Open AccessFinger Vein Image ROI Extraction Based on Active Contour Method
Author(s) -
Yifan Wang,
Huimin Lu,
Ruoran Gao,
Guizeng Wang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2171/1/012070
Subject(s) - artificial intelligence , computer science , computer vision , region of interest , segmentation , matching (statistics) , image segmentation , enhanced data rates for gsm evolution , pattern recognition (psychology) , mathematics , statistics
Under the background of the novel coronavirus pneumonia outbreak in the world, unrestricted and contactless finger vein collection devices have significantly improved public health safety. However, due to the unfixed position of the finger and the open or semi-open characteristics of the acquisition device, it is inevitable to introduce plenty of factors that affect the recognition performance, such as low contrast, uneven illumination and edge disappearance. In view of these practical problems, we propose a method for ROI extraction of finger vein images that combines active contour method and morphological post-processing operations. This method starts from the local segmentation, and finally completes the acquisition of finger masks at the global level, and then combines some morphological operations to achieve precise extraction of finger masks. We designed and conducted plenty of comparison experiments on the proposed algorithm and the current mainstream finger vein image ROI extraction methods on three public available finger vein datasets. Experimental results show that our method accurately extracts the complete finger region mask and achieves the best matching accuracy on all datasets.