A local feature-based facial expression recognition system from depth video - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 343

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.