Name
Abstract | ||
|---|---|---|
This paper addresses the problem of recognizing gestures which are captured using the Kinect sensor in a educational game devoted to the deaf community. Different strategies are evaluated to deal with the problem of having few samples for training. We have experimented a Leave One Out Training and Testing (LOOT) strategy and an HMM-based ensemble of classifiers. A dataset containing 181 videos of gestures related to nine signs commonly used in educational games is introduced, which is available for research purposes. The experimental results have shown that the proposed ensemble-based method is a promising strategy to deal with problems where few training samples are available. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICTAI.2014.101 | ICTAI |
Keywords | Field | DocType |
gesture recognition, hidden markov models, kinect sensor | Pattern recognition,Deaf community,Computer science,Gesture,Gesture recognition,Speech recognition,Educational game,Artificial intelligence,Hidden Markov model | Conference |
ISSN | Citations | PageRank |
1082-3409 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vinicius Godoy | 1 | 0 | 0.34 |
| Alceu S. Britto Jr. | 2 | 25 | 3.77 |
| Alessandro L. Koerich | 3 | 525 | 39.59 |
| Jacques Facon | 4 | 67 | 15.67 |
| Luiz S. Oliveira | 5 | 476 | 47.22 |
| Britto, A.S. | 6 | 0 | 0.34 |