  Emotion recognition plays an important role in human-computer interaction
(HCI) and has been extensively studied for decades. Although tremendous
improvements have been achieved for posed expressions, recognizing human
emotions in "close-to-real-world" environments remains a challenge. In this
paper, we proposed two strategies to fuse information extracted from different
modalities, i.e., audio and visual. Specifically, we utilized LBP-TOP, an
ensemble of CNNs, and a bi-directional LSTM (BLSTM) to extract features from
the visual channel, and the OpenSmile toolkit to extract features from the
audio channel. Two kinds of fusion methods, i,e., feature-level fusion and
model-level fusion, were developed to utilize the information extracted from
the two channels. Experimental results on the EmotiW2018 AFEW dataset have
shown that the proposed fusion methods outperform the baseline methods
significantly and achieve better or at least comparable performance compared
with the state-of-the-art methods, where the model-level fusion performs better
when one of the channels totally fails.
