Abstract
With the fast development of the information, the application of distributed recognition system becomes more widespread. But the difference of hardware condition of terminal acquisition and all kinds of environments in distributed recognition system made biometric feature images different, which were gathered by different hardware. Include contrast, lightness, shifting, angle of rotation, size and so on. These differences will inevitably reduce accuracy of recognition and will not satisfy the development needs of the times. This paper synthetically analyses the important factors of heterogeneous dorsal hand vein images which are resulted by different devices. After normalizing grayscale images, this paper uses a segmentation method based on gradient difference to segment the texture of veins and uses SIFT to extract and match features. Discrimination in this paper can improve to 90.17 %, which is higher than other algorithms. This method can effectively solve the problem about dorsal hand vein recognition across different devices.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Standring, S.: Gray’s Anatomy, 39th edn. Elsevier Churchill Livingston, Edinburgh (2005)
Cross, J.M., Smith, C.L.: Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification. In: 29th International Carnahan Conference on Security Technology, pp. 20–35 (1995)
Wang, L., Leedham, G., Cho, S.Y.: Infrared imaging of hand vein patterns for biometric purposes. Comput. Vis. 1(3–4), 113–122 (2007)
Kumar, A., Prathyusha, K.V.: Personal authentication using hand vein triangulation and knuckle shape. IEEE Trans. Image Process. 18, 2127–2136 (2009)
Shahin, M., Badawi, A., Kamel, M.: Biometric authentication using fast correlation of near infrared hand vein patterns. Int. J. Biomed. Sci. 2(3), 141–148 (2007)
Wang, Y., Wang, H.: Gradient based image segmentation for vein pattern. In: Fourth International Conference on Computer Sciences and Convergence Information Technology, pp. 1614–1618 (2009)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Bakshi, S., Mehrotra, H., Majhi, B.: Postmatch pruning of SIFT pairs for iris recognition. International Journal of Biometrics 5(2), 160–180 (2013)
Morales, A., Ferrer, M.A., Kumar, A.: Towards contactless palmprint authentication. IET Comput. Vis. 5(6), 407–416 (2011)
Meng, X., Yin, Y., Yang, G., et al.: Retinal identification based on an improved circular Gabor filter and scale invariant feature transform. Sensors 13(7), 9248–9266 (2013)
Abaza, A., Bourlai, T.: On ear-based human identification in the mid-wave infrared spectrum. Image Vis. Comput. 31(9), 640–648 (2013)
Tan, C., Wang, H., Pei, D.: SWF-SIFT approach for infrared face recognition. Tsinghua Sci. Technol. 15(3), 357–362 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Wang, Y., Zheng, X., Wang, C. (2016). Dorsal Hand Vein Recognition Across Different Devices. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-46654-5_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46653-8
Online ISBN: 978-3-319-46654-5
eBook Packages: Computer ScienceComputer Science (R0)