  Automatic emotion recognition is a challenging task. In this paper, we
present our effort for the audio-video based sub-challenge of the Emotion
Recognition in the Wild (EmotiW) 2018 challenge, which requires participants to
assign a single emotion label to the video clip from the six universal emotions
(Anger, Disgust, Fear, Happiness, Sad and Surprise) and Neutral. The proposed
multimodal emotion recognition system takes audio, video and text information
into account. Except for handcraft features, we also extract bottleneck
features from deep neutral networks (DNNs) via transfer learning. Both temporal
classifiers and non-temporal classifiers are evaluated to obtain the best
unimodal emotion classification result. Then possibilities are extracted and
passed into the Beam Search Fusion (BS-Fusion). We test our method in the
EmotiW 2018 challenge and we gain promising results. Compared with the baseline
system, there is a significant improvement. We achieve 60.34% accuracy on the
testing dataset, which is only 1.5% lower than the winner. It shows that our
method is very competitive.
