Abstract
The interest in automatic emotion recognition and the larger field of Affective Computing has recently gained momentum. The current emergence of large, video-based affect datasets offering rich multi-modal inputs facilitates the development of deep learning-based models for automatic affect analysis However, recent approaches to process these modalities cannot fully exploit them due to the use of oversimplified fusion schemes. Furthermore, the efficient use of temporal information inherent to these huge data are largely unexplored hindering their potential progress. In this work, we propose a multi-modal, sequence-based neural network with gating mechanisms for affect recognition. Our model consists of three major networks: Firstly, a latent-feature generator that extracts compact representations from both modalities. Secondly, a multi-task discriminator that estimates both input identity and quadrant estimation. Thirdly, a sequence-based predictor with attention and gating mechanisms that effectively merges both modalities and uses this information through sequence modelling. Using SEMAINE and SEWA affect datasets, we observe the impact of both proposed methods with progressive increase in accuracy. We further show in our ablation studies how the internal attention weight and gating coefficient impact our models' estimates quality. Finally, we demonstrate state of the art accuracy through comparisons with current alternatives on both datasets.
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