  Automatic emotion recognition (AER) is a challenging task due to the abstract
concept and multiple expressions of emotion. Although there is no consensus on
a definition, human emotional states usually can be apperceived by auditory and
visual systems. Inspired by this cognitive process in human beings, it's
natural to simultaneously utilize audio and visual information in AER. However,
most traditional fusion approaches only build a linear paradigm, such as
feature concatenation and multi-system fusion, which hardly captures complex
association between audio and video. In this paper, we introduce factorized
bilinear pooling (FBP) to deeply integrate the features of audio and video.
Specifically, the features are selected through the embedded attention
mechanism from respective modalities to obtain the emotion-related regions. The
whole pipeline can be completed in a neural network. Validated on the AFEW
database of the audio-video sub-challenge in EmotiW2018, the proposed approach
achieves an accuracy of 62.48%, outperforming the state-of-the-art result.
