Master Thesis - Department of Computer Science

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