A local feature-based facial expression recognition system from depth video - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 340

FIGURE 1 Basic steps involved in the proposed facial expression recognition system.
Figure 2(a) represents a depth image from a surprise expression. It can be noticed that in the
depth image, the higher pixel value represents the near (e.g., nose) and the lower (e.g., eyes)
the far distance. The pseudo-color image corresponding to the depth image in Figure 2(b) also
indicates the significant differences among different face portions where the color intensities.
Figure 3(a)-(c) shows five generalized depth faces from a happy, surprise, and disgust expres-
sions, respectively.
FIGURE 2 (a) A depth image and (b) corresponding pseudo color image of a surprise image.
FIGURE 3 A sequential depth facial expression images of (a) happy, (b) surprise, and (c) dis-
gust.
3 Feature extraction
The feature extraction of the proposed approach consists of three fundamental stages: (1) LDP
is performed first on the depth faces of the facial expression videos, (2) PCA is applied on the
LDP features for dimensionality reduction, and (3) LDA is then applied to compress the same
facial expression images as close as possible and to separate the different expression class im-
ages as far as possible.