Abstract
In this paper, a new feature descriptor is presented and proposed for personal verification based on near infrared images of hand-dorsa veins. This new feature descriptor is a modification of the previously proposed partition local binary patterns (PLBP) by adding feature weighting, combining multi-scale PLBP and fusion with structure information. While addition of feature weighting aims to reduce the influence of insignificant local binary patterns, combination of multi-scale features aims to get more texture information and fusion with structure feature aims to increase binary information. Testing on a large database with more than two thousand hand-dorsa vein images, Multi-scale PLBP (MPLBP) is shown to be more effective than the original PLBP and Weighted PLBP (WPLBP), and offers a better performance in recognition of hand-dorsa vein images with a correct recognition rate reaching approximately 99% using a simple nearest neighbor (NN) classifier.
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References
Ding, Y., Zhuang, D., Wang, K.: A study of hand vein recognition method. In: 2005 IEEE International Conference on Mechatronics and Automations, pp. 2106–2110 (2005)
Delac, K., Grgic, M.: A survey of biometric recognition methods. In: 46th International Symposium Electronics in Marine, pp. 184–193 (2004)
Wang, R., Wang, G., Chen, Z., Zeng, Z.: A palm vein identification system based on Gabor wavelet features. Neural Computing & Applications 24(1), 161–168 (2014)
Wang, Y., Li, K., Cui, J.: Hand-dorsa vein recognition based on partition local binary pattern. In: 10th International Conference on Signal Processing, Beijing, pp. 1671–1674 (2010)
Zhao, S., Wang, Y.: Extracting hand vein patterns from low-quality images: a new biometric technique using low-cost devices. In: 4th International Conference on Image and Graphics, Sichuan, pp. 667–671 (2007)
Cross, J.M., Smith, C.L.: Thermo graphic imaging of the subcutaneous vascular network of the back of the hand for biometric identification. In: International Carnahan Conference on Security Technology (1995)
Wang, L., Leedham, G.: Near- and far-infrared imaging for vein pattern biometrics. In: IEEE International Conference on Video Signal Based Surveillance, Sydney, pp. 52–57 (2006)
Wang, Y., Fan, Y., Liao, W., Li, K., Lik-Kwan, S., Martin, V.: Hand vein recognition based on multiple keypoints sets. In: International Conference on Biometrics (2012)
Hu, Y., Wang, Z., Yang, X., Xue, Y.: Hand vein recognition based on the connection lines of reference point and feature point. Infrared Physics & Technology 62, 110–114 (2014)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Flusser, J.: On the Independence of Rotation Moment Invariants. Pattern Recognition Letters 33, 1405–1410 (2000)
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Li, K., Zhang, G., Wang, Y., Wang, P., Ni, C. (2015). Hand-dorsa Vein Recognition Based on Improved Partition Local Binary Patterns. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_37
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DOI: https://doi.org/10.1007/978-3-319-25417-3_37
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