Master Thesis - Department of Computer Science

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