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

for facial animation, Lengagne et al. [120] and Fua [121] use bundle-adjustment and
least-squares fitting to fit a complex animation model to uncalibrated videos. This
algorithm makes use of stereo data, silhouette edges, and 2D feature points. Five
manually-selected features points and initial values of camera positions are essential
for the convergence of this method.
Ahlberg [122] adapts a 3D wireframe model
(CANDIDE-3 [123]) to a 2D video image. The two modeling methods proposed in
this thesis follow the modeling approach using a generic face model; both of our methods
make use of a generic face model (Waters’ face model) as a priori knowledge of
facial geometry and employ (i) displacement propagation and 2.5D snakes in the first
method and (ii) interacting snakes and semantic face graphs in the second method
for adapting recognition-orientated features to an individual’s geometry.
2.3.2 Snakes for Face Alignment
As a computational bridge between the high-level a priori knowledge of object shape
and the low-level image data, snakes (or active contours) are useful models for extracting
the shape of deformable objects. Similar to other template-based approaches such
as Hough transform and active shape models, active contours have been employed
to detect object boundary, track objects, reconstruct 3D objects (stereo snakes and
inter-frame snakes), and match/identify shape. Snakes self converge in an iterative
way, and deform either with or without topological constraints.
Research on active contours focuses on issues related to representation (e.g., parametric
curves, splines, Fourier series, and implicit level-set functions), energy func-
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