Improve accuracy for emotion recognition module about face_classification

[oarriaga]

Hello @swapgit I am happy to hear that you like the project. In order to improve the emotion classification module we could try several things:

• pre-train or train along another emotion dataset (I have tried to pre-train with the KDEF dataset but it didn't show any perceivable increase in accuracy).
• The labels are not uniformly distributed consequently we could try to re-train with the existent dataset using a weighted loss.

[csbotos]

Thank you! this repo is a miracle :)

[EDITED] The using the initial settings the preloaded weights would yield a lower performance, however it can be later trained to the reported 66%

How about using balanced training batches (so each batch will contain same amount of samples from each class)? I know it would represent a different class distribution, where the deviation of the small sets would be narrow - for me it worked better than class weights.

[oarriaga]

Hello @csbotos I am happy to hear you like the project :). Yes, we can also try to have balanced batches. The drop in accuracy could be related to not using the correct optimizer weights. I encountered an issue in keras in which the optimizer weights were not compatible between keras versions; therefore, I either deleted them entirely from the hdf5 files or I set the compile flag to False when loading the models.

[csbotos]

Yeah, I just discovered that the learning rate could be too raw for the pretrained network, now the algorithm topped again at 66%