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Multi-feature fusion partitioned local binary pattern method for finger vein recognition

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Abstract

The texture of finger veins is distributed in a network structure, which can be described as regional texture feature. In the image preprocessing stage, noise generated by segmentation algorithm will lead to the loss of texture structure information. Local binary pattern (LBP) feature extraction, which does not require segmentation of images, can effectively reveal local texture features and is robust to monotonic changes in grayscale. However, the LBP operator has two obvious shortcomings: (1) the microscopic limitation: it is easy to lose local information; (2) the feature unity: it will lead to the loss of other feature information. To tack these problems, this paper proposes a multi-feature partitioned local binary pattern (MFPLBP) operator for finger vein recognition. The concept of multi-feature partition is employed to extend the traditional LBP operator. Through the partition processing of the finger vein feature image, the global and local grasp of the image is enhanced, and the influence of local noise on the overall recognition accuracy is weakened. Additionally, the idea of multi-feature fusion is used to make up for the singleness of traditional algorithms. In image recognition, the histogram cross-check is used to judge the similarity of the vein feature histogram. Finally, the experiment showed that the recognition rate of this method has increased by about 13% compared with LBP, and it has increased by about 2% compared with partitioned local binary pattern (PLBP) and traditional multi-scale LBP.

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Funding

This work was partially supported by National Science foundation of China (Grant: 11701478,), in part by the Science and Technology Support Project of Sichuan Province under Grant 2020YFG0045 and 2020YFG0238, in part by the Fundamental Research Funds for the Central Universities under Grant No.2682021ZTPY100.

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  1. School of Mathematics at Southwest, Jiaotong University, Chengdu, 610031, China

    Zhongxia Zhang & Mingwen Wang

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  1. Zhongxia Zhang
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  2. Mingwen Wang
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Correspondence to Mingwen Wang.

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Zhang, Z., Wang, M. Multi-feature fusion partitioned local binary pattern method for finger vein recognition. SIViP 16, 1091–1099 (2022). https://doi.org/10.1007/s11760-021-02058-2

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  • DOI: https://doi.org/10.1007/s11760-021-02058-2

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