10-06-2013, 12:53 PM
A Filterbank-based Representation for Classification and Matching of Fingerprints
A Filterbank.zip (Size: 396.35 KB / Downloads: 13)
INTRODUCTION
Up to now, when a new fingerprint image is added to database, the FingerCode was calculated two times: one time for input image and a second time for the image rotated of a proper angle (22.5/2 degrees) in order to make the process rotation-invariant (see the cited reference for more details). The image was rotated using the Matlab function imrotate. This procedure can introduce noise. To avoid this behavior we calculated the FingerCode associated to the rotated image in this way: we rotate sectorization and the orientation of Gabor filters of filter-bank of the same angle (22.5/2 degree). This is equivalent to consider as filter-bank input a rotated image.
When a new fingerprint image is added to database, only one core point is found. On the other side, when an input image is selected for fingerprint matching, a list of candidates for core point is found and the matching is performed for each of them. At last only the candidate with the smallest distance is considered. For example , in database I have 3 images Img1, Img2 and Img3. Each of them is characterized only by one core point, so I will have 3 core points, each of them associated to an image present in database. If I select an image for fingerprint matching (let it be ImgNew) I found for it a certain number of core point (let it N). For each of these N core points (candidates) I will find the nearest fingerprint image present in database. At last I will obtain N distances (as the number of core points candidates): I say that the recognized image is the image with the nearest distance I have obtained (this distance is associated to one of the initial N core points candidates of ImgNew).