Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 2373818, 14 pages
https://doi.org/10.1155/2017/2373818
Research Article

SIFT Based Vein Recognition Models: Analysis and Improvement

School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221006, China

Correspondence should be addressed to Guoqing Wang; moc.liamtoh@tmucgniqouggnaw

Received 19 November 2016; Revised 15 April 2017; Accepted 9 May 2017; Published 7 June 2017

Academic Editor: Hiro Yoshida

Copyright © 2017 Guoqing Wang and Jun Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. W. Kang, Y. Liu, Q. Wu, and X. Yue, “Contact-free palm-vein recognition based on local invariant features,” PLoS ONE, vol. 9, no. 5, Article ID e97548, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. N. Miura, A. Nagasaka, and T. Miyatake, “Extraction of finger-vein patterns using maximum curvature points in image profiles,” IEICE Transactions on Information and Systems, vol. E90-D, no. 8, pp. 1185–1194, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. J. H. Choi, “Finger vein extraction using gradient normalization and principal curvature,” Proceedings of SPIE—The International Society for Optical Engineering 7251 (2009), January 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. W. Song, T. Kim, H. C. Kim, J. H. Choi, H.-J. Kong, and S.-R. Lee, “A finger-vein verification system using mean curvature,” Pattern Recognition Letters, vol. 32, no. 11, pp. 1541–1547, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. W.-Y. Han and J.-C. Lee, “Palm vein recognition using adaptive Gabor filter,” Expert Systems with Applications, vol. 39, no. 18, pp. 13225–13234, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. J.-C. Lee, “A novel biometric system based on palm vein image,” Pattern Recognition Letters, vol. 33, no. 12, pp. 1520–1528, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. W. M. Yang, X. Huang, F. Zhou, and Q. M. Liao, “Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion,” Information Sciences, vol. 268, pp. 20–32, 2014. View at Google Scholar
  8. E. C. Lee, H. C. Lee, and R. P. Kang, “Finger vein recognition using minutia-based alignment and local binary pattern-based feature extraction,” in Proceedings of the Lee HC and Kang RP, vol. 19, pp. 179–186, August 2009.
  9. X. Qian, S. Guo, X. Li, F. Zhong, and X. Shao, “Finger-vein recognition based on the score level moment invariants fusion,” in Proceedings of the 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009, chn, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Li, S. Guo, F. Gao, and Y. Li, “Vein pattern recognitions by moment invariants,” in Proceedings of the 2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE, pp. 612–615, chn, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. P. O. Ladoux, C. Rosenberger, and B. Dorizzi, “Palm vein verification system based on SIFT matching,” in Proceedings of the 3rd International Conference on Advances in Biometrics, vol. 5558 of Lecture Notes in Computer Science, pp. 1290–1298, Springer. View at Publisher · View at Google Scholar
  12. S. J. Xie, Y. Lu, S. Yoon, J. Yang, and D. S. Park, “Intensity variation normalization for finger vein recognition using guided filter based singe scale retinex,” Sensors (Switzerland), vol. 15, no. 7, pp. 17089–17105, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. X. Li, T. Liu, S. Deng, J. He, and Y. Wang, “Fast recognition of hand vein with SURF descriptors,” Chinese Journal of Scientific Instrument, vol. 32, no. 4, pp. 831–836, December 2014. View at Google Scholar
  14. H. G. Kim, E. J. Lee, G. J. Yoon, S.-D. Yang, E. C. Lee, and S. M. Yoon, “Illumination normalization for SIFT based finger vein authentication,” Advances in Visual Computing, vol. 7432, no. 2, pp. 21–30, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. X. Yan, W. Kang, F. Deng, and Q. Wu, “Palm vein recognition based on multi-sampling and feature-level fusion,” Neurocomputing, vol. 151, no. 2, pp. 798–807, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Pan and W. Kang, “Palm vein recognition based on three local invariant feature extraction algorithms,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7098, pp. 116–124, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. H. Wang, L. Tao, and X. Hu, “Novel Algorithm for Hand Vein Recognition Based on Retinex Method and SIFT Feature Analysis,” Electrical Power Systems Computers, vol. 99, pp. 559–566, June 2011. View at Publisher · View at Google Scholar
  18. D. Huang, Y. Tang, Y. Wang, L. Chen, and Y. Wang, “Hand-Dorsa Vein Recognition by Matching Local Features of Multisource Keypoints,” IEEE Transactions on Cybernetics, vol. 45, no. 9, pp. 1823–1837, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Ch, B. Pu, and A. La, “Dorsal Hand Vein Image Enhancement for Improve Recognition Rate Based on SIFT Keypoint Matching,” in Proceedings of the 2nd International Symposium on Computer Communication Control Automation, pp. 174–177, 2016.
  20. J. Zhao, H. Tian, W. Xu, and X. Li, “A new approach to hand vein image enhancement,” in Proceedings of the 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009, pp. 499–501, chn, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. P. R. Deepak, P. Neelamegam, S. Sriram, and R. Nagarajan, “Enhancement of vein patterns in hand image for biometric and biomedical application using various image enhancement techniques,” in Proceedings of the International Conference on Modelling Optimization and Computing, pp. 1174–1185, ind, April 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. K. Dharavath, G. Amarnath, F. A. Talukdar, and R. H. Laskar, “Impact of image preprocessing on face recognition: A comparative analysis,” in Proceedings of the 3rd International Conference on Communication and Signal Processing, ICCSP 2014, pp. 631–635, ind, April 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. S. G. Stanciu, D. E. Tranca, and D. Coltuc, “Contrast enhancement influences the detection of gradient based local invariant features and the matching of their descriptors,” Journal of Visual Communication and Image Representation, vol. 32, pp. 246–256, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. R. Kalia, K.-D. Lee, B. V. R. Samir, S.-K. Je, and W.-G. Oh, “An analysis of the effect of different image preprocessing techniques on the performance of SURF: Speeded Up Robust Features,” in Proceedings of the 2011 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2011, kor, February 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. P. Campos, M. Forkin, and X. Xu, “Evaluating Gabor preprocessing for SIFT-based ocular recognition,” in Proceedings of the 49th Annual Association for Computing Machinery Southeast Conference, ACMSE'11, pp. 365-366, usa, March 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. J. L. Johnson, D. A. Gregory, and J. C. Kirsch, “Incoherent image intensity normalization, contour enhancement, and pattern recognition system,” US4743097[P], 1988.
  27. D. H. Rao and P. P. Pan, “A survey on image enhancement techniques: Classical spatial filter, neural network, cellular neural network and fuzzy filter,” in Proceedings of the IEEE International Conference on Industrial Technology, pp. 2821–2826, 2006.
  28. N. R. Mokhtar, N. H. Harun, M. Y. Mashor et al., “Image enhancement techniques using local, global, bright, dark and partial contrast stretching for acute leukemia images,” in Proceedings of the World Congress on Engineering, pp. 807–812, 2009.
  29. G. Xu, J. Su, H. Pan, Z. Zhang, and H. Gong, “An image enhancement method based on gamma correction,” in Proceedings of the 2009 International Symposium on Computational Intelligence and Design, ISCID 2009, pp. 60–63, chn, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. R. Srivastava, J. Gupta R P, H. Parthasarthy, and S. Srivastava, “PDE based unsharp masking, crispening and high boost filtering of digital images,” in Proceedings of the 2nd International Conference on Contemporary Computing, pp. 8–43, 2009.
  31. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley Longman Publishing, 2nd edition, 2001.
  32. R. Hummel, “Image enhancement by histogram transformation,” Computer Graphics Image Processing, vol. 6, no. 2, pp. 184–195, October 1977. View at Google Scholar
  33. J. B. Zimmerman, S. M. Pizer, E. V. Staab, J. R. Perry, W. McCartney, and B. C. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Transactions on Medical Imaging, vol. 7, no. 4, pp. 304–312, 1988. View at Publisher · View at Google Scholar · View at Scopus
  34. K. Santhi and R. Banu, “Adaptive histogram equalization and its variations,” Signal Image and Video Processing, vol. 9, no. 1, pp. 73–87, 2015. View at Google Scholar
  35. C. Zuo, Q. Chen, and X. B. Sui, “Range limited bi-histogram equalization for image contrast enhancement,” OPTIK, vol. 124, no. 5, pp. 425–431, 2013. View at Google Scholar
  36. E. H. Land, “An alternative technique for the computation of the designator in the retinex theory of color vision.,” Proceedings of the National Academy of Sciences of the United States of America, vol. 83, no. 10, pp. 3078–3080, 1986. View at Publisher · View at Google Scholar · View at Scopus
  37. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. View at Publisher · View at Google Scholar · View at Scopus
  38. J. Wang, G. Wang, M. Li, K. Wang, and T. Hao, “Hand vein recognition based on improved template matching,” International Journal Bioautomation, vol. 18, no. 4, pp. 337–348, 2014. View at Google Scholar
  39. R. Kabacinski and M. Kowalski, “Vein pattern database and benchmark results,” Electronics Letters, vol. 47, no. 20, pp. 26–32, 2011. View at Google Scholar
  40. A. Relja and A. Zisserman, “Three things everyone should know to improve object retrieval,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2911–2918, 2012.
  41. J. T. Arnfred, S. Winkler, and S. Süsstrunk, “Mirror match: Reliable feature point matching without geometric constraints,” in Proceedings of the 2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013, pp. 256–260, jpn, November 2013. View at Publisher · View at Google Scholar · View at Scopus
  42. A. Curz, J. C. Caicedo, and F. A. Gonzalez, “Visual pattern mining in hitology image collection using bag of features,” Artificial Intelligence in Medicine, vol. 52, no. 2, pp. 91–106, 2011. View at Google Scholar
  43. J. T. Arnfred and S. Winkler, “A general framework for image feature matching without geometric constraints,” Pattern Recognition Letters, vol. 73, pp. 26–32, 2016. View at Publisher · View at Google Scholar · View at Scopus