Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition

13 Sep 2018  ·  Zheng Lian, Ya Li, Jian-Hua Tao, Jian Huang ·

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.

PDF Abstract


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here