Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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(a) (b)<br />
Figure A.10: (a) and (b) shows the enhanced fingerprint images after masking.<br />
segmentation. Firstly, the image is divided into blocks and the gray-level variance is<br />
calculated for each block in the image. If the variance is less than the global threshold,<br />
then the block is assigned to be a background region (i.e. assign R(i, j) = 0 for the<br />
block centered at (i, j)); otherwise, it is assigned to be the part <strong>of</strong> the foreground (i.e.<br />
assign R(i, j) = 1), where R is called the mask. The gray-level variance for a block<br />
<strong>of</strong> size W × W is defined as:<br />
V (k) = 1<br />
W 2<br />
W� −1<br />
i=0<br />
W� −1<br />
j=0<br />
(E(i, j) − M(k)) 2 . (A.29)<br />
where M(k) is the mean gray-level value for block k and E(i, j) is the enhanced<br />
image. Then, the enhanced image after masking can be represented by,<br />
⎧<br />
⎫<br />
⎪⎨<br />
⎪⎬<br />
E(i, j) if R(i, j) = 1<br />
H(i, j) =<br />
(A.30)<br />
⎪⎩<br />
⎪⎭<br />
255 Otherwise<br />
Fig. A.9 and Fig. A.10 shows the masks and enhanced images after masking for input<br />
fingerprints, respectively.<br />
A.3 Image Binarization<br />
Binarization is the process <strong>of</strong> converting a gray-level image into a binary image where<br />
the black pixels represent ridges and white pixels represent valleys. This process<br />
improve the contrast between the ridges and valleys and consequently facilitates the<br />
extraction <strong>of</strong> minutiae.<br />
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