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.14: Thinned images <strong>of</strong> two input fingerprints shown in Fig. A.2.<br />
ditions (c’) and (d’) are used to remove north-west boundary points and south-east<br />
corner points. The iteration process is repeated until no more pixels can be removed.<br />
The skeleton images <strong>of</strong> the input fingerprint images are shown in Fig. A.14.<br />
A.5 Minutiae Extraction<br />
Once a binary skeleton has been obtained, a simple image scan allows the pixel<br />
corresponding to minutiae to be detected. This stage consists <strong>of</strong> two stages [100],<br />
• Detection <strong>of</strong> ridge endings and bifurcations from thinned images, by using<br />
neighborhood constraints and<br />
• Post-processing to remove undesired features.<br />
For minutiae detection the concept <strong>of</strong> Crossing number is used. Pixels corre-<br />
sponding to minutiae are characterized by a crossing number different from 2. The<br />
crossing number [6] cn(p) <strong>of</strong> a pixel p in a binary image is defined as half the sum <strong>of</strong><br />
the differences between pairs <strong>of</strong> adjacent pixels in the 8-neighborhood <strong>of</strong> p:<br />
cn(p) = 1<br />
2<br />
�<br />
i=1,..,8<br />
|val(pi mod 8) − val(pi−1)|, (A.34)<br />
where p0, p1, ..., p7 are the pixels belonging to an ordered sequence <strong>of</strong> pixels defin-<br />
ing the 8-neighborhood <strong>of</strong> p and val(p) ∈ {0, 1} is the pixel value. It is simple to note<br />
(Fig. A.15) that a pixel p with val(p) = 1:<br />
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