To overcome the performance degradation of finger-vein recognition, many previous studies have developed different enhancement methods for finger-vein images, some of which are compared with the proposed method in Table 1. Previous quality enhancement methods for finger-vein images can be classified into restoration-based and non-restoration-based methods [6].Table 1.Comparison of the proposed method and previous methods.For example, Yang et al. developed a restoration-based method that removes the optical blur from the camera lens and the skin scattering blur from the structure of the finger skin layers to transform a low-quality finger-vein image into a high-quality image [7]. They formulate the camera lens and skin scattering blurs by considering the optical characteristics of the skin layers using a Gaussian-based point spread function (PSF) model and a depth-PSF model.

Several restored images are obtained based on various skin surface depth parameters because it is not possible to correctly estimate the depth of the skin surface in the vein region. In addition, a linear superposition method is employed to conjoin the several restored images to produce a combined image. However, this method is limited because the processing time is increased by obtaining several restored images with various skin surface depth parameters. To eliminate the skin scattering blur in a finger-vein image, an optical model based on skin scattering and atmospheric scattering components has also been used for enhancing finger-vein images [8].

This approach is based on de-hazing and Drug_discovery the removal of skin scattering blur, which makes the vein patterns in a finger-vein image easier to distinguish. However, this method is limited because its performance can be affected by the detection of the scattering parameter. In addition, enhancement of the recognition accuracy was not discussed in this paper.Yang et al. proposed an enhancement method for finger-vein images based on scattering removal, Gabor filtering, and a multi-scale multiplication rule [9]. However, they assumed that the luminance of the surrounding environment would be constant during processing to facilitate scattering removal. In addition, the optimal parameters of the Gabor filter were designed in an elaborate manner based on the characteristics of the vein lines.

Therefore, the parameters need to be redesigned for vein images captured using different devices. By contrast, our proposed method uses a roughly designed Gabor filter, which has the advantage that its performance is not affected significantly by the different types of vein images (in this study, this was confirmed by tests using two finger-vein databases, which were collected with two different devices). In our method, performance enhancement is achieved using a combination of Gabor and Retinex filters based on a fuzzy system.