphone-payments-rely-software-security (accessed on 3 February 2021) 

  2. 2. Ratha N.K. Connell J.H. Bolle R.M. Enhancing security and privacy in biometrics-based authentication systems IBM Syst. J. 2001 40 614 634 10.1147/sj.403.0614 

  3. 3. Thandauthapani T.D. Reeve A.J. Long A.S. Turner I.J. Sharp J.S. Exposing latent fingermarks on problematic metal surfaces using time of flight secondary ion mass spectroscopy Sci. Justice 2018 58 405 414 10.1016/j.scijus.2018.08.004 30446069 

  4. 4. Marasco E. Ross A. A Survey on Antispoofing Schemes for Fingerprint Recognition Systems ACM Comput. Surv. 2014 47 1 36 10.1145/2617756 

  5. 5. Husseis A. Liu-Jimenez J. Goicoechea-Telleria I. Sanchez-Reillo R. Dynamic Fingerprint Statistics: Application in Presentation Attack Detection IEEE Access 2020 8 95594 95604 10.1109/ACCESS.2020.2995829 

  6. 6. Antonelli A. Cappelli R. Maio D. Maltoni D. Fake Finger Detection by Skin Distortion Analysis IEEE Trans. Inform. Forensics Secur. 2006 1 360 373 10.1109/TIFS.2006.879289 

  7. 7. Zhang Y. Tian J. Chen X. Yang X. Shi P. Fake Finger Detection Based on Thin-Plate Spline Distortion Model Advances in Biometrics Springer Berlin/Heidelberg, Germany 2007 742 749 10.1007/978-3-540-74549-5_78 

  8. 8. Jia J. Cai L. Zhang K. Chen D. A New Approach to Fake Finger Detection Based on Skin Elasticity Analysis Advances in Biometrics Springer Berlin/Heidelberg, Germany 2007 309 318 10.1007/978-3-540-74549-5_33 

  9. 9. Derakhshani R. Schuckers S.A. Hornak L.A. O’Gorman L. Determination of vitality from a non-invasive biomedical measurement for use in fingerprint scanners Pattern Recognit. 2003 36 383 396 10.1016/S0031-3203(02)00038-9 

  10. 10. Parthasaradhi S. Derakhshani R. Hornak L. Schuckers S. Time-Series Detection of Perspiration as a Liveness Test in Fingerprint Devices IEEE Trans. Syst. Man Cybernet. Part C 2005 35 335 343 10.1109/TSMCC.2005.848192 

  11. 11. Abhyankar A. Schuckers S. Integrating a wavelet based perspiration liveness check with fingerprint recognition Pattern Recognit. 2009 42 452 464 10.1016/j.patcog.2008.06.012 

  12. 12. Plesh R. Bahmani K. Jang G. Yambay D. Brownlee K. Swyka T. Johnson P. Ross A. Schuckers S. Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures Proceedings of the 2019 International Conference on Biometrics, ICB 2019 Crete, Greece 4?7 June 2019 10.1109/ICB45273.2019.8987297 

  13. 13. Busch C. Sousedik C. Presentation attack detection methods for fingerprint recognition systems: A survey IET Biom. 2014 3 219 233 10.1049/iet-bmt.2013.0020 

  14. 14. Husseis A. Liu-Jimenez J. Goicoechea-Telleria I. Sanchez-Reillo R. A survey in presentation attack and presentation attack detection Proceedings of the 2019 International Carnahan Conference on Security Technology (ICCST) Chennai, India 1?3 October 2019 10.1109/CCST.2019.8888436 

  15. 15. Nixon M.S. Handbook of Biometric Anti-Spoofing Springer Cham, Switzerland 2019 207 228 10.1007/978-3-319-92627-8 

  16. 16. Goicoechea Telleria I. Evaluation of Presentation Attack Detection under the Context of Common Criteria Ph.D. Thesis Universidad Carlos III de Madrid Madrid, Spain 2019 

  17. 17. Casula R. Orru G. Angioni D. Feng X. Marcialis G.L. Roli F. Are spoofs from latent fingerprints a real threat for the best state-of-art liveness detectors? arXiv 2020 2007.03397 

  18. 18. ISO/IEC 30107-3:2017―Information Technology―Biometric Presentation Attack Detection―Part 3: Testing and Reporting Available online: https://www.iso.org/standard/67381.html (accessed on 3 February 2021) 

  19. 19. Szummer M. Picard R.W. Temporal texture modeling Proceedings of the IEEE International Conference on Image Processing, IEEE Lausanne, Switzerland 19 September 1996 Volume 3 823 826 10.1109/icip.1996.560871 

  20. 20. Zhao G. Pietikainen M. Dynamic texture recognition using local binary patterns with an application to facial expressions IEEE Trans. Pattern Anal. Mach. Intell. 2007 29 915 928 10.1109/TPAMI.2007.1110 17431293 

  21. 21. Song B. Li K. Zong Y. Zhu J. Zheng W. Shi J. Zhao L. Recognizing spontaneous micro-expression using a three-stream convolutional neural network IEEE Access 2019 7 184537 184551 10.1109/ACCESS.2019.2960629 

  22. 22. Zhao X. Lin Y. Heikkila J. Dynamic Texture Recognition Using Volume Local Binary Count Patterns with an Application to 2D Face Spoofing Detection IEEE Trans. Multimed. 2018 20 552 566 10.1109/TMM.2017.2750415 

  23. 23. Solmaz B. Assari S.M. Shah M. Classifying web videos using a global video descriptor Mach. Vis. Appl. 2013 24 1473 1485 10.1007/s00138-012-0449-x 

  24. 24. Rahman S. See J. Spatio-temporal mid-level feature bank for action recognition in low quality video Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Shanghai, China 20?25 March 2016 Institute of Electrical and Electronics Engineers Inc. Piscataway, NJ, USA 2016 Volume 2016 May 1846 1850 10.1109/ICASSP.2016.7471996 

  25. 25. Paivarinta J. Rahtu E. Heikkila J. Volume local phase quantization for blur-insensitive dynamic texture classification Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Springer Berlin/Heidelberg, Germany 2011 Volume 6688 LNCS 360 369 10.1007/978-3-642-21227-7_34 

  26. 26. Ojansivu V. Heikkila J. Blur Insensitive Texture Classification Using Local Phase Quantization Springer Berlin/Heidelberg, Germany 2008 236 243 10.1007/978-3-540-69905-7_27 

  27. 27. Martin A. Doddington G. Kamm T. Ordowski M. Przybocki M. The DET curve in assessment of detection task performance Proceedings of the European Conference on Speech Communication and Technology Rhodes, Greece 22?25 September 1997 1895 1898 

관련 콘텐츠

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로