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

Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild