王红军 副研究员

硕士生导师

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学历:博士研究生毕业

学位:工学博士学位

办公地点:犀浦3号教学楼31529

毕业院校:四川大学

学科:电子信息. 软件工程. 计算机应用技术

所在单位:计算机与人工智能学院

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论文成果

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

影响因子:1.583

DOI码:10.1007/s11760-021-02058-2

所属单位:西南交通大学

发表刊物:Signal, Image and Video Processing

关键字:Local binary pattern · Feature extraction · Multi-scale · Partition · Finger vein

摘要: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.

第一作者:Zhongxia Zhang

论文类型:学术论文

通讯作者:Mingwen Wang

论文编号:20220411503087

学科门类:工学

一级学科:计算机科学与技术

页面范围:1091–1099

ISSN号:1863-1703

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发表时间:2022-01-22