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

images with registered range data) captured in the frontal view; the other takes only
color images as its facial measurements. Both modeling methods adapt facial features
of a generic model to those extracted from an individual’s facial measurements in a
global-to-local fashion. The first method aligns the model globally, uses the 2.5D
active contours to refine feature boundaries, and propagates displacements of model
vertices iteratively to smooth non-feature areas. The resulting face model is visually
similar to the true face. The resulting 3D model has been shown to be quite useful for
recognizing non-frontal views based on an appearance-based recognition algorithm.
The second modeling method aligns semantic facial components, e.g., eyes, mouth,
nose, and the face outline, of the generic semantic face graph onto those in a color face
image. The nodes of a semantic face graph, derived from a generic 3D face model,
represent high-level facial components, and are connected by triangular meshes. The
semantic face graph is first coarsely aligned to the locations of detected face and facial
components, and then finely adapted to the face image using interacting snakes,
each of which describes a semantic component.
A successful interaction of these
multiple snakes results in appropriate component weights based on distinctiveness
and visibility of individual components. Aligned facial components are transformed
to a feature space spanned by Fourier descriptors for semantic face matching. The
semantic face graph allows face matching based on selected facial components, and
updating of a 3D face model based on 2D images.
The results of face matching
demonstrate the classification and visualization (e.g., the generation of cartoon faces
and facial caricatures) of human faces using the derived semantic face graphs.
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