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

(a)
Figure 2.5. Internal representations of the LFA-based approach (from Penev and
Atick [34]). (a) An average face image is marked with five localized features; (b) five
topographic kernels associated with the five localized features are shown in the top
row, and the corresponding residual correlations are shown in the bottom row.
(b)
The representative set can be obtained from either digital cameras or extracted from
videos. On the other hand, in pose-invariant approaches, a face is represented by a
3D face model. The 3D face shape of an individual is explicitly represented, while
the 2D images are implicitly encoded in this face model. The 3D face models can be
constructed by using either 3D digitizers or range sensors, or by modifying a generic
face model using a video sequence or still face images of frontal and profile views.
The pose-dependent algorithms can be further divided into three classes:
appearance-based (holistic) [29], [78] feature-based (analytic) [102], [103] and hybrid
(which combines holistic and analytic methods) [60], [99], [32], [34] approaches.
The appearance-based methods are sensitive to intra-subject variations, especially
to changes in hairstyle, because they are based on global information in an image.
However, the feature-based methods suffer from the difficulty of detecting local fiducial
“points”. The hybrid approaches were proposed to accommodate both global and
local face shape information. For example, LFA-based methods, eigen-template meth-
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