Most of the current image processing methods used in the near-infrared imaging of fingervascular system concentrate on the extraction of internal structures (veins). In this paper, we proposea novel approach which allows to enhance both internal and external features of a finger. The methodis based on the Distance Transformation and allows for selective extraction of physiological structuresfrom an observed finger. We evaluate the impact of its parameters on the effectiveness of the alreadyestablished processing pipeline used for biometric identification. The new method was comparedwith 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 imagesobtained in a wide range of NIR wavelengths (730-950 nm). The results indicate a clear superiorityof the proposed approach over the remaining alternatives. The method managed to reach over 90%identification accuracy for all analyzed datasets.
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| http://dx.doi.org/10.3390/s20061644 | DOI Listing |
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