  This paper addresses the problem of automatic emotion recognition in the
scope of the One-Minute Gradual-Emotional Behavior challenge (OMG-Emotion
challenge). The underlying objective of the challenge is the automatic
estimation of emotion expressions in the two-dimensional emotion representation
space (i.e., arousal and valence). The adopted methodology is a weighted
ensemble of several models from both video and text modalities. For video-based
recognition, two different types of visual cues (i.e., face and facial
landmarks) were considered to feed a multi-input deep neural network. Regarding
the text modality, a sequential model based on a simple recurrent architecture
was implemented. In addition, we also introduce a model based on high-level
features in order to embed domain knowledge in the learning process.
Experimental results on the OMG-Emotion validation set demonstrate the
effectiveness of the implemented ensemble model as it clearly outperforms the
current baseline methods.
