  Automatic facial expression classification (FER) from videos is a critical
problem for the development of intelligent human-computer interaction systems.
Still, it is a challenging problem that involves capturing high-dimensional
spatio-temporal patterns describing the variation of one's appearance over
time. Such representation undergoes great variability of the facial morphology
and environmental factors as well as head pose variations. In this paper, we
use Conditional Random Forests to capture low-level expression transition
patterns. More specifically, heterogeneous derivative features (e.g. feature
point movements or texture variations) are evaluated upon pairs of images. When
testing on a video frame, pairs are created between this current frame and
previous ones and predictions for each previous frame are used to draw trees
from Pairwise Conditional Random Forests (PCRF) whose pairwise outputs are
averaged over time to produce robust estimates. Moreover, PCRF collections can
also be conditioned on head pose estimation for multi-view dynamic FER. As
such, our approach appears as a natural extension of Random Forests for
learning spatio-temporal patterns, potentially from multiple viewpoints.
Experiments on popular datasets show that our method leads to significant
improvements over standard Random Forests as well as state-of-the-art
approaches on several scenarios, including a novel multi-view video corpus
generated from a publicly available database.
