Name
Abstract | ||
|---|---|---|
A continuous multimodal human affect recognition for both arousal and valence dimensions in a non-acted spontaneous scenario is investigated in this paper. Different regression models based on Random Forests and Echo State Networks are evaluated and compared in terms of robustness and accuracy. Moreover, an extension of Echo State Networks to a bi-directional model is introduced to improve the regression accuracy. A hybrid method using Random Forests, Echo State Networks and linear regression fusion is developed and applied on the test subset of the AVEC16 challenge. Finally, the label shift and prediction delay is discussed and an annotator specific regression model, as well as fusion architecture, is proposed for future work. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2988257.2988260 | AVEC@ACM Multimedia |
Keywords | Field | DocType |
Affect recognition, multi-modal fusion, Echo State Networks | Computer vision,Pattern recognition,Regression,Computer science,Regression analysis,Robustness (computer science),Speech recognition,Artificial intelligence,Random forest,Linear regression | Conference |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammadreza Amirian | 1 | 28 | 4.11 |
| Markus Kächele | 2 | 222 | 14.76 |
| Patrick Thiam | 3 | 58 | 9.29 |
| Viktor Kessler | 4 | 10 | 3.19 |
| Friedhelm Schwenker | 5 | 23 | 3.82 |