Master Thesis - Department of Computer Science

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