  In this work, we propose a novel Generative Adversarial Stacked Autoencoder
that learns to map facial expressions, with up to plus or minus 60 degrees, to
an illumination invariant facial representation of 0 degrees. We accomplish
this by using a novel convolutional layer that exploits both local and global
spatial information, and a convolutional layer with a reduced number of
parameters that exploits facial symmetry. Furthermore, we introduce a
generative adversarial gradual greedy layer-wise learning algorithm designed to
train Adversarial Autoencoders in an efficient and incremental manner. We
demonstrate the efficiency of our method and report state-of-the-art
performance on several facial emotion recognition corpora, including one
collected in the wild.
