  This paper presents our approach to the One-Minute Gradual-Emotion
Recognition (OMG-Emotion) Challenge, focusing on dimensional emotion
recognition through visual analysis of the provided emotion videos. The
approach is based on a Convolutional and Recurrent (CNN-RNN) deep neural
architecture we have developed for the relevant large AffWild Emotion Database.
We extended and adapted this architecture, by letting a combination of multiple
features generated in the CNN component be explored by RNN subnets. Our target
has been to obtain best performance on the OMG-Emotion visual validation data
set, while learning the respective visual training data set. Extended
experimentation has led to best architectures for the estimation of the values
of the valence and arousal emotion dimensions over these data sets.
