Abstract
Most of the current image processing methods used in the near-infrared imaging of finger vascular system concentrate on the extraction of internal structures (veins). In this paper, we propose a novel approach which allows to enhance both internal and external features of a finger. The method is based on the Distance Transformation and allows for selective extraction of physiological structures from an observed finger. We evaluate the impact of its parameters on the effectiveness of the already established processing pipeline used for biometric identification. The new method was compared with five state-of-the-art approaches to features extraction (position-gray-profile-curve—PGPGC, maximum curvature points in image profiles—MC, Niblack image adaptive thresholding—NAT, repeated dark line tracking—RDLT, and wide line detector—WD) on the GustoDB database of images obtained in a wide range of NIR wavelengths (730–950 nm). The results indicate a clear superiority of the proposed approach over the remaining alternatives. The method managed to reach over identification accuracy for all analyzed datasets.
Highlights
One of the methods of person verification utilizes the near-infrared (NIR) images of the finger vascular system, which is proved to contain a set of features unique for each human
The possible answers can be classified into four categories: true positive (TP), true negative (TN), false positive (FP) and false negative (FN)
Our Modified Distance Transformation (MDT), employed before for image quality estimation, appeared to allow for selective enhancing of internal and external structures in the human finger which is crucial in the biometric system prior to the classification phase
Summary
One of the methods of person verification utilizes the near-infrared (NIR) images of the finger vascular system, which is proved to contain a set of features unique for each human. While the visible cannot penetrate inside the body showing only the skin, the NIR reveals and distinguishes internal structures from the external ones. This is because the light in a range of. 700–1000 nm is strongly absorbed by oxidized haemoglobin in veins [2] and lowly by the tissues [3]. This method of identification has been already described well in rich literature [4]
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