Papers
Topics
Authors
Recent
Search
2000 character limit reached

$\textit{sweet}$- An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments

Published 14 Apr 2024 in cs.CV | (2404.09376v2)

Abstract: Current finger-vein or palm-vein recognition systems usually require direct contact of the subject with the apparatus. This can be problematic in environments where hygiene is of primary importance. In this work we present a contactless vascular biometrics sensor platform named \sweet which can be used for hand vascular biometrics studies (wrist, palm, and finger-vein) and surface features such as palmprint. It supports several acquisition modalities such as multi-spectral Near-Infrared (NIR), RGB-color, Stereo Vision (SV) and Photometric Stereo (PS). Using this platform we collect a dataset consisting of the fingers, palm and wrist vascular data of 120 subjects and develop a powerful 3D pipeline for the pre-processing of this data. We then present biometric experimental results, focusing on Finger-Vein Recognition (FVR). Finally, we discuss fusion of multiple modalities, such palm-vein combined with palm-print biometrics. The acquisition software, parts of the hardware design, the new FV dataset, as well as source-code for our experiments are publicly available for research purposes.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (43)
  1. L. Wang and G. Leedham, “Near- and far-infrared imaging for vein pattern biometrics,” in 2006 IEEE International Conference on Video and Signal Based Surveillance.   IEEE, 2006, pp. 52–52.
  2. J. Hashimoto, “Finger vein authentication technology and its future,” in 2006 Symposium on VLSI Circuits, 2006. Digest of Technical Papers.   IEEE, 2006, pp. 5–8.
  3. K. Shaheed et al., “A systematic review of finger vein recognition techniques,” Information, vol. 9, no. 9, p. 213, 2018.
  4. M. Vanoni et al., “Cross-database evaluation using an open finger vein sensor,” in 2014 IEEE workshop on biometric measurements and systems for security and medical applications (BIOMS) proceedings.   IEEE, 2014, pp. 30–35.
  5. A. H. Mohsin et al., “Finger vein biometrics: taxonomy analysis, open challenges, future directions, and recommended solution for decentralised network architectures,” IEEE Access, vol. 8, pp. 9821–9845, 2020.
  6. R. Raghavendra et al., “Design and development of low-cost sensor to capture ventral and dorsal finger vein for biometric authentication,” IEEE Sensors Journal, vol. 19, no. 15, pp. 6102–6111, 2019.
  7. J. A. Otter et al., “Evidence that contaminated surfaces contribute to the transmission of hospital pathogens and an overview of strategies to address contaminated surfaces in hospital settings,” American Journal of Infection Control, vol. 41, no. 5, pp. S6–S11, 2013.
  8. R. Raghavendra et al., “A low-cost multi-fingervein verification system,” in 2018 IEEE International Conference on Imaging Systems and Techniques (IST).   IEEE, 2018, pp. 1–6.
  9. G. K. O. Michael et al., “Design and implementation of a contactless palm print and palm vein sensor,” in 2010 11th International Conference on Control Automation Robotics & Vision.   IEEE, 2010, pp. 1268–1273.
  10. Z. Zhang, F. Zhong, and W. Kang, “Study on reflection-based imaging finger vein recognition,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 2298–2310, 2021.
  11. P. Tome and S. Marcel, “Palm vein database and experimental framework for reproducible research,” in 2015 international conference of the biometrics special interest group (BIOSIG).   IEEE, 2015, pp. 1–7.
  12. P. Chen et al., “Design of low-cost personal identification system that uses combined palm vein and palmprint biometric features,” IEEE Access, vol. 7, pp. 15 922–15 931, 2019.
  13. A. Sierro, P. Ferrez, and P. Roduit, “Contact-less palm/finger vein biometrics,” in 2015 International Conference of the Biometrics Special Interest Group (BIOSIG).   IEEE, 2015, pp. 1–12.
  14. R. Raghavendra and C. Busch, “A low cost wrist vein sensor for biometric authentication,” in 2016 IEEE International Conference on Imaging Systems and Techniques (IST).   IEEE, 2016, pp. 201–205.
  15. S. Bhattacharya, A. Ranjan, and M. Reza, “A portable biometrics system based on forehead subcutaneous vein pattern and periocular biometric pattern,” IEEE Sensors Journal, vol. 22, no. 7, pp. 7022–7033, 2022.
  16. W. Yuan and Y. Tang, “The driver authentication device based on the characteristics of palmprint and palm vein,” in 2011 International Conference on Hand-Based Biometrics.   IEEE, 2011, pp. 1–5.
  17. D. Zhang, Z. Guo, and Y. Gong, “Multispectral biometrics systems,” in Multispectral biometrics.   Springer, 2016, pp. 23–35.
  18. L. Spinoulas et al., “Multispectral biometrics system framework: Application to presentation attack detection,” IEEE Sensors Journal, vol. 21, no. 13, pp. 15 022–15 041, 2021.
  19. S. Crihalmeanu and A. Ross, “Multispectral scleral patterns for ocular biometric recognition,” Pattern Recognition Letters, vol. 33, no. 14, pp. 1860–1869, 2012.
  20. R. Rowe, K. Nixon, and S. Corcoran, “Multispectral fingerprint biometrics,” in Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop, 2005, pp. 14–20.
  21. Y. Hao et al., “Multispectral palm image fusion for accurate contact-free palmprint recognition,” in 2008 15th IEEE International Conference on Image Processing.   IEEE, 2008, pp. 281–284.
  22. K. I. Chang et al., “Multimodal 2d and 3d biometrics for face recognition,” in 2003 IEEE International SOI Conference. Proceedings (Cat. No. 03CH37443).   IEEE, 2003, pp. 187–194.
  23. X. Liang et al., “Innovative contactless palmprint recognition system based on dual-camera alignment,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 10, pp. 6464–6476, 2022.
  24. C. Kauba et al., “Three-dimensional finger vein recognition: A novel mirror-based imaging device,” Journal of Imaging, vol. 8, no. 5, 2022.
  25. W. Kang et al., “Study of a full-view 3d finger vein verification technique,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1175–1189, 2019.
  26. K. H. Cheng and A. Kumar, “Contactless biometric identification using 3d finger knuckle patterns,” IEEE transactions on pattern analysis and machine intelligence, vol. 42, no. 8, pp. 1868–1883, 2019.
  27. L. Chen, X. Wang, H. Jiang, L. Tang, Z. Li, and Y. Du, “Design of palm vein platform and pattern enhancement model based on raspberry pi,” in 2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT).   IEEE, 2021, pp. 495–498.
  28. I. P. A. S. Gunawan et al., “Vein visualization system using camera and projector based on distance sensor,” in 2018 International Electronics Symposium on Engineering Technology and Applications (IES-ETA).   IEEE, 2018, pp. 150–156.
  29. Y. Yin et al., “SDUMLA-HMT: A multimodal biometric database,” in Biometric Recognition.   Springer Berlin Heidelberg, 2011, pp. 260–268.
  30. Y. Lu et al., “An available database for the research of finger vein recognition,” vol. 1, 12 2013, pp. 410–415.
  31. B. T. Ton and R. N. J. Veldhuis, “A high quality finger vascular pattern dataset collected using a custom designed capturing device,” in Proc. 2013 Intl. Conf. on Biometrics (ICB), 2013, pp. 1–5.
  32. X. Qiu, W. Kang, S. Tian, W. Jia, and Z. Huang, “Finger vein presentation attack detection using total variation decomposition,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 2, pp. 465–477, 2018.
  33. M. Miura, A. Nagasaka, and T. Miyatake, “Feature Extraction of finger-vein patterns based on repeated line tracking and its Application to Personal Identification,” in Proceedings on IAPR conference on Machine Vision and Applications, vol. 15, 2004, pp. 194 – 203.
  34. ——, “Extraction of Finger-Vein Pattern Using Maximum Curvature Points in Image Profiles,” in Proceedings on IAPR conference on Machine Vision Applications, 2005, pp. 347–350.
  35. B. Huang et al., “Finger-vein authentication based on wide line detector and pattern normalization,” in 2010 20th International Conference on Pattern Recognition, 2010, pp. 1269–1272.
  36. J. Yang, Y. Shi, and R. Wu, “Finger-vein recognition based on gabor features,” in Biometric Systems, Z. Riaz, Ed.   Rijeka: IntechOpen, 2011, ch. 2. [Online]. Available: https://doi.org/10.5772/17182
  37. I. Kovač and P. Marák, “Finger vein recognition: utilization of adaptive gabor filters in the enhancement stage combined with sift/surf-based feature extraction,” Signal, Image and Video Processing, vol. 17, pp. 635 – 641, 2023.
  38. H. Bay et al., “SURF: Speeded up robust features,” Computer Vision and Image Understanding (CVIU), vol. 110, no. 3, pp. 346–359, 2008.
  39. R. Zhang et al., “Deep learning for finger vein recognition: A brief survey of recent trend,” 07 2022. [Online]. Available: https://arxiv.org/pdf/2207.02148.pdf
  40. V. Bros, K. Kotwal, and S. Marcel, “Vein enhancement with deep auto-encoders to improve finger vein recognition,” in Biometrics Special Interest Group (BIOSIG 2021), 2021.
  41. H. Hirschmuller, “Stereo processing by semiglobal matching and mutual information,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 328–341, 2008.
  42. R. J. Woodham, “Photometric method for determining surface orientation from multiple images,” Optical Engineering, vol. 19, no. 1, pp. 139–144, 1980.
  43. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
Citations (1)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Summarize

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Tweets

Sign up for free to view the 1 tweet with 0 likes about this paper.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up for Free