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Andreas Uhl

Venen Biometrie

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Zusammenfassung

Venen Biometrie erregt Aufmerksamkeit durch zunehmende Verwendung im Finanzsektor bei biometrischen Geldautomaten und im Homebanking – Grund genug diesen neueren biometrischen Methoden etwas genauer auf den Zahn zu fühlen.

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Literatur

  1. Sue Black. All that remains: A life in death. Doubleday, 2018.

  2. Andreas Uhl, Christoph Busch, Sebastien Marcel, Raymond Veldhuis (Eds.). Handbook of Vascular Biometrics, Springer International Publishing, 2020.

  3. Andreas Uhl. State-of-the-Art in Vascular Biometrics. In [2], pp. 3-61, 2020.

  4. Kashif Shaheed, Hangang Liu, Gongping Yang, Imran Qureshi, Jie Gou, and Yilong Yin. A systematic review of finger vein recognition techniques. Information, 9:213, 2018.

    Article  Google Scholar 

  5. Hao Luo, Fa-Xin Yu, Jeng-Shyang Pan, Shu-Chuan Chu, and Pei-Wei Tsai. A survey of vein recognition techniques. Information Technology Journal, 9(6):1142–1149, 2010.

    Article  Google Scholar 

  6. Chuck Wilson. Vein Pattern Recognition: A Privacy Enhancing Biometric. CRC Press, Boca Raton, FL, US, 2010.

    Book  Google Scholar 

  7. Jarina B. Mazumdar and S. R. Nirmala. Retina based biometric authentication system: A review. International Journal of Advanced Research in Computer Science, 9(1), 2018.

  8. Abhijit Das, Umapada Pal, Michael Blumenstein, and Miguel Angel Ferrer Ballester. Sclera recognition – a survey. In Second IAPR Asian Conference on Pattern Recognition (ACPR’13), pages 917–921, 2013.

  9. Christof Kauba and Andreas Uhl. Shedding light on the veins – reflected light or transillumination in hand-vein recognition. In Proceedings of the 11th IAPR/IEEE International Conference on Biometrics (ICB’18), pages 1–8, Gold Coast, Queensland, Australia, 2018.

  10. Christof Kauba, Bernhard Prommegger, and Andreas Uhl. The two sides of the finger – an evaluation on the recognition performance of dorsal vs. palmar finger-veins. In Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG’18), Darmstadt, Germany, 2018.

  11. R. Raghavendra and C. Busch. Exploring dorsal finger vein pattern for robust person recognition. In 2015 International Conference on Biometrics (ICB), pages 341–348, May 2015.

  12. Bernhard Prommegger, Christof Kauba, and Andreas Uhl. Multi-perspective finger-vein biometrics. In Proceedings of the IEEE 9th International Conference on Biometrics: Theory, Applications, and Systems (BTAS2018), pages 1–9, Los Angeles, California, USA, 2018.

  13. Bernhard Prommegger, Christof Kauba, and Andreas Uhl. Different Views on the Finger -Score Level Fusion in Multi-Perspective Finger Vein Recognition. In [2], 44 pages, 2020.

  14. H. Proenca and L.A. Alexandre. UBIRIS: a noisy iris image database. In F. Roli and S. Vitulano, editors, Image Analysis and Processing – ICIAP 2005, volume 3617 of Lecture Notes in Computer Science, pages 970–977, Cagliari, Italy, September 2005. Springer, 2005.

  15. Y. Matsuda, N. Miura, Y. Nonomura, A. Nagasaka, and T. Miyatake. Walkthrough-style multi-finger vein authentication. In Proceedings of the IEEE International Conference on Consumer Electronics (ICCE’17), pages 438–441, 2017

  16. Lin Zhang, Lida Li, Anqi Yang, Ying Shen, Meng Yang. Towards contactless palmprint recognition: A novel device, a new benchmark, and a collaborative representation based identification approach. Pattern Recognition 69: 199-212, 2017.

    Article  Google Scholar 

  17. Pedro Tome and Sébastien Marcel. On the vulnerability of palm vein recognition to spoofing attacks. In The 8th IAPR International Conference on Biometrics (ICB), May 2015.

  18. Pedro Tome, Matthias Vanoni, and Sébastien Marcel. On the vulnerability of finger vein recognition to spoofing attacks. In Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG’14), pages 111–120, September 2014.

  19. R. Raghavendra, M. Avinash, S. Marcel, and C. Busch. Finger vein liveness detection using motion magnification. In Procdings of the Seventh IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS’15), 2015.

  20. Jin Yeong Bok, Kun Ha Shu, Eui Chul Lee. Detecting FingerVein Data using Remote Photoplethysmography. Electronics 8(9): 1016, 2019.

    Article  Google Scholar 

  21. Simon Kirchgasser, Christof Kauba, and Andreas Uhl. Towards Understanding Acquisition Conditions Inuencing Finger-Vein Recognition. In [2], 21 pages, 2020.

  22. He Zheng, Qiantong Xu, Yapeng Ye, and Wenxin Li. Effects of meteorological factors on finger vein recognition. In IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2017, New Delhi, India, February 22-24, 2017, pages 1–8, 2017.

  23. Daniel Hartung and Christoph Busch. Why vein recognition needs privacy protection. In Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP’09), pages 1090–1095, 2009.

  24. Sneha Das and C Malathy. Survey on diagnosis of diseases from retinal images. Journal of Physics: Conference Series, 1000(1):012053, 2018.

    Google Scholar 

  25. Nada Elhussieny, Hossam El-Rewaidy, and Ahmed S Fahmy. Low cost system for screening cardiovascular diseases in large population: preliminary results. In 13th International IEEE Symposium on Biomedical Imaging (ISBI’18), 2016.

  26. Abhijit Das, Umapada Pal, Miguel A. Ferrer, Michael Blumenstein, Dejan Stepec, Peter Rot, Ziga Emersic, Peter Peer, Vitomir Struc, S. V. Aruna Kumar, and B. S. Harish. SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition. In 2017 IEEE International Joint Conference on Biometrics, IJCB 2017, Denver, CO, USA, October 1-4, 2017, pages 742–747, 2017.

  27. H. C. Lee, K. R. Park, B. J. Kang, and S. J. Park. A new mobile multimodal biometric device integrating finger vein and fingerprint recognition. In Proceedings of the 4th International Conference on Ubiquitous Information Technologies Applications, pages 1–4, 2009.

  28. J. H. Song, C. Kim, and Y. Yoo. Vein visualization using a smart phone with multispectral wiener estimation for point-of-care applications. IEEE Journal of Biomedical and Health Informatics, 19(2):773–778, 2015.

    Article  Google Scholar 

  29. Hideo Sato. Finger vein verification technology for mobile apparatus. In Proceedings of the International Conference on Security and Cryptography (SECRYPT’09), pages 37–41, 2009.

  30. S. Bazrafkan, T. Nedelcu, C. Costache, and P. Corcoran. Finger vein biometric: Smartphone footprint prototype with vein map extraction using computational imaging techniques. In Proceedings of the IEEE International Conference on Consumer Electronics (ICCE’16), pages 512–513, 2016.

  31. R. R. Fletcher, V. Raghavan, R. Zha, M. Haverkamp, and P. L. Hibberd. Development of mobile-based hand vein biometrics for global health patient identification. In IEEE Global Humanitarian Technology Conference (GHTC 2014), pages 541–547, 2014..

  32. A. Sierro, P. Ferrez, and P. Roduit. Contact-less palm/finger vein biometrics. In Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG’15), pages 145–156, 2015.

  33. C. Zhang, Z. Liu, Y. Liu, F. Su, J. Chang, Y. Zhou, and Q. Zhao. Reflection-type finger vein recognition for mobile applications. Journal of the Optical Society of Korea, 19(5):467–476, 2015.

    Article  Google Scholar 

  34. Luca Debiasi, Christof Kauba, Bernhard Prommegger, and Andreas Uhl. Near-infrared illumination add-on for mobile hand-vein acquisition. In Proceedings of the IEEE 9th International Conference on Biometrics: Theory, Applications, and Systems (BTAS2018), pages 1–9, Los Angeles, California, USA, 2018.

  35. K. Jini, H. Lu, Z. Sun, C. Cheng, J. Ye, and D. Qian. Telemedicine screening of retinal diseases with a handheld portable non-mydriatic fundus camera. BMC Ophthalmology, 17:89, 2017.

    Article  Google Scholar 

  36. T. Swedish, K. Roesch, I.K. Lee, K. Rastogi, S. Bernstein, and R. Raskar. eyeselfie: Self directed eye alignment using reciprocal eye box imaging. ACM Trans. Graph., 34(4), 2015.

    Article  Google Scholar 

  37. L.J. Haddock and C. Qian. Smartphone technology for fundus photography. Retinal Physician, 12(6):51–58, 2015.

    Google Scholar 

  38. S. Alkassar, W. Woo, S. Dlay, and J. Chambers. Sclera recognition: on the quality measure and segmentation of degraded images captured under relaxed imaging conditions. IET Biometrics, 6(4):266–275, 2017.

    Article  Google Scholar 

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Authors and Affiliations

  1. Salzburg, Österreich

    Andreas Uhl

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  1. Andreas Uhl
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Correspondence to Andreas Uhl.

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Uhl, A. Venen Biometrie . Datenschutz Datensich 44, 16–22 (2020). https://doi.org/10.1007/s11623-019-1215-2

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