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This paper presents a novel CNN-RNN based approach, which exploits multipleCNN features for dimensional emotion recognition in-the-wild, utilizing theOne-Minute Gradual-Emotion (OMG-Emotion) dataset. Our approach includes firstpre-training with the relevant and large in size, Aff-Wild and Aff-Wild2emotion databases. Low-, mid- and high-level features are extracted from thetrained CNN component and are exploited by RNN subnets in a multi-taskframework. Their outputs constitute an intermediate level prediction; finalestimates are obtained as the mean or median values of these predictions.Fusion of the networks is also examined for boosting the obtained performance,at Decision-, or at Model-level; in the latter case a RNN was used for thefusion. Our approach, although using only the visual modality, outperformedstate-of-the-art methods that utilized audio and visual modalities. Some of ourdevelopments have been submitted to the OMG-Emotion Challenge, ranking secondamong the technologies which used only visual information for valenceestimation; ranking third overall. Through extensive experimentation, wefurther show that arousal estimation is greatly improved when low-levelfeatures are combined with high-level ones.

Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Dataset