In this paper, the Center-Symmetric local binary pattern (CSLBP) operator is firstly used as a feature extraction method for finger vein recognition. The CSLBP feature can be viewed as a combination of the texture-based feature and the gradient-based feature. Moreover, CSLBP is easy-to-implement and computational simplicity. However, due to its small spatial support area, the bit-wise comparison therein made between two single pixel values is much affected by noise and sensitive to image translation and rotation. To address this problem, we further present a modified feature, termed Multi-scale Block Center-Symmetric local binary pattern (MB-CSLBP). Instead of individual pixel, in MB-CSLBP we perform the comparison based on average values of block sub- regions. It encodes not only microstructures but also macrostructures of image patterns, and hence provides a more complete image representation than the basic LBP and CSLBP operator. Experiments show that better performances are gained by the proposed method. © Springer International Publishing 2013.
CITATION STYLE
Xiao, R., Yang, G., Yin, Y., & Yang, L. (2013). Modified binary pattern for finger vein recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8232 LNCS, pp. 258–265). https://doi.org/10.1007/978-3-319-02961-0_32
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