Iterated graph cut method for automatic and accurate segmentation of finger-vein images
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Iterated graph cut method for automatic and accurate segmentation of finger-vein images Lei Lei1
· Feng Xi1 · Shengyao Chen1 · Zhong Liu1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Finger-vein image · Segmentation · Graph cut · Fully automatic · Iterated
1 Introduction Recent advances in computer vision and machine intelligence have facilitated biometric technologies, which increasingly rely on image data in security practices. As Lei Lei
[email protected] Feng Xi [email protected] Shengyao Chen [email protected] Zhong Liu [email protected] 1
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
an important biometric identifier, finger-vein recognition 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, and thus pose a formidable challenge for subsequent pattern recognition. Therefore, the drive to introduce comp
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