Name
Abstract | ||
|---|---|---|
This paper describes an approach for the engagement prediction task, a sub-challenge of the 7th Emotion Recognition in the Wild Challenge (EmotiW 2019). Our method involves three fundamental steps: feature extraction, regression and model ensemble. In the first step, an input video is divided into multiple overlapped segments (instances) and the features extracted for each instance. The combinations of Long short-term memory (LSTM) and Fully connected layers deployed to capture the temporal information and regress the engagement intensity for the features in previous step. In the last step, we performed fusions to achieve better performance. Finally, our approach achieved a mean square error of 0.0597, which is 4.63% lower than the best results last year.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3340555.3355714 | ICMI |
Keywords | Field | DocType |
Engagement detection, E-learning Environment, Ensemble model, Facial behavior, Affective computing | Computer science,Human–computer interaction | Conference |
ISBN | Citations | PageRank |
978-1-4503-6860-5 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Van T. Huynh | 1 | 5 | 2.85 |
| Soo-Hyung Kim | 2 | 191 | 49.03 |
| Gueesang Lee | 3 | 208 | 52.71 |
| Hyungjeong Yang | 4 | 455 | 47.05 |