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2024 | vol. 1 (30) | 5--19
Tytuł artykułu

Forearm vein pattern recognition using features from visible and NIR images

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Języki publikacji
EN
Abstrakty
EN
Forearm vein recognition is one of many available methods used for iden-tification. However, forearm veins can be considered more secure compared to otherbiometric traits because the veins are inside the human body and therefore not easilymanipulated. Veins possess several properties that make a good biometric feature forpersonal identification: 1) they are difficult to damage and modify; 2) they are difficultto simulate using a fake template; and 3) vein information can represent the liveness ofperson. Features were extracted from each pair of visible and NIR images. For the visibleimages, feature extraction was done using the Gabor filter. For the NIR forearm images,a crossing number was used to extract properties of the veins e.g. bifurcation. We presentthe results of the recognition of forearm veins patterns that show the suitability of themethod for biometric identification purposes.
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Strony
5--19
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr.
Twórcy
  • Bydgoszcz University of Science and Technology, Department of Telecommunications, Computer Sciences and Electrical Engineering, al. prof. S. Kaliskiego 7, 85-796 Bydgoszcz, Poland, choras@pbs.edu.pl
Bibliografia
  • 1. Choras, R.S. Iris Recognition, Computer Recognition Systems 3, AISC 57, 637-644, Springer Verlag Berlin Heidelberg, 2009.
  • 2. Choras, R.S. Personal identification using forearm vein patterns. 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI), 2017, 1-5.
  • 3. Choras, R.S. Biometric personal authentication using images of forearm vein patterns. 2017 International Conference on Signals and Systems (ICSigSys), 2017, 40-43
  • 4. Ding, Y., Zhuang, D., Wang, K., A study of hand vein recognition method, Proc. IEEE Intl. Conf. Mechatronics & Automation, 2005, 2106-2110.
  • 5. Gonzales, R.C., Woods, R.E., Digital Image Processing, Pearson Prentice Hall, 2008.
  • 6. Haralick, R., Shanmugam, K., Dinstein, I. Textural features for image classification. IEEE Trans. on Systems, Man, and Cybernetics, 1973, SMC-3(6), 610-621.
  • 7. Kang, B.J., et al,. Multimodal biometric method based on vein and geometry of a single finger. IET Computer Vision, 2010, 4.3, 209-217.
  • 8. Kirbas, C., Quek, K. Vessel extraction techniques and algorithm: a survey. Proceedings of the 3rd IEEE Symposium on BioInfomratics and Bioengineering, 2003.
  • 9. Kono, M., et al,. Near-infrared finger vein patterns for personal identification. Applied Optics, 2002, 41(35), 7429-7436.
  • 10. Kumar, A. Incorporating cohort information for reliable palmprint authentication. Proceeding of ICVGIP, 2008, 583-590.
  • 11. Kumar, A., Prathyusha, K. Venkata., Personal Authentication using Hand Vein Triangulation and Knuckle Shape. IEEE Transactions on Image Processing, 2009.
  • 12. Lee, H.C., et al, Finger vein recognition usin. Journal of Zhejiang University-SCIENCE C, 2010, 11(7), 514-524.
  • 13. Lee, E.C., et al., New finger biometric method using near infrared imaging. Sensors, 2011, 11, 2319-2333.
  • 14. Liu, C. J., Wechsler, H. Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing, 2002, 11(4), 467- 476.
  • 15. Liu, D. H., Lam, K. M., Shen, L. S. Optimal sampling of Gabor features for face recognition. Pattern Recognition Letters, 2004, 25(2), 267-276.
  • 16. Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S. Handbook of Fingerprint Recognition, 2003, Springer.
  • 17. Meng, X., Yin, Y., Yang, G., Xi, X. Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform. Sensors, 2013, 13, 9248-9266.
  • 18. Pierre-Olivier, L., Christophe, R., Bernadette, D. Palm vein verification system based on SIFT matching. Proceedings of Third International Conference ICB, 2009, 1290-1298.
  • 19. Park, U., Jillela, R., Ross, A., Jain, A. Periocular biometrics in the visible spectrum. IEEE Trans. Inform. Forensics Secur., 2011, 6(1), 96-106.
  • 20. Ranade, A., Rosenfeld, A. Point pattern matching by relaxation, Pattern Recognition, 1993, 12(2), 269-275.
  • 21. Ross, A., Nandakumar, K., Jain, A.K. Handbook of Multibiometrics, Springer, 2006.
  • 22. Tanaka, T., Kubo, N. Biometric authentication by hand vein patterns. Proc. SICE Annual Conference, 2004, 249-253.
  • 23. Wang, D., et al., User identication based on finger-vein patterns for consumer electronics devices. IEEE Transactions on Consumer Electronics, 2010, 56.2, 799-804
  • 24. Wang,Y.,Li, K., Cui, J. Hand-dorsa vein recognition based on partition local binary pattern. IEEE 10th International Conference on Signal Processing (ICSP), 2010, 1671-1674.
  • 25. Wang, Y., Hu, J., Phillips, D. A fingerprint orientation model based on 2d fourier expan sion (FOMFE) and its application to singular-point detection and fingerprint indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(4), 573-585.
  • 26. Woodard, D., Pundlik, S., Lyle, J., Miller, P. Periocular region appearance cues for biometric identification. Computer Vision and Pattern Recognition Workshops (CVPRW), 2010, 162-169.
  • 27. Yuksel, A., Akarun, L., Sankur, B. Biometric identification through hand vein patterns. International Workshop in Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB), 2010, 1-6.
  • 28. Xueyan, L., Shuxu, G., Fengli, G., Ye, L. Vein pattern recognitions by moment invariants. Proceedings of the First International Conference on Bioinformatics and Biomedical Engineering, 2007, 612-615.
  • H. Zhang et al, Finger Vein Recognition Based on Gabor Filter, Intelligence Science and Big Data Engineering (Lecture Notes), (2013) pp. 827-834
  • 29. Zeng, Z., Hu, J., Face Recognition Based on Shearlets and Principle Component Analysis. IEEE International Conference on Intelligent Networking and Collaborative Systems, 2013, 697-701.
  • 30. Zhang, H., et al. Finger Vein Recognition Based on Gabor Filter. Intelligence Science and Big Data Engineering, 2013, 827-834.
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  • 32. Zhang, Z., et al. Multiscale Feature Extraction of Finger-Vein Patterns Based on Curvelets and Local Interconnection Structure Neural Network. The 18th International Conference on Pattern Recognition, 2006, 4, 145-148.
  • 33. Zuiderveld, K. Contrast Limited Adaptive Histogram Equalization, 1994, Academic Press, Cambridge
Typ dokumentu
Bibliografia
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