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Automatic facial expression classification (FER) from videos is a criticalproblem for the development of intelligent human-computer interaction systems.Still, it is a challenging problem that involves capturing high-dimensionalspatio-temporal patterns describing the variation of one's appearance overtime. Such representation undergoes great variability of the facial morphologyand environmental factors as well as head pose variations. In this paper, weuse Conditional Random Forests to capture low-level expression transitionpatterns. More specifically, heterogeneous derivative features (e.g. featurepoint movements or texture variations) are evaluated upon pairs of images. Whentesting on a video frame, pairs are created between this current frame andprevious ones and predictions for each previous frame are used to draw treesfrom Pairwise Conditional Random Forests (PCRF) whose pairwise outputs areaveraged over time to produce robust estimates. Moreover, PCRF collections canalso be conditioned on head pose estimation for multi-view dynamic FER. Assuch, our approach appears as a natural extension of Random Forests forlearning spatio-temporal patterns, potentially from multiple viewpoints.Experiments on popular datasets show that our method leads to significantimprovements over standard Random Forests as well as state-of-the-artapproaches on several scenarios, including a novel multi-view video corpusgenerated from a publicly available database.

Dynamic Pose-Robust Facial Expression Recognition by Multi-View Pairwise Conditional Random Forests