Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
Error<br />
d1 = (1 − TZeroF RR) d2 = |IDT ZeroF RR<br />
i |<br />
Gi ∝ d1<br />
FAR(t)<br />
d2<br />
ZeroFRR<br />
TZeroF RR<br />
EER<br />
d1<br />
Li ∝ d2<br />
ZeroFAR<br />
b ID<br />
FRR(t)<br />
Figure 3.8: ZeroFRR and TZeroF RR are illustrated using hypothetical curves <strong>of</strong> FAR(t)<br />
and FRR(t).<br />
Hence, goatishness and lambishness can be expressed as,<br />
Gi = exp (1 − TZeroF RR) ,<br />
� TZeroF RR |IDi |<br />
Li = exp<br />
bID �<br />
. (3.8)<br />
where IDi is the set <strong>of</strong> impostor scores for i th subject and |.| denotes the<br />
cardinality <strong>of</strong> a set. The significance <strong>of</strong> Gi and Li are explained using a diagram<br />
in Figure 3.8.<br />
3.3.2 Subject-Specific Subband Selection Algorithm<br />
The above defined criteria are used in a subject-specific subband selection algorithm.<br />
As described in Section 3.2.2, each (l, k) pair corresponds to a subband face (Al −Ak)<br />
which is obtained by reconstructing from subbands upto the level-l, after suppressing<br />
the approximation at level-k, where 0 ≤ l < k ≤ log N, N ∗ N being the resolution<br />
<strong>of</strong> the image. Given (l, k), we transform (DWT) all images from the training and<br />
validation sets to the subband face, (Al − Ak). Then we use any subspace method<br />
for feature extraction and generate a confusion matrix (see Eqn. 3.1) using nearest-<br />
neighbor policy followed by a normalization technique to convert score values in the<br />
range [0, 1]. From the confusion matrix the genuine and impostor score sets are<br />
57<br />
1<br />
t