Data Augmentation and Transfer Learning Approaches Applied to Facial
  Expressions Recognition
Enrico Randellini Leonardo Rigutini Claudio Sacca'
The face expression is the first thing we pay attention to when we want to
understand a person's state of mind. Thus, the ability to recognize facial
expressions in an automatic way is a very interesting research field. In this
paper, because the small size of available training datasets, we propose a
novel data augmentation technique that improves the performances in the
recognition task. We apply geometrical transformations and build from scratch
GAN models able to generate new synthetic images for each emotion type. Thus,
on the augmented datasets we fine tune pretrained convolutional neural networks
with different architectures. To measure the generalization ability of the
models, we apply extra-database protocol approach, namely we train models on
the augmented versions of training dataset and test them on two different
databases. The combination of these techniques allows to reach average accuracy
values of the order of 85\% for the InceptionResNetV2 model.