Facial Expression Recognition on FER Private 2013 (test)
73.475AccuracyOCFER-Net
Evaluation results
| Method | Links | |
|---|---|---|
| OCFER-NetBackbone=ResNet-182025.12 | 73.475 | |
| VGG-VD-16Pretraining=VGGFaces2016.10 | 72.89 | |
| HDC††architecture_type=ensemble2016.10 | 72.72 | |
| WuJie10102025.12 | 72.388 | |
| VGG-MPretraining=VGGFaces2016.10 | 72.08 | |
| AlexNetPretraining=VGGFaces2016.10 | 71.44 | |
| HDC*architecture_type=best single CNN2016.10 | 70.58 | |
| VGG-VD-16Pretraining=ImageNet2016.10 | 70.38 | |
| Resnet-50Pretraining=VGGFaces2016.10 | 70.33 | |
| Resnet-50Pretraining=ImageNet2016.10 | 69.02 | |
| VGG-MPretraining=ImageNet2016.10 | 67.57 | |
| AlexNetPretraining=ImageNet2016.10 | 63.28 |