Name
Abstract | ||
|---|---|---|
In this paper, we apply a heat-map approach for human face tracking. We utilize the heat-map extracted from the convolutional neural networks (CNN) for face / non-face classification problem. The CNN architecture we build is a shallow network to extract information that is meaningful in locating an object. In addition, we made many CNNs with changes in pool-size of the last layer to obtain a well-defined heat-map. Experiments in the Visual Tracking Object dataset show that the results of the method are very encouraging. This shows the effectiveness of our proposed method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3184066.3184081 | 2ND INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND SOFT COMPUTING (ICMLSC 2018) |
Keywords | Field | DocType |
Convolution neural network, heat-map, face tracking | Pattern recognition,Convolutional neural network,Computer science,Eye tracking,Artificial intelligence,Facial motion capture | Conference |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nhu-Tai Do | 1 | 1 | 1.72 |
| Soo-Hyung Kim | 2 | 191 | 49.03 |
| Hyungjeong Yang | 3 | 455 | 47.05 |
| Gueesang Lee | 4 | 208 | 52.71 |
| In Seop Na | 5 | 42 | 13.83 |