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Iterated graph cut method for automatic and accurate segmentation of finger-vein images

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Abstract

Recent advances in computer vision and machine intelligence have facilitated biometric technologies, which increasingly rely on image data in security practices. As an important biometric identifier, the near-infrared (NIR) finger-vein pattern is favoured by non-contact, high accuracy, and enhanced security systems. However, large stacks of low-contrast and complex finger-vein images present barriers to manual image segmentation, which locates the objects of interest. Although some headway in computer-aided segmentation has been made, state-of-the-art approaches often require user interaction or prior training, which are tedious, time-consuming and prone to operator bias. Recognizing this deficiency, the present study exploits structure-specific contextual clues and proposes an iterated graph cut (IGC) method for automatic and accurate segmentation of finger-vein images. To this end, the geometric structures of the image-acquisition system and the fingers provide the hard (centreline along the finger) and shape (rectangle around the finger) constraints. A node-merging scheme is applied to reduce the computational burden. The Gaussian probability model determines the initial labels. Finally, the maximum a posteriori Markov random field (MAP-MRF) framework is tasked with iteratively updating the data models of the object and the background. Our approach was extensively evaluated on 4 finger-vein databases and compared with some benchmark methods. The experimental results indicate that the proposed IGC method outperforms the state-of-the-practice approaches in finger-vein image segmentation. Specifically, the IGC method, relative to its level set deep learning (LSDL) counterpart, can increase the average F-measure value by 5.03%, 6.56%, 49.91%, and 22.89% when segmenting images from four different finger-vein databases. Therefore, this work can provide a feasible path towards fully automatic image segmentation.

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Authors and Affiliations

  1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Lei Lei, Feng Xi, Shengyao Chen & Zhong Liu

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  1. Lei Lei
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  2. Feng Xi
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  3. Shengyao Chen
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  4. Zhong Liu
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Correspondence to Lei Lei.

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Lei, L., Xi, F., Chen, S. et al. Iterated graph cut method for automatic and accurate segmentation of finger-vein images. Appl Intell 51, 673–689 (2021). https://doi.org/10.1007/s10489-020-01828-8

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