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Temporal context is key to the recognition of expressions of emotion.Existing methods, that rely on recurrent or self-attention models to enforcetemporal consistency, work on the feature level, ignoring the task-specifictemporal dependencies, and fail to model context uncertainty. To alleviatethese issues, we build upon the framework of Neural Processes to propose amethod for apparent emotion recognition with three key novel components: (a)probabilistic contextual representation with a global latent variable model;(b) temporal context modelling using task-specific predictions in addition tofeatures; and (c) smart temporal context selection. We validate our approach onfour databases, two for Valence and Arousal estimation (SEWA and AffWild2), andtwo for Action Unit intensity estimation (DISFA and BP4D). Results show aconsistent improvement over a series of strong baselines as well as overstate-of-the-art methods.

Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition