Name
Abstract | ||
|---|---|---|
This paper presents the evaluations and analyses of hand gesture recognition using convolution neural networks (CNNs). CNNs are expected to deal with various articulation and multi-view changes of hand gestures. We show how to employ depth-based hand data with convolution neural networks and how to obtain the successful training and testing results from it. The evaluations were performed on famous hand database. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/URAI.2016.7625742 | 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) |
Keywords | Field | DocType |
hand gesture,depth,CNN,classification | Thumb,Convolutional neural network,Convolution,Gesture,Computer science,Gesture recognition,Speech recognition,Artificial neural network | Conference |
ISSN | ISBN | Citations |
2325-033X | 978-1-5090-0822-3 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeongwon Pyo | 1 | 0 | 0.34 |
| Sanghoon Ji | 2 | 0 | 0.34 |
| Sujeong You | 3 | 0 | 1.01 |
| Taeyoung Kuc | 4 | 0 | 0.34 |