- Implementation of Deep Region and Multi-Label Learning for Facial Action Unit Detection.
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Do the Experiments on the Cohn-Kanade dataset. And I only use about 600 images (nearly 500 images for training, 100 images for testing, 12 AU, no alignment ).
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Compare with and without Region Layer. In the situation of without Region Layer, I use one convolution layer to replace it.
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Directly train without sample operation to deal with imbalance between positive and negative samples. So the dataset only contains label (1, -1)
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Calculate loss according to the formula in Paper which considers the label {-1, 0, 1}. So If you want to do the paper's experiments (positive and negative samples for each AU), you can rewrite the lib/data_loader.
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Only calculate the F1-score.
- python 3.6
- pytorch 0.3.0
You can see the results in log files
- logs/region_layer.log.
- logs/without_region_layer.log.
Visualization The result with region layer is worse than without region layer. I think it maybe have something to do with
- Small dataset (overfitting) which has only 600 images and no sample operation.
- Without alignment.
Compare to the results in paper
(Some AU is different from the AU in my experiment)
