Master Thesis - Department of Computer Science

(a) (b)
Figure A.7: Enhanced images of two input fingerprints shown in Fig. A.2.
a trade-off. The larger the values, the more robust to noise the filters are, but the
more likely the filter will create spurious ridges and valleys. On the other hand, the
smaller the values of δx and δy, the less likely the filters will create spurious ridges
and valleys, and they will be less effective in removing noise. The enhanced image E
is expressed as follows,
E(i, j) =
Wg/2
�
Wg/2
�
u=−Wg/2 v=−Wg/2
h (u, v : O(i, j), F (i, j)) G(i − u, j − v). (A.28)
where Wg = 11 specifies the size of the Gabor filters. Fig. A.8 shows a number
of 16 × 16 local blocks and corresponding Gabor filters tuned to the frequency and
orientation of the corresponding blocks. Fig. A.7 demonstrates the enhanced images
of the input fingerprints shown in Fig. A.2.
A.2 Image Segmentation
Segmentation is the process of separating the foreground regions from the background
regions. The background corresponds to the regions containing no valid fingerprint
information. When minutiae extraction algorithms are applied to the background
regions of an image, it results in the extraction of noisy and false minutiae. Thus,
segmentation is employed to discard these background regions to facilitate reliable
minutiae extraction. In a fingerprint, the background regions generally exhibit a
low gray-scale variance, whereas the foreground regions have a very high variance.
Hence, a method based on variance thresholding [87] can be used to perform the
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