  The success of deep learning models depends on the size and quality of the
dataset to solve certain tasks. Here, we explore how far generated data can aid
real data in improving the performance of Neural Networks. In this work, we
consider facial expression recognition since it requires challenging local data
generation at the level of local regions such as mouth, eyebrows, etc, rather
than simple augmentation. Generative Adversarial Networks (GANs) provide an
alternative method for generating such local deformations but they need further
validation. To answer our question, we consider noncomplex Convolutional Neural
Networks (CNNs) based classifiers for recognizing Ekman emotions. For the data
generation process, we consider generating facial expressions (FEs) by relying
on two GANs. The first generates a random identity while the second imposes
facial deformations on top of it. We consider training the CNN classifier using
FEs from: real-faces, GANs-generated, and finally using a combination of real
and GAN-generated faces. We determine an upper bound regarding the data
generation quantity to be mixed with the real one which contributes the most to
enhancing FER accuracy. In our experiments, we find out that 5-times more
synthetic data to the real FEs dataset increases accuracy by 16%.

Computer Vision and Pattern Recognition