Revisiting the EmotiW challenge: how wild is it really?

The focus of this work is emotion recognition in the wild based on a multitude of different audio, visual and meta features. For this, a method is proposed to optimize multi-modal fusion architectures based on evolutionary computing. Extensive uni- and multi-modal experiments show the discriminative power of each computed feature set and fusion architecture. Furthermore, we summarize the EmotiW 2013/2014 challenges and review the conclusions that have been drawn and compare our results with the state-of-the-art on this dataset.