Fingerprint Image Enhancement Based on Second Directional Derivative of the Digital Image
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Fingerprint Image Enhancement Based on Second Directional Derivative of the Digital Image Marius Tico Nokia Research Center, P.O. Box 100, FIN-33721 Tampere, Finland Email: [email protected]
Vesa Onnia Institute of Digital and Computer Systems, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland Email: [email protected]
Pauli Kuosmanen Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FIN-33101 Tampere, Finland Email: [email protected] Received 16 October 2001 and in revised form 10 April 2002 This paper presents a novel approach of fingerprint image enhancement that relies on detecting the fingerprint ridges as image regions where the second directional derivative of the digital image is positive. A facet model is used in order to approximate the derivatives at each image pixel based on the intensity values of pixels located in a certain neighborhood. We note that the size of this neighborhood has a critical role in achieving accurate enhancement results. Using neighborhoods of various sizes, the proposed algorithm determines several candidate binary representations of the input fingerprint pattern. Subsequently, an output binary ridge-map image is created by selecting image zones, from the available binary image candidates, according to a MAP selection rule. Two public domain collections of fingerprint images are used in order to objectively assess the performance of the proposed fingerprint image enhancement approach. Keywords and phrases: fingerprints, image enhancement, image derivative, facet model, ridge pattern.
1. INTRODUCTION Fingerprints are graphical ridge patterns present on human fingers, which, due to their uniqueness and permanence, are among the most reliable human characteristics that can be used for people identification [1, 2]. A common hypothesis, confirmed by the experience accumulated during a century of forensic use of fingerprints, is that certain local structures derived from the fingerprint ridges, called minutiae, are able to capture the invariant and discriminatory information present in the fingerprint image. Several factors like the presence of scars, variations of the pressure between the finger and acquisition sensor, worn artifacts, the environmental conditions during the acquisition process, and so forth, can dramatically affect the quality of the acquired fingerprint image. Since minutiae depend on fine details of the ridge pattern, their extraction can become notoriously difficult if the noise generated by the factors described above is not substantially reduced. The main goals of a fingerprint image enhancement algorithm are (i) to reduce the noise present in the image, and (ii) to detect
the fingerprint ridges. An input gray-scale fingerprint image is thereby transformed by the enhancement algorithm into a binary representation of the ridge pattern, called binary ridge-map image. Inspecting a fingerprint image we may note that the image pixels located on fingerprint ridges usually exhibit lower gray level intensities
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