LSTM for dynamic emotion and group emotion recognition in the wild

In this paper, we describe our work in the fourth Emotion Recognition in the Wild (EmotiW 2016) Challenge. For video based emotion recognition sub-challenge, we extract acoustic features, LBPTOP, Dense SIFT and CNN-LSTM features to recognize the emotions of film characters. For group level emotion recognition sub-challenge, we use LSTM and GEM model. We… (More)