Face Detection and Modeling for Recognition - Biometrics Research ...

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Figure 5.2. 3D generic face model: (a) Waters’ triangular-mesh model shown in the
side view; (b) model in (a) overlaid with facial curves including hair and ears at a
side view; (c) model in (b) shown in the frontal view.
for facial component i with a close boundary such as eyes and mouth, and with endvertex
padding for those having open boundary such as ears and hair components.
The advantage of using semantic graph descriptors for face matching is that these descriptors
can seamlessly encode geometric relationships (scaling, rotation, translation,
and shearing) among facial components in a compact format in the spatial frequency
domain, because the vertices of all the facial components are specified in the same
coordinate system with the origin around the nose (see Fig. 5.2). The reconstruction
of semantic face graphs from semantic graph descriptors is obtained by
ũ i (n) = F −1 {a i (k)} =
L∑
i −1
k=0
a i (k) · e j2πkn/N i
, (5.2)
where L i (< N i ) is the number of frequency components used for the i th face component.
Figure 5.3 shows the reconstructed semantic face graphs at different levels
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