  Variational AutoEncoders (VAE) employ deep learning models to learn a
continuous latent z-space that is subjacent to a high-dimensional observed
dataset. With that, many tasks are made possible, including face reconstruction
and face synthesis. In this work, we investigated how face masks can help the
training of VAEs for face reconstruction, by restricting the learning to the
pixels selected by the face mask. An evaluation of the proposal using the
celebA dataset shows that the reconstructed images are enhanced with the face
masks, especially when SSIM loss is used either with l1 or l2 loss functions.
We noticed that the inclusion of a decoder for face mask prediction in the
architecture affected the performance for l1 or l2 loss functions, while this
was not the case for the SSIM loss. Besides, SSIM perceptual loss yielded the
crispest samples between all hypotheses tested, although it shifts the original
color of the image, making the usage of the l1 or l2 losses together with SSIM
helpful to solve this issue.
