So far we have seen the
experimental results of DAPM and here we will be seeing the experimental
results of DoG pore model. Also the experiment was conducted on a high
resolution partial fingerprint database using the proposed pore extraction and
pore matching methods and implemented in the MATLAB software's image processing
toolbox.
Pore Detection Accuracy
We first assess the pore detection accuracy of the Difference
of Gaussian. For this purpose we choose a set of 10 fingerprint images from the
database. Fig.1 shows the examples of pore extraction result of the two pore
extraction model. In addition to the visual evaluation of the pore detection
results, we calculated the pore detection accuracy on the 10 fingerprint images
by using the two metric: True Detection Rate (TDR) and False Detection Rate (FDR).
A
good pore extraction should have a high TDR and a low FDR simultaneously. Table
1 list the average detection accuracy of the two pore extraction considered as
a true pore whereas the one which lies outside the ridge i.e. on valley are
considered as false pores.
Table 1: Performance Measure of Difference of Gaussian
|
Images
|
FDR
|
Percentage
|
TDR
|
Percentage
|
|
Image_1
|
42/473
|
08.87 %
|
276/473
|
73.15 %
|
|
Image_2
|
41/389
|
10.53 %
|
338/389
|
86.88 %
|
|
Image_3
|
38/448
|
08.48 %
|
359/448
|
80.13 %
|
|
Image_4
|
29/422
|
06.87 %
|
341/422
|
80.80 %
|
|
Image_5
|
54/456
|
11.84 %
|
375/456
|
82.23 %
|
|
Image_6
|
35/412
|
08.49 %
|
345/412
|
83.73 %
|
|
Image_7
|
47/491
|
09.57 %
|
409/491
|
83.29 %
|
|
Image_8
|
54/374
|
14.43 %
|
276/374
|
74.60 %
|
|
Image_9
|
17/363
|
04.68 %
|
316/363
|
87.05 %
|
|
Image_10
|
26/320
|
05.50 %
|
280/320
|
87.50 %
|
The fig 2 below shows the results of the Difference of Gaussian algorithm on four different partial fingerprint images. The extracted pores using DoG algorithms is marked with red circles.
 |
|
Fig.2: Results of Difference
of Gaussian pore extraction; (a),(c),(e) and (g) are input images; (b), (d), (f) and (h) shows detected pores on input images.
|
Here
from the experimental results we can see that the true detection rates of the
two pore models are more than sufficient. Also the false detection rate is low.
The numbers of pores extracted from the two pore models are sufficient for
reliable matching of pores. As seen from the Fig1 the number of pore are more
than the average number of minutiae which testified our claim of higher feature
count than minutiae count including ridge ending and bifurcations. Thus the
proposed Dynamic Anisotropic Pore Model and Difference of Gaussian model has
yield the sufficient features i.e. pores which when used with direct pore
matching method will overcome the traditional fingerprint recognition technique
using minutiae features.
