Name
Abstract | ||
|---|---|---|
This paper addresses the problem of automatic emotion recognition in the scope of the One-Minute Gradual-Emotional Behavior challenge (OMG-Emotion challenge). The underlying objective of the challenge is the automatic estimation of emotion expressions in the two-dimensional emotion representation space (i.e., arousal and valence). The adopted methodology is a weighted ensemble of several models from both video and text modalities. For video-based recognition, two different types of visual cues (i.e., face and facial landmarks) were considered to feed a multi-input deep neural network. Regarding the text modality, a sequential model based on a simple recurrent architecture was implemented. In addition, we also introduce a model based on high-level features in order to embed domain knowledge in the learning process. Experimental results on the OMG-Emotion validation set demonstrate the effectiveness of the implemented ensemble model as it clearly outperforms the current baseline methods. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Modalities,Sensory cue,Arousal,Domain knowledge,Expression (mathematics),Ensemble forecasting,Computer science,Speech recognition,Artificial intelligence,Sequential model,Artificial neural network,Machine learning |
DocType | Volume | Citations |
Journal | abs/1805.01416 | 0 |
PageRank | References | Authors |
0.34 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro M. Ferreira | 1 | 49 | 10.14 |
| Diogo Pernes | 2 | 0 | 2.03 |
| Kelwin Fernandes | 3 | 36 | 7.71 |
| Ana Rebelo | 4 | 183 | 16.21 |
| Jaime S. Cardoso | 5 | 543 | 68.74 |