Ruggero Donida Labati, Vincenzo Piuri, IEEE, Fellow, Fabio Scotti, IEEE, Member
Department of development Technologies Universit` degli Studi di Milano a Milano, 20122, Italy. ruggero.donida@unimi.it, vincenzo.piuri@unimi.it, fabio.scotti@unimi.it
Abstractâ€"Contact-based sensors be the traditional devices used to captivate ?ngerprint images in commercial and homeland security applications. Contact-less systems achieve the ?ngerprint conquer by vision systems avoiding that exploiters touch any parts of the biometric device. Typically, the ?nger is laid in the working area of an optics system coupled with a CCD module. The captured light pattern on the ?nger is connect to the real ridges and valleys of the user ?ngertip, but the obtained images present important differences from the traditional ?ngerprint images. These differences are related to ninefold factors such as light, focus, blur, and the color of the skin. Unfortunately, the identity coincidence methods designed for ?ngerprint images captured with touch-based sensors do not obtain suf?cient accuracy when are directly applied to touch-less images.
Recent works show that multiple views analysis and 3D reconstruction can enhance the ?nal biometric accuracy of such systems. In this paper we propose a impertinent method for the identi?cation of the minutiae pairs between ii views of the same ?nger, an important footfall in the 3D reconstruction of the ?ngerprint template. The method is divisible in the sequent tasks: ?rst, an image preprocessing step is performed; second, a set of nominee minutiae pairs is selected in the two images, then a joust of prognosis pairs is created; last, a set of local features centered around the two minutiae is produced and processed by a classi?er based on a trained neural network. The output of the system is the list of the minutiae pairs present in the input images. Experiments show that the method is...If you lack to get a full essay, order it on our website: Orderessay
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