ReactioNet: Learning High-order Facial Behavior from Universal Stimulus-Reaction by Dyadic Relation Reasoning.

Diverse visual stimuli can evoke various human affective states, which are usually manifested in an individual’s muscular actions and facial expressions. In lab-controlled emotion datasets, such a critical component (i.e., stimulus) was commonly designed in a limited way, making researchers incapable of generalizing the universal correlation and causation of stimulus-reaction as well as predicting possible emotions from context, timing, and relation. In this paper, we collected a large-scale spontaneous facial behavior database ReactioNet, which contains 1.1 million coupled stimulus-reaction tuples (visual/audio/caption from both stimuli and subjects). We introduce a new facial behavior detection scenario, Dyadic Relation Reasoning (DRR), which aims to detect facial actions by reasoning their relations with stimuli. By aggregating the dyadic information, our method essentially forms a relation prototype Universal Stimulus Reaction (U-SR), which encodes the low-order and high-order relationships between stimulus agents and facial reactions. A framework with both non-graph and graph modules is further developed to evaluate DRR-based facial action unit detection, facial expression recognition, and scene classification. Specifically, to learn "what" arouses a facial reaction, the non-graph module associates and projects the fine-grained stimulus-reaction features into common subspaces using cross-domain contrastive learning. To learn "how" stimulus-reaction pairs are mutually related, the graph module adopts Graph Convolution Network to represent, converge, and infer the dyadic U-SR relation under two relation assumptions (i.e., homophily and heterophily [68]). Extensive experiments demonstrate the effectiveness of the proposed work. The new dataset will be available for the research community.