V-Vibe: A robust ROI extraction method based on background subtraction for vein images collected by infrared device

Abstract

In recent years, the global epidemic has put forward higher requirements for public health security, and the non-contact finger vein acquisition and identification technology based on infrared imaging has become a research hotspot due to its high security, accuracy and convenience. However, infrared images generally have the problems of low resolution, low contrast and low signal-to-noise ratio, which seriously affect the effective extraction and utilization of image feature information. Moreover, most of the images captured by unconstrained vein acquisition devices have a large number of complex backgrounds, which are often similar to the pixel distribution of finger region, which greatly affects the recognition performance of vein images. To solve the above problems, we carried out the following exploratory work. First, we designed and developed a new finger vein infrared acquisition device. The device adopts a kind of physical structure of triangular spotlight, and in combination with our proposed V-Vibe method can achieve complete Region of Interest (ROI) extraction of finger vein images. Second, we created a new finger vein dataset (named FV-BS) for background subtraction. The dataset contains 420 individuals, 4,200 finger vein image samples and 420 device background images, as well as a subset of vein image data filled with complex backgrounds. Third, we proposed a new method named V-Vibe to extract ROI from vein images by using background subtraction technology. Subtraction can effectively eliminate factors that affect recognition performance in both vein images and background sample set, thus greatly reducing the influence of complex backgrounds and low-quality infrared images on identification. To verify the validity and accuracy of the V-Vibe method, we designed and conducted a large number of experiments on several datasets, including four publicly available datasets, as well as self-built FV-BS, and analyzed the results in detail. Compared with other methods, the ROI extraction effect of V-Vibe method proposed in this paper is significantly improved, and EER is reduced by 1.18% and 2.37% on average respectively in maximum curvature and repeated line tracking feature extraction methods, which is an effective method for ROI extraction of finger vein images. Furthermore, we also tested the proposed V-Vibe method to extract ROI from the vein images of palm and back of hands. Experimental results show that our method has excellent segmentation accuracy on both palm and dorsal hand vein datasets, with the average F1 Score reaching 95.1%.