A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild
This work addresses emotion recognition from videos for applications in human-computer interaction, but it is incremental as it adapts an existing architecture to a specific challenge.
The paper tackled dimensional emotion recognition in-the-wild by developing a multi-component CNN-RNN architecture, achieving best performance on the OMG-Emotion visual validation dataset for estimating valence and arousal values.
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.