Name
Abstract | ||
|---|---|---|
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 train linear SVM classifiers for these kinds of features on the AFEW6.0 and HAPPEI dataset, and use a fusion network we proposed to combine all the extracted features at the decision level. The final achievements we have gained are 51.54% accuracy on the AFEW testing set and 0.836 RMSE on the HAPPEI testing set. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2993148.2997640 | ICMI |
Keywords | Field | DocType |
Emotion Recognition, Multimodal Features, LSTM, Group Emotion | Scale-invariant feature transform,Decision level,Emotion recognition,Computer science,Speech recognition,Group emotion,Linear svm | Conference |
Citations | PageRank | References |
17 | 0.62 | 25 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Sun | 1 | 104 | 21.35 |
| Qinglan Wei | 2 | 27 | 2.44 |
| Liandong Li | 3 | 77 | 5.02 |
| Qihua Xu | 4 | 23 | 2.04 |
| Jun He | 5 | 71 | 11.24 |
| Lejun Yu | 6 | 36 | 3.28 |