  In this paper, we describe our approach for the OMG- Emotion Challenge 2018.
The goal is to produce utterance-level valence and arousal estimations for
videos of approximately 1 minute length. We tackle this problem by first
extracting facial expressions features of videos as time series data, and then
using Recurrent Neural Networks of the Echo State Network type to model the
correspondence between the time series data and valence-arousal values.
Experimentally we show that the proposed approach surpasses the baseline
methods provided by the organizers.
