Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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3.7 Area difference between genuine and impostor distribution with their<br />
ideal counterparts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55<br />
3.8 ZeroFRR and TZeroF RR are illustrated using hypothetical curves <strong>of</strong><br />
FAR(t) and FRR(t). . . . . . . . . . . . . . . . . . . . . . . . . . . . 57<br />
3.9 Training samples for three databases: first, second and third row shows<br />
the training set for Yale, PIE, and ORL databases, respectively. . . . 64<br />
4.1 A typical eigenvalue spectrum with its division into null space and<br />
range space. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75<br />
4.2 Stepwise feature subset selection . . . . . . . . . . . . . . . . . . . . . 80<br />
4.3 Architecture <strong>of</strong> our proposed method <strong>of</strong> feature fusion. . . . . . . . . 82<br />
4.4 The fusion architecture <strong>of</strong> the proposed method <strong>of</strong> decision fusion. . . 87<br />
4.5 Examples where LDA based on the criteria tr(S −1<br />
w Sb) fails to discrim-<br />
inate between classes. . . . . . . . . . . . . . . . . . . . . . . . . . . . 89<br />
4.6 Unique decomposition <strong>of</strong> faces into null space and range space <strong>of</strong> within-<br />
class scatter drawn from Yale (first row), ORL (second row) and PIE<br />
(third row) databases. . . . . . . . . . . . . . . . . . . . . . . . . . . 97<br />
4.7 Training samples for three databases: First, second and third row<br />
shows the training set for Yale, ORL, PIE databases, respectively. . 98<br />
5.1 Operation <strong>of</strong> proposed method for classifier fusion. . . . . . . . . . . . 108<br />
A.1 (a) and (b) A local window containing ridge valley structures; (c) and<br />
(d) Curve (i.e. x-signature) obtained along the direction normal to the<br />
ridge orientation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124<br />
A.2 Input fingerprint images. . . . . . . . . . . . . . . . . . . . . . . . . . 125<br />
A.3 Normalized images <strong>of</strong> two input fingerprints shown in Fig. A.2. . . . 125<br />
A.4 Orientation images <strong>of</strong> two input fingerprints shown in Fig. A.2. . . . . 127<br />
A.5 A X-signature with w = W and l = H in our case. . . . . . . . . . . . 127<br />
A.6 Frequency images (after filtering) <strong>of</strong> two input fingerprints shown in<br />
Fig. A.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130<br />
A.7 Enhanced images <strong>of</strong> two input fingerprints shown in Fig. A.2. . . . . 131<br />
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