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

commercial parametric face modeling system [17], FaceGen Modeller, which is based
on face shape statistics. It can efficiently create a character with specified age, gender,
race, and caricature morphing. Current trend in face recognition is to employ
3D face model explicitly
[67], because such a model provides a potential solution
to identifying faces with variations in illumination, 3D head pose, and facial expression.
These variations, called the intra-subject variations, also include changes due
to aging, facial hair, cosmetics, and facial accessories. These intra-subject variations
constitute the primary challenges in the field of face recognition. As object-centered
representations of human faces, 3D face models not only can augment recognition
systems that utilize viewer-centered face representations (based on 2D face images),
but also can blend together holistic approaches and geometry-based approaches for
recognition. However, the three state-of-the-art face recognition algorithms [68], (1)
the principal component analysis (PCA)-based algorithm; (2) the local feature analysis
(LFA)-based algorithm; and (3) the dynamic-link-architecture-based algorithm,
use only viewer-centered representations of human faces. A 3D model-based matching
algorithm is likely to provide a potential solution for advancing face recognition
technology.
However, for face recognition, it is more important to capture facial
distinctiveness of recognition-oriented components than to generate a realistic face
model.
We briefly introduce our face modeling methods for recognition (i.e., face
alignment) and model compression in the following subsections.
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