Abstract
This paper introduces a novel method to enlarge the hand-dorsa vein database using principal component analysis (PCA), which will be applied to increase the samples of each class. The ten samples of each hand is divided into two sets, feature set B and projection set M. Set B is used to provide the feature space using PCA methods. Set M is used to obtain projection coefficients for new image. A new sample can be constructed with the feature space and projection coefficient using PCA reconstruction method. In this work, the database is enlarged from 2040 images to 10200 images, with the samples of each hand increasing from 10 to 50. The experimental results show that the enlarged database has a satisfied recognition rate of 98.66 % using Partition Local Binary Patterns (PLBP), which indicates the proposed method performs well and would be applicable in the simulation test.
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Acknowledgments
This work is supported by National Natural Science Foundation of Shandong Province (ZR2015FL018).
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Li, K., Zhang, G., Wang, Y., Wang, P., Ni, C. (2016). Enlargement of the Hand-Dorsa Vein Database Based on PCA Reconstruction. 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_32
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DOI: https://doi.org/10.1007/978-3-319-46654-5_32
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