Master Thesis - Department of Computer Science

Figure 2.7: Examples of minutiae; A minutia can be characterized by position and
orientation.
Then the Poincare index PG,C is defined as the total rotation of the vectors of G
along C. An interesting implementation of Poincare method for locating singular
points was proposed by Bazen and Gerez [11]. According to Green’s Theorem, closed
line integral over a vector field can be calculated as a surface integral over the rotation
of this vector field. In practice, instead of summing angle differences along a closed
path, they compute the “rotation” of the orientation image and then perform a local
integration over a small neighborhood of each element. Based on the observation
that only a limited number of singularities can be present in a fingerprint, Karu and
Jain [57] proposed to iteratively smooth the orientation image until a valid number
of singularities is detected by Poincare index. Srinivasan and Murthy [117] extract
singularities according to the local histogram of the orientation image. Koo and Kot
[63] used multi-resolution approach to determine the singularities with single pixel
accuracy.
Most automatic systems for fingerprint comparison are based on minutiae match-
ing. Hence, reliable minutiae extraction is an extremely important task and lot of
search has been devoted to this topic. An example of minutiae is given in Fig 2.7.
In some approaches minutiae are extracted on a thinned image which is obtained af-
ter binarization process. Some others have proposed minutiae extraction approaches
that work directly on the gray-scale images without binarization and thinning. After
obtaining binary skeleton, a simple image scan allows the pixel corresponding to minu-
tiae to be selected. Pixels corresponding to minutiae are characterized by a crossing
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