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

for topographic facial features (e.g., eyebrows, cheek, mouth, etc.)
Mean MEF1 MEF2 MEF3 MEF4 MEF5 MEF6 MEF7 MEF8
(a)
Mean MDF1 MDF2 MDF3 MDF4 MDF5 MDF6 MDF7 MDF8
(b)
Figure 2.3. Internal representations of the PCA-based approach and the LDA-based
approach (from Weng and Swets [31]). The average (mean) images are shown in the
first column. Most Expressive Features (MEF) and Most Discriminating Features
(MDF) are shown in (a) and (b), respectively.
The PCA-based algorithm provides a compact but non-local representation of
face images.
Based on the appearance of an image at a specific view, the PCA
algorithm works at the pixel level. Hence, the algorithm can be regarded as “picture”
recognition, in other words, it is not explicitly using any facial features. The EBGMbased
algorithm constructs local features (extracted using Gabor wavelets) and global
face shape (represented as a graph), and so this approach is much closer to “face”
recognition. However, the EBGM algorithm is pose-dependent, and it requires initial
graphs for different poses during its training stage.
The LFA-based algorithm is
derived from the PCA-based method; it is also called a kernel PCA method. In this
approach, however, the choice of kernel functions for local facial features (e.g., eyes,
mouth, and nose) and the selection of locations of these features still remains an open
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