Advertisement

Dorsal Hand Recognition

Chapter

Abstract

Dorsal hand recognition has drawn much attention due to its commonality, uniqueness, and stability in biometrics . In particular, the hand vein, which is considered as the main identification information in dorsal hand, is a living feature and hard to be fabricated, so it reflects the huge advantage in improving the anti-spoof ability. However, the imaging of subcutaneous feature needs more requirements for the light source than other biometric features such as face . Unsuitable light source would result in poor-quality dorsal hand image with amount of important information loss or be mixed with useless information. To address the problem of spectra selection is a very effective way to improve final recognition performance of dorsal hand. Multispectral technique is just the one that helps us to search for the optimal band for light source around visible light part and near-infrared (NIR) light part in image acquisition process. On the other hand, the feature pattern is one of the key factors influencing the recognition performance. Considering that multispectral analysis should be implemented on the same feature pattern overall the whole spectra, applying a proper one is necessary for making sure that it can be suitable for not only dorsal hand vein extraction, but also multispectral images.

Keywords

Dorsal hand recognition Multispectral image Band selection Feature estimation 

References

  1. Badawi AM (2006) Hand vein biometric verification prototype: a testing performance and patterns similarity. IEEE Proc Image Process Comput Vis Pattern Recognit 2:26–29Google Scholar
  2. Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256. doi: 10.1109/34.121791 CrossRefGoogle Scholar
  3. Chen L, Zheng H, Li L, Xie P, Liu S (2007) Near-infrared dorsal hand vein image segmentation by local thresholding using grayscale morphology. In: 1st international conference on bioinformatics and biomedical engineering, pp 868–871. doi: 10.1109/ICBBE.2007.226
  4. Daugman JG (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11):1148–1161. doi: 10.1109/34.244676 CrossRefGoogle Scholar
  5. Ding Y, Zhuang D, Wang K (2005) A study of hand vein recognition method. IEEE Proc Int Conf Mechatron Autom 4:2106–2110. doi: 10.1109/ICMA.2005.1626888 Google Scholar
  6. Im S-K, Park H-M, Kim Y-W, Han S-C, Kim S-W, Kang C-H (2001) A biometric identification system by extracting hand vein patterns. Korean J Phys Soc 38(3):268–272Google Scholar
  7. Jia W, Huang D-S, Zhang D (2008) Palmprint verification based on robust line orientation code. Pattern Recognit 41(5):1504–1513. doi: 10.1016/j.patcog.2007.10.011 MATHCrossRefGoogle Scholar
  8. Kong A, Zhang D (2004) Competitive coding scheme for palmprint verification. Int Conf Pattern Recognit 1:520–523. doi: 10.1109/ICPR.2004.1334184 Google Scholar
  9. Kumar A, Prathyusha KV (2009) Personal authentication using hand vein triangulation and knuckle shape. IEEE Trans Image Process 18(9):2127–2136. doi: 10.1109/TIP.2009.2023153 MathSciNetCrossRefGoogle Scholar
  10. Li W, Zhang L, Zhang D, Lu G, Yan J (2010a) Efficient joint 2D and 3D palmprint matching with alignment refinement. IEEE Conf Comput Vis Pattern Recognit 795–801. doi: 10.1109/CVPR.2010.5540134
  11. Li X, Liu X, Liu Z (2010b) A dorsal hand vein pattern recognition algorithm. In: 3rd international congress on image and signal processing, vol 4, 1723–1726. doi: 10.1109/CISP.2010.5647776
  12. Liu X, Zou B, Sun J (2004) A new approach to separating touching spots in particle images. In: Proceedings of international symposium on intelligent multimedia, video and speech processing, pp 133–136. doi: 10.1109/ISIMP.2004.1434018
  13. Matcher SJ, Elwell CE, Cooper CE, Cope M, Delpy DT (1995) Performance comparison of several published tissue near-infrared spectroscopy algorithms. Anal Biochem 227(1):54–68. doi: 10.1006/abio.1995.1252 CrossRefGoogle Scholar
  14. Ostu N (1979) A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern 9(1):62–66. doi: 10.1109/TSMC.1979.4310076 CrossRefGoogle Scholar
  15. Ramalho SM, Correia P, Soares L (2011) Biometric identification through palm and dorsal hand vein patterns. IEEE Int Conf Comput Tool (EUROCON) 1–4. doi: 10.1109/EUROCON.2011.5929297
  16. Wang L, Leedham G, Cho S-Y (2007) Infrared imaging of hand vein patterns for biometric purposes. IET Comput Vis 1(3–4):113–122. doi: 10.1049/iet-cvi:20070009 MATHMathSciNetCrossRefGoogle Scholar
  17. Wang L, Leedham G, Cho DS-Y (2008a) Minutiae feature analysis for infrared hand vein pattern biometrics. Pattern Recognit 41(3):920–929. doi: 10.1016/j.patcog.2007.07.012 CrossRefGoogle Scholar
  18. Wang K, Zhang Y, Yuan Z, Zhuang D (2008b) Hand vein recognition based on multi supplemental features of multi-classifier fusion decision. IEEE Proc Int Conf Mechatron Autom 1790–1795. doi: 10.1109/ICMA.2006.257486
  19. Wu X, Gao E, Tang Y, Wang K (2010) A novel biometric system based on hand vein. In: 5th international conference on frontier of computer science and technology, pp 522–526. doi: 10.1109/FCST.2010.65
  20. Yang J, Zhang D, Frangi AF, Yang Y-J (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137. doi: 10.1109/TPAMI.2004.1261097 CrossRefGoogle Scholar
  21. Yuan W, Wang R, Sun S (2010) Palm-dorsa vein recognition based on two-dimensional fisher linear discriminant. Chin J Comput Appl 30(3):646–649Google Scholar
  22. Yuksel A, Akarun L, Sankur B (2010) Biometric identification through hand vein patterns. In: International workshop on emerging techniques and challenges for hand-based biometrics, pp 1–6. doi: 10.1109/ETCHB.2010.5559295
  23. Zhang D, Kong W-K, You J, Wong M (2003) Online palmprint identification. IEEE Trans Pattern Anal Mach Intell 25(9):1041–1050. doi: 10.1109/TPAMI.2003.1227981 CrossRefGoogle Scholar
  24. Zuo W, Zhang D, Wang K (2006) Bidirectional PCA with assembled matrix distance metric for image recognition. IEEE Trans Syst Man Cybern B Cybern 36(4):863–872. doi: 10.1109/TSMCB.2006.872274 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Biometrics Research CentreThe Hong Kong Polytechnic UniversityHung HomHong Kong SAR
  2. 2.Shenzhen Key Laboratory of Broadband Network & Multimedia, Graduate School at ShenzhenTsinghua UniversityShenzhenChina
  3. 3.University of Shanghai for Science and TechnologyShanghaiChina

Personalised recommendations