• Implementation of Deep Region and Multi-Label Learning for Facial Action Unit Detection.

• 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 ).

• Compare with and without Region Layer. In the situation of without Region Layer, I use one convolution layer to replace it.

• Directly train without sample operation to deal with imbalance between positive and negative samples. So the dataset only contains label (1, -1)

• 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.

• 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)