Image Processing Reference

In-Depth Information

number of eigenvectors are chosen according to the highest eigenvalues for projection of LDP

features. The PCA feature space projections
V
of LDP features can be represented as

(6)

3.3 LDA on PCA Features

To obtain more robust features, LDA is performed on the PCA feature vectors
F
. Basically,

LDA is based on class specific information which maximizes the ratio of the within,
Q
w
and

between,
Q
b
scater matrix. The optimal discrimination matrix
W
LDA
is chosen from the max-

imization of ratio of the determinant of the between and within class scater matrix as

(7)

where
W
LDA
is the discriminant feature space. Thus, the LDP-LDA feature vectors of facial ex-

pression images can be obtained as follows:

(8)

Figure 7
shows an exemplar plot of 3D LDA representation of the LDP-PCA features of all

the facial expression depth images that shows a good separation among the representation of

the depth faces of different classes.

FIGURE 7
3D plot of LDP-PCA-LDA features of depth faces from six expressions.

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